essay on scientific progress

We Need a New Science of Progress

Humanity needs to get better at knowing how to get better.

In 1861, the American scientist and educator William Barton Rogers published a manifesto calling for a new kind of research institution. Recognizing the “daily increasing proofs of the happy influence of scientific culture on the industry and the civilization of the nations,” and the growing importance of what he called “Industrial Arts,” he proposed a new organization dedicated to practical knowledge. He named it the Massachusetts Institute of Technology.

Rogers was one of a number of late-19th-century reformers who saw that the United States’ ability to generate progress could be substantially improved. These reformers looked to the successes of the German university models overseas and realized that a combination of focused professorial research and teaching could be a powerful engine for advance in research. Over the course of several decades, the group—Rogers, Charles Eliot, Henry Tappan, George Hale, John D. Rockefeller, and others—founded and restructured many of what are now America’s best universities, including Harvard, MIT, Stanford, Caltech, Johns Hopkins, the University of Chicago, and more. By acting on their understanding, they engaged in a kind of conscious “progress engineering.”

Progress itself is understudied. By “progress,” we mean the combination of economic, technological, scientific, cultural, and organizational advancement that has transformed our lives and raised standards of living over the past couple of centuries. For a number of reasons, there is no broad-based intellectual movement focused on understanding the dynamics of progress, or targeting the deeper goal of speeding it up. We believe that it deserves a dedicated field of study. We suggest inaugurating the discipline of “Progress Studies.”

Before digging into what Progress Studies would entail, it’s worth noting that we still need a lot of progress. We haven’t yet cured all diseases; we don’t yet know how to solve climate change; we’re still a very long way from enabling most of the world’s population to live as comfortably as the wealthiest people do today; we don’t yet understand how best to predict or mitigate all kinds of natural disasters; we aren’t yet able to travel as cheaply and quickly as we’d like; we could be far better than we are at educating young people. The list of opportunities for improvement is still extremely long.

Read: The 50 greatest breakthroughs since the wheel

Those are major challenges. A lot of progress can also come from smaller advances: Thousands of lesser improvements that together build upon one another can together represent an enormous advance for society. For example, if our discoveries and inventions improve standards of living by 1 percent a year, children will by adulthood be 35 percent better off than their parents. If they improve livelihoods at 3 percent a year, those same children will grow up to be about 2.5 times better off.

Whether viewed in terms of large or small improvements, progress matters a lot.

Looking backwards, it’s striking how unevenly distributed progress has been in the past. In antiquity, the ancient Greeks were discoverers of everything from the arch bridge to the spherical earth. By 1100, the successful pursuit of new knowledge was probably most concentrated in parts of China and the Middle East. Along the cultural dimension, the artists of Renaissance Florence enriched the heritage of all humankind, and in the process created the masterworks that are still the lifeblood of the local economy. The late 18th and early 19th century saw a burst of progress in Northern England, with the beginning of the Industrial Revolution. In each case, the discoveries that came to elevate standards of living for everyone arose in comparatively tiny geographic pockets of innovative effort. Present-day instances include places like Silicon Valley in software and Switzerland’s Basel region in life sciences.

These kinds of examples show that there can be ecosystems that are better at generating progress than others, perhaps by orders of magnitude. But what do they have in common? Just how productive can a cultural ecosystem be? Why did Silicon Valley happen in California rather than Japan or Boston? Why was early-20th-century science in Germany and Central Europe so strong? Can we deliberately engineer the conditions most hospitable to this kind of advancement or effectively tweak the systems that surround us today?

This is exactly what Progress Studies would investigate. It would consider the problem as broadly as possible. It would study the successful people, organizations, institutions, policies, and cultures that have arisen to date, and it would attempt to concoct policies and prescriptions that would help improve our ability to generate useful progress in the future.

Read: Is ‘progress’ good for humanity?

Along these lines, the world would benefit from an organized effort to understand how we should identify and train brilliant young people, how the most effective small groups exchange and share ideas, which incentives should exist for all sorts of participants in innovative ecosystems (including scientists, entrepreneurs, managers, and engineers), how much different organizations differ in productivity (and the drivers of those differences), how scientists should be selected and funded, and many other related issues besides.

Plenty of existing scholarship touches on these topics, but it takes place in a highly fragmented fashion and fails to directly confront some of the most important practical questions.

Imagine you want to know how to most effectively select and train the most talented students. While this is an important challenge facing educators, policy makers, and philanthropists, knowledge about how best to do so is dispersed across a very long list of different fields. Psychometrics literature investigates which tests predict success. Sociologists consider how networks are used to find talent. Anthropologists investigate how talent depends on circumstances, and a historiometric literature studies clusters of artistic creativity. There’s a lively debate about when and whether “10,000 hours of practice” are required for truly excellent performance. The education literature studies talent-search programs such as the Center for Talented Youth. Personality psychologists investigate the extent to which openness or conscientiousness affect earnings. More recently, there’s work in sportometrics, looking at which numerical variables predict athletic success. In economics, Raj Chetty and his co-authors have examined the backgrounds and communities liable to best encourage innovators. Thinkers in these disciplines don’t necessarily attend the same conferences, publish in the same journals, or work together to solve shared problems.

When we consider other major determinants of progress, we see insufficient engagement with the central questions. For example, there’s a growing body of evidence suggesting that management practices determine a great deal of the difference in performance between organizations. One recent study found that a particular intervention—teaching better management practices to firms in Italy—improved productivity by 49 percent over 15 years when compared with peer firms that didn’t receive the training. How widely does this apply, and can it be repeated? Economists have been learning that firm productivity commonly varies within a given sector by a factor of two or three , which implies that a priority in management science and organizational psychology should be understanding the drivers of these differences. In a related vein, we’re coming to appreciate more and more that organizations with higher levels of trust can delegate authority more effectively, thereby boosting their responsiveness and ability to handle problems. Organizations as varied as Y Combinator, MIT’s Radiation Lab, and ARPA have astonishing track records in catalyzing progress far beyond their confines. While research exists on all of these fronts, we’re underinvesting considerably. These examples collectively indicate that one of our highest priorities should be figuring out interventions that increase the efficacy, productivity, and innovative capacity of human organizations.

Similarly, while science generates much of our prosperity, scientists and researchers themselves do not sufficiently obsess over how it should be organized. In a recent paper, Pierre Azoulay and co-authors concluded that Howard Hughes Medical Institute’s long-term grants to high-potential scientists made those scientists 96 percent more likely to produce breakthrough work. If this finding is borne out, it suggests that present funding mechanisms are likely to be far from optimal, in part because they do not focus enough on research autonomy and risk taking.

Read: Small teams of scientists have fresher ideas

More broadly, demographics and institutional momentum have caused enormous but invisible changes in the way we support science. For example, the National Institutes of Health (the largest science-funding body in the U.S.) reports that, in 1980, it gave 12 times more funding to early-career scientists (under 40) than it did to later-career scientists (over 50). Today, that has flipped: Five times more money now goes to scientists of age 50 or older. Is this skew toward funding older scientists an improvement? If not, how should science funding be allocated? We might also wonder: Do prizes matter? Or fellowships, or sabbaticals? Should other countries organize their scientific bodies along the lines of those in the U.S. or pursue deliberate variation? Despite the importance of the issues, critical evaluation of how science is practiced and funded is in short supply, for perhaps unsurprising reasons. Doing so would be an important part of Progress Studies.

Progress Studies has antecedents, both within fields and institutions. The economics of innovation is a critical topic and should assume a much larger place within economics. The Center for Science and the Imagination at Arizona State University seeks to encourage optimistic thinking about the future through fiction and narrative: It observes, almost certainly correctly, that imagination and ambition themselves play a large role. Graham Allison and Niall Ferguson have called for an “ applied history ” movement, to better draw lessons from history and apply them to real-world problems, including through the advising of political leaders. Ideas and institutions like these could be more effective if part of an explicit, broader movement.

In a world with Progress Studies, academic departments and degree programs would not necessarily have to be reorganized. That’s probably going to be costly and time-consuming. Instead, a new focus on progress would be more comparable to a school of thought that would prompt a decentralized shift in priorities among academics, philanthropists, and funding agencies. Over time, we’d like to see communities, journals, and conferences devoted to these questions.

Such shifts have occurred before. A lot of excellent climate-science research—in environmental science, physics, chemistry, oceanography, mathematics and modeling, computer science, biology, ecology, and other fields—was being pursued before we recognized “climate science” as a discipline unto itself. Similarly, the designation of “Keynesian economics” helped economists focus on fiscal policy as a tool for recession fighting, just as the name “monetarism” created a focal interest in questions surrounding the money supply.

An important distinction between our proposed Progress Studies and a lot of existing scholarship is that mere comprehension is not the goal. When anthropologists look at scientists, they’re trying to understand the species. But when viewed through the lens of Progress Studies, the implicit question is how scientists (or funders or evaluators of scientists) should be acting. The success of Progress Studies will come from its ability to identify effective progress-increasing interventions and the extent to which they are adopted by universities, funding agencies, philanthropists, entrepreneurs, policy makers, and other institutions. In that sense, Progress Studies is closer to medicine than biology: The goal is to treat , not merely to understand.

We know that, to some readers, the word progress may sound too normative. However, it is the explicit bedrock upon which Vannevar Bush made his case for postwar funding of science, a case that led to the establishment of the National Science Foundation. In an era where funding for good projects can be hard to come by, or is even endangered, we must affirmatively make the case for the study of how to improve human well-being. This possibility is a fundamental reason why the American public is interested in supporting the pursuit of knowledge, and rightly so.

If we look to history, the organization of intellectual fields, as generally recognized realms of effort and funding, has mattered a great deal. Areas of study have expanded greatly since the early European universities were formed to advance theological thinking. Organized study of philosophy and the natural sciences later spawned deeper examination of—to name a few—mathematics, physics, chemistry, biology, and economics. Each discipline, in turn with its subfields, has spawned many subsequent transformative discoveries. Our point, quite simply, is that this process has yet to reach a natural end, and that a more focused, explicit study of progress itself should be one of the next steps.

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26 Scientific Progress

Alexander Bird, Department of Philosophy, University of Bristol

• Published: 09 July 2015
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What constitutes scientific progress? This article considers and evaluates three competing answers to this question. These seek to understand scientific progress in terms of problem-solving, of truthlikeness/verisimilitude, and of knowledge, respectively. How does each fare, taking into consideration the fact that the history of science involves disruptive change, not merely the addition of new beliefs to old beliefs, and the fact that sometimes the history of such changes involves a sequence of theories, all of which are believed to be false, even by scientific realists? The three answers are also evaluated with regard to how they assess certain real and hypothetical scientific changes. Also considered are the three views of the goal of science implicit in the three answers. The view that the goal of science is knowledge and that progress is constituted by the accumulation of knowledge is argued to be preferable to its competitors.

1 Introduction

“ What Des-Cartes did was a good step. You have added much several ways, & especially in taking the colours of thin plates into philosophical consideration. If I have seen further it is by standing on the shoulders of Giants.” Newton’s famous lines come in a letter to Hooke concerning research into the nature of light, carried out by Newton, Hooke, and Descartes. Those words have come to represent the idea of scientific progress—the idea that science is a collective enterprise in which scientists add to the edifice upon which their colleagues and predecessors have been laboring. While the metaphor is an old one, it is only in the early modern period that thinkers came to view history and culture—science in particular—in terms of progress. Descartes (1637/1960 : 85) himself, in his Discourse on Method, having remarked on the state of knowledge in medicine—that almost nothing is known compared to what remains to be known—invites “men of good will and wisdom to try to go further by communicating to the public all they learn. Thus, with the last ones beginning where their predecessors had stopped, and a chain being formed of many individual lives and efforts, we should go forward all together much further than each one would be able to do by himself.” Descartes’s desire found its expression in the scientific societies of the time, such as the Académie des Sciences (1666) and the Royal Society (1660), and in learned journals, such as the Journal des Sçavans and the Philosophical Transactions of the Royal Society. Zilsel (1945) finds late renaissance artisans publishing knowledge of their crafts in the spirit of contributing incrementally to the public good and understanding. But it is in the work of Francis Bacon that progress as an ideal for science is first promoted as such. In The Advancement of Learning (1605) and The New Organon (1620) , Bacon lays down the growth of knowledge as a collective goal for scientists, knowledge that would lead to social improvement also. And in his New Atlantis (1627) Bacon articulates a vision of a society centered upon Salomon’s House, a state-supported college of scientists working on cooperative projects and the model for the learned academies founded in the seventeenth century.

With the notion of scientific progress in hand, several questions naturally arise: What is scientific progress? How does one promote scientific progress? How can we detect or measure scientific progress? Is there in fact progress in science? 1 This article concentrates principally on the first question, but the various questions are related. For example, Descartes and Bacon accepted that science is the sort of activity where progress can be made by collective activity, in particular by adding to the achievements of others. This contrasts with the idea of progress in the arts. Although controversial, a case can be made that progress in the arts is made when the horizons of an art form are expanded through the exploration of new expressive possibilities. If so, that progress is typically individual and not straightforwardly cumulative—new artworks do not pick up where their predecessors left off. Furthermore, it seems plausible that the very point of scientific activity is to make progress, whereas it is rather less plausible that the raison d’etre of the arts is related to progress of any kind. If this is correct, the scientific progress is the sort of thing that is best promoted collectively, can be added to incrementally, and is linked intimately to the aim of science.

2 Progress and the Aim of Science

How does a conception of progress relate to the goals of an activity? The simple view of progress here is that if an activity A aims at goal X, then A makes progress insofar as it gets closer to achieving X or does more of X or does X better (depending on what X is and how it is specified). However, it is worth noting that an activity can be said to show progress along dimension D, even if that activity does not have any goal, let alone a consciously entertained or publicly agreed one, to which D is related. Progress in the arts, as mentioned, is like this. We might judge that political institutions have made progress if they are more responsive to the desires and needs of citizens, even if we do not suppose that this is the aim of those institutions. In such cases we judge progress by some appropriate standard, typically concerning a good that relates to the nature of the activity in question. For example, an artist might aim at producing a certain kind of aesthetic experience in an audience. An appropriate standard for judging progress in the arts is therefore the expansion of the possibilities for producing aesthetic experiences. Note also that a collective enterprise may make progress and may have a goal even if it is not the intention of any individual to contribute to that progress or to promote that goal. The artist may be focused solely on the experience of his or her audience and have no specific intention of widening the expressive possibilities of the art form. Nonetheless, if the work in fact does broaden the range of expressive possibilities, then he or she may thereby be contributing to artistic progress. Likewise, a business may have the aim of making a profit, even though most of its employees (perhaps even all of them) do not have this as an individual goal.

Thus we can talk of progress in science even if we do not attribute an aim to science and do not attribute the same aim to scientists. That said, most commentators do attribute a goal to science and tend to assume that scientists have that goal also. So the three principal approaches to scientific progress relate to three views of the aim of science, in accordance with the simple view of progress, as follows:

Science aims at solving scientific problems. Science makes progress when it solves such problems.

Science aims at truth. Science makes progress when it gets closer to the truth.

Science aims at knowledge. Science makes progress when it adds to the stock of knowledge.

Some well-known views of the aim of science might appear not to fit any of these. Bas van Fraassen (1980 : 12) holds empirical adequacy to be the aim of science. But this can be accommodated within (b) insofar as the aim of empirical adequacy is the aim of achieving a certain kind of truth, the truth of a theory’s observational consequences.

All three approaches accept that the goals of science can be achieved collectively and in particular in an incremental and (normally) cumulative way, just as described by Descartes. Problem-solving takes place within a tradition; problems solved by a scientist will add to the problems solved by the tradition. A theory developed by one scientist may be improved by a successor, thus bringing it closer to the truth. Scientists add to the stock of knowledge generated by predecessors (including by improving their theories).

3 Progress as Solving Scientific Problems

Scientists engage with scientific problems. Such problems arise from a tradition of solving problems. Consider this example: Given that we can account for the motions of the known planets within the framework of Newtonian mechanics and theory of gravitation, how should we account for the motion of the moon? This problem troubled astronomers and mathematicians for several decades in the eighteenth century and spurred research. Alexis Clairaut eventually showed that the difficulty in reconciling observations with Newtonian theory was due to the use of unsuitable approximations. In solving this problem, Clairaut thereby made progress in astronomy.

The principal proponents of the problem-solving approach to progress are Thomas Kuhn (1970) and Larry Laudan (1977) . Kuhn uses the terminology of “puzzle-solving,” but this has the same intent as Laudan’s “problem-solving.” For both, scientific activity takes place within a tradition of research. Research traditions are characterized by shared commitments that form a background that gives significance to research problems and guides the testing and evaluation of theories (i.e., proposed solutions to problems). For example, those in a common research tradition will share basic ontological commitments (beliefs about what sorts of entities exist—e.g., in medicine whether there are humors or germs), background theories (e.g., Newtonian mechanics), mathematical techniques (e.g., the calculus), and methods of theory assessment (e.g., significance testing in classical statistics). Kuhn used the terms “paradigm” and “disciplinary matrix” to refer to research traditions just described.

Kuhn emphasized a particularly important component of a research tradition: shared exemplars of scientific problems and their solutions that act as a model for future problem-solving within the tradition. (Kuhn also used the term “paradigm” to refer to such exemplars.) These paradigms-as-exemplars are Kuhn’s answer to the question about how science is able to make progress. For individuals, they explain the cognitive psychology of problem-solving. Training with exemplars allows scientists to see new problems as similar to old ones and as such requiring a similar approach to solving them. For research communities, shared exemplars and disciplinary matrices permit the individuals and groups within the community to agree on fundamentals and thus agree on the problems that need to be solved, the means of solving them, and, for the most part, whether a proposed solution is satisfactory. Research carried out in this way, governed by a shared disciplinary matrix, Kuhn calls “normal science.” Kuhn contrasts normal science with “pre-paradigm” (or “pre-normal”) science, a period of science characterized by a multiplicity of schools that differ over fundamentals. Pre-paradigm science fails to make progress because intellectual energy is put into arguing over those fundamental disagreements rather than into solving agreed puzzles according to agreed standards. For mature sciences, a more important contrast is “extraordinary” (or “revolutionary”) science, which occurs when the research tradition finds itself unable to solve particularly significant problems. Under such circumstances a change of paradigm is needed. New kinds of problem-solution are required, ones that differ in significant ways from the exemplars that had previously dominated the field.

According to Laudan (1981 : 145), “science progresses just in case successive theories solve more problems than their predecessors.” Kuhn and Laudan regard progress during normal science as unproblematic—during normal science solutions to problems are generally accepted and add to the sum of problems solved. Extraordinary science is less straightforward, because the rejection of a paradigm (i.e., a rupture within the research tradition) will often mean that some of the problems previously regarded as solved (by that paradigm, within that tradition) are no longer regarded as such. For example, Descartes’s vortex theory of planetary motion explained why the planets moved in the same plane and in the same sense (rotational direction). The successor theory, Newton’s inverse square law of gravitational force, solved various problems (e.g., why the planets obeyed Kepler’s laws) but lacked an explanation for the aforementioned problems to which Descartes’s theory offered a solution. Hence the adoption of Newton’s theory required relinquishing certain apparent problem-solutions. Such reductions in problem-solving ability are known as “Kuhn-losses.” The existence of Kuhn-losses makes assessment of progress more complicated, because, according to Laudan’s account, the change in question will be progressive only if the Kuhn-losses are compensated for by a greater number of successful problem-solutions provided by the new paradigm. That requires being able to individuate and count problems. Furthermore, presumably some problems are more significant than others, and we would therefore want to weight their solutions accordingly.

Laudan (1981 : 149) admits that the best way of carrying out this individuation of problems is not entirely clear, but he argues that all theories of progress will come up against that or an analogous difficulty—individuating confirming and disconfirming instances. We shall see that this is a rather less pressing matter for the other views of progress. Kuhn, on the other hand, accepts that there is no definitive way of making such assessments. The significance of a problem is determined by the tradition or paradigm that gives rise to it. There is no uncontentious way of making such assessments across paradigms: this is the problem of incommensurability. Thus there can be rational disagreement between adherents of two different paradigms. But that does not mean that Kuhn denies that there is progress through revolutions. On the contrary, Kuhn (1970 : 160–173) is clear that there is progress. Scientists operating within the new paradigm must be able to say, from their perspective at least, that the new paradigm retains much of the problem-solving power of its predecessor and that the reduction in the number of problems solved (the Kuhn-losses) are outweighed by the ability of the new paradigm to solve the most pressing anomalies of the predecessor while offering the promise of new problems and solutions to come. Incommensurability means that during a revolution this assessment—the assessment that moving to the new paradigm is progressive relative to retaining the old paradigm—is not rationally mandated and can be rationally disputed.

What counts a solving a problem? For that matter, what counts as a problem or a puzzle in the first place? A crucial feature of the problem-solving approach is that the scientific tradition or paradigm determines what a problem is and what counts as a solution. For Kuhn puzzles may be generated by a paradigm in a number of ways. For example, if a theory at the heart of a paradigm involves an equation with an unknown constant, then one puzzle will be to determine the value of that constant; subsequently, determining its value with greater precision or by a different means will also be puzzles. Most puzzles acquire their status by similarity to exemplar puzzles, and solutions are accepted as such on the basis of similarity to exemplary solutions. For Laudan also, problems are determined by the tradition and its current leading theories—for example a (conceptual) problem exists if the theory makes assumptions about the world that run counter to prevailing metaphysical assumptions. In the simplest cases, an empirical problem is solved by a theory if the theory entails (along with boundary conditions) a statement of the problem—there is no requirement that the theory should be true. I have called this feature of the problem-solving approach “internalist” in the epistemological sense ( Bird 2007 : 69). Internalist epistemologies are those that maintain that the epistemological status (e.g., as justified) of a belief should be accessible to the subject. The problem-solving approach is internalist in that it provides, as Laudan (1981 : 145) says, an account of the goal of science (and so of progress) such that scientists can determine whether that goal is being achieved or approached—the community is always in a position to tell whether a scientific development has the status of being progressive.

A consequence of this way of thinking about problems and solutions is that entirely false theories may generate problems and solutions to those problems. It implies that a field might be making progress (not merely appearing to make progress) by the accumulation of falsehoods, so long as those falsehoods are deducible from the principal theories in the field. For example, according to the dominant theory among alchemists, all matter is made up of some combination of earth, air, fire, and water; therefore it should be possible to transmute one substance into another by appropriate changes to the proportions of these elements. Consequently, a leading problem for alchemists was to discover a mechanism by which one metal could be transmuted into another. Alchemists provided solutions in terms of the dominant theory, i.e., solutions referred to the four elements and the four basic qualities (moist, cold, dry, and warm). Neither the problem nor its solutions are genuine, but that does count against progress being made in alchemy according to the problem-solving approach. Early writers in the Hippocratic tradition disagreed about the correct humors. One—the author of On Diseases, IV— proposes blood, phlegm, bile, and water. This sets up an asymmetrical relationship with the elements—the humors and elements overlap in one (water) but not the others. By replacing water with black bile, a symmetrical relationship between the humors, elements, and qualities could be restored. Furthermore, according to the author of On the Nature of Man, a relationship between the humors and the seasons could be established, explaining the prevalence of phlegm in winter for example ( Nutton 1993 : 285). The problem-solving approach takes such developments as progressive, despite their being devoid of any truth.

Another implication of the problem-solving approach is that the rejection of any theory without a replacement counts as regressive, so long as the theory did raise and solve some problems. Laudan, Kuhn, and others point out that because theories are not rejected outright with nothing to replace them, this scenario does not normally arise. However, even if rare, it does occur. In 1903 René Blondlot believed that he had discovered a new form of radiation, which he named “N-rays” (“N” for Nancy, where he was born and worked). N-rays attracted much interest until the American physicist R. W. Wood visited Blondlot’s laboratory and surreptitiously removed a crucial prism from Blondlot’s experiment. Blondlot nonetheless continued to see the rays. After Wood’s intervention it became accepted that N-rays simply did not exist; scientists gave up research in N-rays and the community repudiated the earlier “discoveries”. During the intervening period, 1903–1905, more than 300 articles on the topic were published ( Lagemann 1977 ), discussing the nature of the rays and their properties (e.g., which materials could generate or transmit the rays). N-ray research was carried out as normal science—research problems were posed and solved. So, according to the problem-solving approach, N-ray research was progressive until 1905, and, because its repudiation after Wood involved a loss of problems and solutions without any compensating gain, that repudiation must count as regressive. This conclusion will strike many as just wrong—N-ray research did not add anything to progress, but its rejection did perhaps add something.

Laudan regards the disconnection between progress and truth as an advantage of his account. Truth, says Laudan (1981 : 145), is transcendent and “closed to epistemic access.” Kuhn (1970 : 206) also argues that truth is transcendent. Note that to assert this is to assume antirealism. Scientists gather evidence and provide arguments as to why that evidence gives us reason to believe, to some degree or other, the relevant hypotheses. Scientific realists claim that, in many mature sciences at least, this process does in fact lead to beliefs that are true or close to the truth. Antirealists deny this. Laudan’s claim that truth is transcendent, if correct, implies that the scientists are on a hiding to nothing. Their evidence and arguments cannot be leading us toward the truth, for if they did, then truth would not be transcendent—it would be epistemically accessible through the process of scientific research.

So scientific realists have two reasons to reject the problem-solving approach. First, the motivation (“truth is transcendent”) that Laudan gives for the internalism of that approach assumes antirealism. Second, the approach gives perverse verdicts on particular scientific episodes: if scientists in the grip of a bad theory add further falsehoods (false problem-solutions), that counts as progressive; if, on the other hand, they realize their error and repudiate those falsehoods, that counts as regressive.

4 Progress as Increasing Nearness to the Truth

A natural response to the realist’s objections to the problem-solving approach is to seek an account of progress in terms of truth—what I have called the “semantic” approach to progress ( Bird 2007 : 72). If the problem-solving approach takes progress to consist of later theories solving more problems than their predecessors, then a truth approach might take progress to consist in later theories containing more truth than their predecessors. This would occur, for example, when later theories add content to earlier theories and that new content is true.

However, the view of progress as accumulating true content has not found favor even among realists. The principal reason is the recognition that theories are often not in fact true. A good theory may in fact be only a good approximation to the truth, and a better successor theory may cover the same subject matter but be a better approximation to the truth without in fact being strictly true itself. So realists have tended to favor accounts of progress in terms of increasing truthlikeness (nearness to the truth, verisimilitude), not (directly) in terms of truth itself. Indeed, what David Miller (1974 : 166) regards as the “problem of verisimilitude” is the problem of saying how it is that there is progress, for example in the sequence of theories of motion from Aristotle to Oresme to Galileo to Newton, even though all the theories are false. It would seem that we do have an intuitive grasp of a nearness to the truth relation. In the following cases, T 2 is intuitively nearer to the truth than T 1 and T 4 is nearer to the truth than T 3 : Although it is not clear whether T 4 is nearer to the truth than T 2 , there is no reason why we should expect any verdict on such a question; the predicate “nearer to the truth than” delivers only a partial order.

It may seem obvious that someone who prefers the increasing nearness to the truth approach should provide an account of what nearness to the truth is and when one theory is nearer to the truth than another. However, it is unclear to what extent this is an essential task. If the various available accounts of truthlikeness are all unsatisfactory, the truthlikeness account of progress is severely undermined. On the other hand, consider the simple accumulation of truth view with which I started this section. Would it be a fair criticism of that view that philosophers do not have an agreed, unproblematic account of what truth is? Is the latter a problem for scientific realism, insofar as the latter also appeals to a notion of truth? Presumably not—we can and do use the concept of truth unproblematically. So the issue may come down to this: Is the concept of truthlikeness a theoretical or technical one introduced for the philosophical purposes discussed earlier? Or is it a concept with a pre-philosophical use, one that has clear application in a sufficient range of important cases? If the former, then the lack of a clear philosophical account of truthlikeness does cast doubt on the coherence of the concept and its use to account for progress. If the latter, then (like the concepts of truth, cause, knowledge, etc.) the truthlikeness concept has a legitimate philosophical application even in the absence of a satisfactory analysis. 3

Either way, it is instructive to look at one of the key debates in the early development of theories of truthlikeness. The basic idea behind Popper’s (1972 : 52) account of verisimilitude is that theory A is closer to the truth than B when A gets everything right that B gets right and some other things right besides, without getting anything wrong that B does not also get wrong. 4 Now this turns out to fail even in standard cases where A is false but is clearly closer to the truth than B (e.g., provides a closer numerical approximation of some constant; Tichý 1974 ). It can be shown that A will have some false consequences that are not consequences of B. So one might be tempted to drop the second condition: so long as the truths are accumulating, we can ignore the fact that there might be some accumulation of false consequences also. But a (prima facie) problem with ignoring the falsehoods is that a change to a theory might add to the true consequences in a small way but add to the false consequences in a major way. One might believe that such a change should be regarded as regressive. Focusing just on true consequences would also imply that a maximally progressive strategy would be to adopt an inconsistent theory, because it has every truth among its consequences.

A different approach initiated by Tichý and others ( Hilpinen 1976 ; Niiniluoto 1977 ) considers how much the possible worlds described by theories are like the actual world. Tichý imagines a language with three atomic propositions to describe the weather: “it is hot,” “it is rainy,” and “it is windy”. Let us assume that all three propositions are true. Say Smith believes “it is not hot and it is rainy and it is windy” whereas Jones believes “it is not hot and it is not rainy and it is not windy”. We can say that Jones is further from the truth than Smith since there are three respects in which a world may be like or unlike the actual world, and the possible world described by Jones’s theory differs from the actual world in all three respects (so the distance from the actual world is 3), while the possible world described by Smith’s theory differs from the actual world in one respect (so distance = 1). The world of Jones’s theory is thus more distant from the actual world than the world of Smith’s theory, and so the latter theory is closer to the truth than the former. Miller (1974) , however, considers an alternative language with the atomic propositions “it is hot”, “it is Minnesotan”, and “it is Arizonan”. “It is Minnesotan” is true precisely when “it is hot ↔ it is rainy” is true, and “it is Arizonan” is true precisely when “it is hot ↔ it is windy” is true. In this language the truth is expressed by “it is hot and it is Minnesotan and it is Arizonan,” while Smith’s beliefs are expressed by “it is not hot and it is not Minnesotan and it is not Arizonan” and Jones’s beliefs are expressed by “it is not hot and it is Minnesotan and it is Arizonan”. Given this language for the description of possible worlds, the world described by Smith’s theory differs from the actual world in three respects (distance = 3), and the world of Jones’s theory differs in just one respect (distance = 1). So Jones’s theory is closer to the truth than Smith’s. It would thus appear that this approach makes verisimilitude relative to a language. That is a problematic conclusion for the scientific realist, who wants progress and so (on this approach) verisimilitude to be objective. One might tackle this problem in various ways. For example, one might say that some languages are objectively better at capturing the natural structure of the world. (While I think that is correct, it seems insufficient to solve the problem entirely.) Or one might try quite different approaches to truthlikeness. (See Oddie [2014] for a clear and detailed survey of the options.) Eric Barnes (1991) proposes a more radical response that notes an epistemic asymmetry between the beliefs expressed in the two sets of vocabularies; this, I propose in the next section, is instructive for the purposes of understanding progress.

What is more obviously problematic for the truthlikeness approach are cases in which our judgments of progress and of truthlikeness are clear but divergent. I have suggested that we can imagine an example of this by considering again the case of Blondlot and N-rays ( Bird 2007 , 2008 ). 5 Observation of new effects, with new apparatus, particularly at the limits of perceptual capacities, is often difficult, so it is not entirely surprising that scientists could honestly think they were observing something when they were not. Almost all the work on the subject was carried out by French scientists—although some British scientists claimed to detect N-rays, most scientists outside France (and quite a few in France) could not detect them. It is plausible that the French N-ray scientists were motivated by national sentiment ( Lagemann 1977 ), consciously or unconsciously proud that that there were now French rays to stand alongside the X-rays (German), cathode rays (British), and canal rays (German again) discovered and investigated in the preceding decades. 6 Blondlot had an excellent reputation—he was a member of the Académie des Sciences; others who worked on N-rays were well respected, including Augustin Charpentier and Jean Becquerel (son of Henri Becquerel) ( Klotz 1980 ). No doubt these reputations also had a persuasive role—in that context the observation or otherwise of N-rays became a test of a scientist’s skills, not of the theory. Whatever the explanation, it is clear that the scientific justification for belief in N-rays was at best limited. Now let us imagine, counter-to-fact, that at some later date it is discovered that there really are rays answering to many of Blondlot’s core beliefs about N-rays; many of Blondlot’s beliefs (and those of the scientific community in France) turn out to be correct or highly truthlike, although those scientists held those beliefs for entirely the wrong reasons (their techniques could not in fact detect the genuine rays). In this case, according to the truthlikeness view of progress, Blondlot’s work contributed to progress (by adding true and highly truthlike propositions to the community’s beliefs) whereas Wood’s revelation was regressive (because it removed those beliefs). That seems entirely wrong—even if Blondlot got things right (by accident), the episode is clearly one of pathological science, not of progressive science—excepting Wood’s intervention, which got things back on track.

The preceding example shows that the truthlikeness account of progress conflicts with our judgments concerning particular instances. The case is partly hypothetical—in science it is difficult to get things right (factually correct or highly truthlike) for the wrong reasons. There are nonetheless some real cases. For example, it was believed in the Renaissance period that infants and young children had more body water as a proportion of total body weight than older children and adults. The source of this belief was a physiological theory based on the doctrine of the humors. As mentioned previously, the body was supposed to be governed by four humors (bile, black bile, blood, and phlegm); ill health was held to be a matter of an imbalance or corruption of the humors. The natural balance was supposed to change with time, and since the humors were characterized by their proportions of the four basic qualities (moist, dry, warm, and cold), the changing balance of the humors implied change in the natural proportion of these qualities with age. The prevailing view was that the predominant humor in childhood is blood, which is moist and warm ( Newton 2012 ). Children are moist because they are formed from the mother’s blood and the father’s seed, both of which are moist and warm. Childhood is also influenced by the moon, which was held to be warm and moist. In youth, however, the dominant humor is choler, which is warm and dry. And so in growing to maturity a child loses moisture, and indeed this drying continues with age, explaining the wrinkled skin of the elderly in contrast to the soft skin of babies and infants. When humoral theory was dismissed in the late eighteenth and nineteenth centuries, these opinions fell into abeyance. It turns out that modern physiology has shown that body water is proportionally highest in neonates and then declines ( Friis-Hansen et al. 1951 ; Schoeller 1989 ). So the Renaissance doctors, basing their opinion in the false humoral theory, fortuitously had correct opinions about the variation of moisture in the body with age. According to the “increasing truthlikeness” view, when true implications of humoral theory were drawn, science thereby made progress, but it regressed when these beliefs were dropped along with the humoral theory. On the contrary, even the fortuitously true consequences of humoral theory were no contribution to scientific progress insofar as they were based principally on that theory and not on appropriate evidence.

5 Progress as the Accumulation of Knowledge

The third approach to scientific progress is an epistemic one—scientific progress is the accumulation of scientific knowledge; as Sir William Bragg put it: “If we give to the term Progress in Science the meaning which is most simple and direct, we shall suppose it to refer to the growth of our knowledge of the world in which we live” (1936: 41). As Mizrahi (2013) shows, Bragg was far from alone in his opinion—scientists in general take knowledge to be at the core of scientific progress. It is also my own preferred view.

Let us return to Miller’s example of Smith and Jones who are making assertions about the weather. In Miller’s version of the story, they inhabit a windowless, air-conditioned room, thus they are making guesses. Jones guesses that it is not hot and it is not rainy and it is not windy, while Smith guesses that it is not hot and it is rainy and it is windy. The truth is that it is hot and it is rainy and it is windy, so none of Jones’s guesses are correct whereas two of Smith’s are correct. If we further assume that Smith’s guesses are made after Jones’s, then that would look like progress according to the truthlikeness view. But, as argued in the preceding section, lucky guesses are no contribution to progress. Now let us imagine, as does Barnes (1991 : 315), a variation whereby Smith and Jones form their beliefs not by guessing but by investigating indicators of temperature, precipitation, and airspeed in their vicinity, using methods that are usually very reliable by normal scientific standards. However, things go awry in the case of all Jones’s investigations, leading to his false beliefs. They go awry for Smith also in the case of temperature, but his methods for measuring precipitation and airspeed are reliable and working fine. Accordingly, Smith knows that it is rainy and that it is windy. Now consider the beliefs of Smith and Jones when expressed using the language of “hot,” “Minnesotan,” and “Arizonan.” The oddity is that now Jones has two correct beliefs: that the weather is Minnesotan and that it is Arizonan. Let us have a closer look at how it is that Jones has these two true beliefs. The belief that it is Minnesotan is logically equivalent to the belief that it is hot ↔ it is raining, which in turn is equivalent to the belief DIS that either it is both hot and rainy or it is neither hot nor rainy. Jones believes this proposition because he believes that it is not hot and not rainy. So he believes the second disjunct of DIS . Note that this disjunct of DIS is false. Since DIS is a simple consequence of that disjunct, Jones believes the whole of DIS . However, the whole of DIS is true, since the first disjunct is true—a disjunct that Jones believes to be false. So the position is that Jones believes a true proposition, DIS , because it is a disjunction and he believes one of the disjuncts—the one that happens to be false. As Barnes (1991 : 317) points out, Jones’s belief has the structure of a standard Gettier case: a case of a true and justified belief but where the truth and justification come apart. The justification (in this case Jones’s normally reliable investigation that has gone awry) is attached to the false disjunct of DIS whereas the truth comes from the other disjunct of DIS for which Jones has no justification (in fact he thinks it is false). As a standard Gettier case, we must deny that Jones has knowledge in this case, although he has fortuitously obtained a true belief.

The problem we faced was that it looked as if truthlikeness depends on choice of language. Combined with the “progress is increasing truthlikeness” view, that implies that Smith makes progress relative to Jones when we express their beliefs using normal language and that Jones makes more progress when their beliefs are expressed using the terms “Minnesotan” and “Arizonan”. Barnes’s important observation shows that the change of language makes no corresponding difference regarding knowledge: a shift of language reveals that Jones has some true beliefs, but it does not render those beliefs epistemically successful—they are not knowledge. So that in turn means that while the truthlikeness view faces problems, the epistemic view can simply bypass them. 7 Fortuitously true beliefs notwithstanding, Jones knows nothing substantive about the weather whereas Smith does, and therefore Smith contributes to progress and Jones does not.

A suitable response therefore proposes that progress should be seen in terms of increasing knowledge, not nearness to the truth. This, the epistemic view, gives the right results in the other cases considered also. If by fluke Blondlot had some true beliefs about N-rays, they would not have been knowledge and so would have made no contribution to progress. Renaissance doctors did not know that infants have high body water, although that is true, because they had no way of determining that—their beliefs were based on the radically false humoral theory. So there was no progress when they came to have those beliefs either. Likewise, normal science based on a radically false theory, such as the theory of four elements and four basic qualities, is not progressing, even if it appears, from the practitioners’ point of view, to be providing successful solutions to their problems. That is because in such cases there is no addition of knowledge.

The view that progress consists in the accumulation of knowledge faces an objection relating to that which stimulated the truthlikeness approach. A straightforward accumulation of truth view suffered from the fact that there could be progress in a sequence of theories, all of which are false. The idea of truthlikeness, which could increase through that sequence, appeared to solve the problem. If we now switch to the epistemic approach we face a difficulty that does not appear to have such a straightforward solution. Knowledge entails truth, so the community cannot know any proposition in a series of false propositions: however close to the truth p maybe, if p is strictly false, then it cannot be known that p . Nor, however, is there an obvious analogue to truthlikeness for knowledge: a state that is much like knowledge except that the content of that state is false. It might be that we could attempt a formal definition of such a state—call it “approximate knowing”. Approximate knowing would be a certain kind of belief. For example, it might be proposed that the state of approximate knowing that p is a belief that p that is justified, where the justification is appropriately linked to the fact that p has high truthlikeness. Note that if an approach of this sort says anything detailed about what approximate knowing is (such as in this proposal), then it is likely to draw on a particular definition of (non-approximate) knowledge, substituting truthlikeness for truth. Yet accounts and definitions of knowledge are notoriously subject to counterexample. So it may be that the most we can confidently say about this state is that it is like knowing except for a false content. Those, like Williamson (1995) , who take knowledge to be a factive mental state, different in kind from belief, will note that approximate knowing is not a state of the same kind as knowing.

While I do not rule out the idea of approximate knowing, a better approach starts by noting that when a theory T is accepted, what scientists believe is true is not limited to a precise statement of T—indeed they might not believe that precise statement. Scientists will believe many other propositions that are related to T, most importantly its most obvious and salient logical consequences. For example, Dalton not only believed (a) that water is HO; he also believed (b) that water is a compound of hydrogen and oxygen and (c) that water is made up of molecules, each containing a small number of atoms of hydrogen and oxygen, and so forth. Rutherford almost certainly did not believe (d) that Avogadro’s constant has the precise value of 6.16 × 10 23 mol –1 , but he did likely believe (e) that the value of Avogadro’s constant is approximately 6.16 × 10 23 mol –1 , or (f) that the value of Avogadro’s constant lies between 6.0 × 10 23 mol –1 and 6.3 × 10 23 mol –1 . While (a) and (d) are false propositions, (b), (c), (e), and (f) are true propositions. Given the evidence and methods that Dalton and Rutherford employed, it is plausible that Dalton knew (b) and (c) and Rutherford knew (e) and (f). Thus insofar as their predecessors did not have this knowledge, then Dalton and Rutherford contributed to progress, according to the epistemic approach. The relevant consequences are logically weaker and so less informative that the propositions (a) and (c) from which they are derived: in the case of (e) and (f), the consequences are inexact propositions, while in the case of (b) and (c), the consequences are exact propositions but omit details contained in the original proposition (a).

This approach to defending the epistemic view of progress, in the case of false but progressive theories, requires that we are able to find (true) propositions that are consequences of such theories and that are now known by the community but that were not known previously. It is highly plausible that this condition can be met. 8 As the previous examples suggest, if a proposition is strictly false but highly truthlike, then there will exist related nontrivial true propositions. At the very least “T is highly truthlike” will be such a proposition (for false but highly truthlike theory T). But in many cases we will be able to find propositions more precise than this. 9

The next question concerns belief—do the scientists in question believe the true proposition in question? In some cases this may be clear enough—Dalton clearly believed (a) and also (b) and (c). But in other cases it might be less clear, often because the scientist does not believe the principal truthlike but false proposition. The most difficult cases are those in which the theory in question is a model that scientists know from the outset to be strictly false. Nonetheless, in such a case, if the model does contribute to progress, then it will have some significant element of truth or truthlikeness (at least relative to its predecessors), and that will have been achieved by suitable gathering of evidence and reasoning. If so, the scientists promoting the model should be able to say something about the respects in which the model matches reality, even if, at a minimum, that is just to say that the model delivers approximately accurate predictions within certain ranges of the relevant parameters. They will typically have some idea regarding some of the implications of theory that these are supported by the evidence and reasoning whereas others are not. For example, the simple kinetic theory of gases is clearly false, since it assumes that gas molecules have no volume. So no-one ever believed the theory in toto, but scientists do believe nontrivial implications of the theory: that gases are constituted by particles; that the temperature of a gas is in large part a function of the kinetic energy of the particles; and that the ideal gas equation holds with a high degree of approximation for gases at moderate pressure and temperature. 10

So false theories, even those known to be false, can contribute to progress on the epistemic view because they often have significant true content or true implications that are believed by scientists on the basis of good evidence and reasoning (i.e., these implications are known propositions). Thus the epistemic approach is able to accommodate progress made through false theories. At the same time, it delivers more accurate verdicts regarding cases of accidentally true unjustified (or partially) theories, which gives it a distinct advantage, in my opinion. On the other hand, it does need to confront the widely held view that truth is the aim of inquiry and belief and that the justification element of knowledge is not constitutive of the aim of science (nor of scientific progress) but is rather merely instrumental in achieving that aim ( Rowbottom 2010 ; Niiniluoto 2011 ).

6 Progress and the Aim of Science

As Niiniluoto (2011) puts it, “Debates on the normative concept of progress are at the same time concerned with axiological questions about the aims and goals of science.” At the outset I linked approaches to progress with views regarding the aim of science. Laudan (1977 , 1981 ) and Kuhn (1970) think that science aims at solving problems, and so success in so doing is their standard of progress. Oddie (2014) accepts that truth is the aim of inquiry, motivating accounts of truthlikeness. 11   Barnes (1991) and I take knowledge to be the goal of science and accordingly hold scientific progress to be the addition of scientific knowledge. 12 Note that because a goal can have subsidiary goals as means, those who hold that science aims at truth or truthlikeness can agree that science aims at problem-solving, since solving a problem will be a route (albeit a fallible one) to the truth about the matter in question. Likewise the view that knowledge is the goal of science will imply that problem-solving and truth are legitimate goals for science, since pursuing these will further the pursuit of knowledge. So the difference between the views must turn on differences between these goals as ultimate or constitutive goals.

Although the problem-solving view of the constitutive aim of science is consistent with scientific realism, it is not a view that will appeal to the realist. If that view were correct, then a piece of science would have achieved its goal if it solves some problem in the (internalist) sense of Kuhn and Laudan but is nonetheless false. The only way of making that view acceptable is to reject the notion of truth (or truthlikeness) or at least adopt a strong kind of skepticism that would make the goal of truth utopian. If one is a realist and thinks that truth is achievable, then one will be inclined to reject the idea that a problem-solution that appears to solve a problem but is false does satisfy the aim of science.

It might appear difficult to draw a distinction between the goals of truth and those of knowledge. After all, if one uses rational means to pursue the first goal and one does achieve it, then one will typically have achieved the second also. There are differences, however. If one could believe a contradiction and all of its consequences, then one would thereby believe all truths (and all falsehoods). That would achieve the aim of science to a maximal degree, if that aim is truth, whereas it would not achieve the aim of science, if that aim is knowledge. If to avoid this it is added that one’s beliefs must be logically consistent, one could adopt a policy of believing propositions at random, so long as they are consistent with one’s existing beliefs. This would lead to a large quantity of true belief but to no knowledge. Neither of these policies, although productive of truth if successfully adopted, would count as promoting the goal of science. If that verdict is correct, then the view that science aims at knowledge has an advantage. Alternatively, the truth aim would have to be supplemented by an avoidance of error aim, in which case there is a question about how to balance these aims: To what extent does a possible gain in truth outweigh a risk of falsity? The epistemic account does not have to face such a question, since risky belief, even if true, fails to be knowledge.

7 Conclusion: Realism, Progress, and Change

A simplistic picture of the history of science presents science as a highly reliable source of truth—science progresses because it is able to add new truths to the stock of old truths. While it not clear that any scholar really believed this to be true, it may be the impression given by some whiggish, heroic histories of science. Furthermore, optimistic accounts of the scientific method or of inductive logic seem to suggest science should progress by accumulating truth. The history of science portrayed by Kuhn and others, with its periodic reversals of belief, rejects such a view. And if realist philosophies of science say that the history of science ought to be like this, then those philosophies may be rejected too.

If one draws antirealist conclusions from this history, the view of progress as increasing problem-solving power looks attractive. However, such a view does have to take a stand on what is happening during those moments of rupture when previous problem-solutions are rejected—episodes of Kuhn-loss. One may argue that there is an overall increase in problem-solving power, but to do so implies that there is a weighting of problems and their solutions, so that the new problem-and-solution combinations are worth more than those that are lost. Furthermore, Kuhn is clear that old problems-plus-solutions may be rejected on the promise of more and better to come. Problem-solving power is clearly a dispositional concept on this view. If so, it may not be as transparent as Laudan would like whether an episode of scientific change is progressive.

Realists will hold that this approach to progress suffers from not distinguishing apparent from real progress. That there is little or no difference is a consequence of the internalism espoused by Laudan—it should be possible to tell directly whether a problem has been solved by a proposed solution and progress thereby made. But to those who avail themselves of the concept of truth, a sequence of false solutions to a pseudo-problem (a problem founded on a false assumption) cannot be progress, however convincing such solutions are to those working within that paradigm.

The realist can maintain that the existence of falsehoods in the history of science is consistent with realism, so long as those falsehoods are in due course replaced by truths, without truths being replaced by falsehoods. Nonetheless, the favored variant on this for realists has been to conceive of progress in terms of truthlikeness. A sequence of theories may be progressive even if false when each is closer to the truth than its predecessor. This view can avoid much of the difficulty raised by Kuhn-loss, so long as the new problem-solutions are closer to the truth than those they replace. That seems to be the case in the standard examples. Although there was Kuhn-loss in the rejection of Descartes’s vortex theory, his problem-solutions were badly false (it is not because gravity operates via a vortex that the planets rotate in a plane and in the same direction). So the loss of those “solutions” should be nothing for a truth/truthlikeness account of progress to worry about. That said, there are some cases of truth-loss (e.g., that concerning the moisture content of infants’ bodies, when the humoral theory was abandoned) that per se would be regarded as regressive by the truth/truthlikeness standard. One might think that this is wrong, because it is progressive to cease believing in a proposition, true or false, if one discovers that one’s previous reasons for believing it are themselves mistaken. The truth/truthlikeness view might attempt to retrieve the situation by arguing that in such cases the truth-loss is outweighed by truth-gain, which would then require weighting some truths as more significant than others. More problematic are hypothetical cases in which there is no corresponding significant gain (such as the example in which N-rays turn out to exist and Blondlot was right but for the wrong reasons).

Neither Kuhn-loss nor truth-loss are, per se, problematic for the epistemic account. The episodes discussed, actual and hypothetical, are not regressive, since the beliefs rejected do not amount to knowledge. A regressive episode for the epistemic view is one in which there is knowledge loss—a scientific change that involves scientists knowing less at the end than at the beginning. Realists will expect such episodes to be few and far between, for if scientists have acquired enough evidence and good reasons so that they know that p , it will be fairly unlikely that evidence and reasons will come along that will persuade them to give up belief in p . That said, we can conceive of hypothetical cases; if a mass of misleading evidence is generated, then the community might lose its knowledge. But even such cases need not trouble the epistemic account of progress, for such episodes really would be regressive. Thus the epistemic account can claim both to match our pretheoretic judgments about which (real or hypothetical) episodes are progressive relative to their rivals while also finding it more straightforward to deliver the verdict that science has indeed generally been progressive.

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Niiniluoto (1980 : 428) raises three questions corresponding to first, third, and fourth of these.

See Becker (2001) for details.

I am now inclined to think that our ability to give a clear and unambiguous judgment in the cases just mentioned is evidence in favor of the second view, whereas I previously ( Bird 2007 ) took the first.

More formally: A is closer to the truth than B iff B’s true consequences are a subset of the true consequences of A and A’s false consequences are a subset of the false consequences of B and one or both of these two subset relations is a proper subset relation.

See Rowbottom (2008 , 2010 ) for an opposing view and further discussion of this case.

X-rays were first observed by Röntgen in 1895, while cathode rays were first observed by another German, Johann Hittorf, in 1869 and named as such by a third, Eugen Goldstein. Nonetheless, cathode rays were investigated primarily by British physicists—they were produced in the eponymous tube devised by William Crookes, and their nature, streams of electrons, was identified by J. J. Thompson in 1897, confirming the British (Thompson and Schuster) hypothesis against the German (Goldstein, Hertz, and Wiedemann) one that they were a form of electromagnetic radiation. Canal rays, also known as anode rays, were also produced in a Crookes tube and were discovered by Goldstein in 1886 and investigated by Wien and Thompson. Until N-rays, the French has no horse in the physical ray race.

Barnes himself thinks that we can use the epistemic features of such cases to identify an appropriately privileged language—appropriate to that epistemic circumstance. The language issue strikes me as a red herring and that Barnes’s contribution is more effective without the detour via language.

See Niiniluoto (2014) for criticisms of this claim—what follows, I hope, provides some answers to those criticisms.

Is “T is highly truthlike” a proposition of science? The answer might depend on one’s view of the truth predicate and its relatives, such as “truthlike.” For example, one might think that one’s use involves semantic ascent: “T” is about the world whereas “T is true” concerns words (and their relation to the world). On the other hand, a redundancy theorist about truth will think that “T is true” says exactly what “T” says and is equally about the world. Presumably analogous remarks could be make about “truthlike.” In my view, even if the truth and truthlike predicates do involve semantic ascent, they can still be used in scientific propositions: they still make claims about the world, even if they say something else as well. For example, if an economist says, “The simple supply and demand model is a good approximation to the truth as regards many commodity markets, but it is not a good approximation for markets with asymmetric information, such as the markets for complex financial securities and health insurance” then he or she is saying something both about the world and about a theory; the assertion does not seem any the less a scientific statement because of that.

Note that belief here does not need to be occurrent; it may be dispositional. This is not to say that scientists will believe all the significant true implications of a truthlike model—many scientists did not believe the ontological implications of the kinetic theory, developed in the 1850s and 1860s, until the twentieth century. It may be a matter of further discovery that a certain element of a model is highly truthlike rather than merely instrumental; such discoveries will themselves be contributions to progress, since now those elements will themselves, by being believed, become knowledge.

I note that in the earlier (2007) version of his article, Oddie begins “ Truth is the aim of inquiry,” whereas he now (2014) commences more circumspectly “ Truth is widely held to be the constitutive aim of inquiry.”

The debate about the aim of science naturally relates to debates concerning the nature and aim of belief; cf. Velleman (2000) , Wedgwood (2002) , and Owens (2003) .

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What is Scientific Progress? Lessons from Scientific Practice

• Published: 17 November 2013
• Volume 44 , pages 375–390, ( 2013 )

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• Moti Mizrahi 1  

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Alexander Bird argues for an epistemic account of scientific progress, whereas Darrell Rowbottom argues for a semantic account. Both appeal to intuitions about hypothetical cases in support of their accounts. Since the methodological significance of such appeals to intuition is unclear, I think that a new approach might be fruitful at this stage in the debate. So I propose to abandon appeals to intuition and look at scientific practice instead. I discuss two cases that illustrate the way in which scientists make judgments about progress. As far as scientists are concerned, progress is made when scientific discoveries contribute to the increase of scientific knowledge of the following sorts: empirical, theoretical, practical, and methodological. I then propose to articulate an account of progress that does justice to this broad conception of progress employed by scientists. I discuss one way of doing so, namely, by expanding our notion of scientific knowledge to include both know-that and know-how.

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Scientific Progress and the Search for Truth

The Noetic Account of Scientific Progress and the Factivity of Understanding

Understanding scientific progress: the noetic account

Finnur Dellsén

The idea of the growth of knowledge looms large in the history of science, at least from the early modern period. It may be traced back to Francis Bacon, whose Instauratio magna (1620) frontispiece declares: “ Multi pertransibunt et augebitur scientia .” Sarton ( 1927 , I, 3–4) expressed a similar idea as follows: “ The acquisition and systematization of positive knowledge is the only human activity which is truly cumulative and progressive .”

It is worth noting that, according to Bird ( 2007 , 84) contributions to scientific knowledge can be more or less significant. Bird supplements (E) with the notion of significance because of cases of pointless investigation, such as investigating grains of sand. See also Kitcher ( 1993 , 117) on significant truths.

Proponents of (S) include Popper ( 1979 ) and Niiniluoto ( 1987 ). As is well known, explicating the notion of approximate truth is notoriously difficult. Popper’s ( 1972 ) attempt to formalize the notion of verisimilitude was shown to be problematic (see Miller 1974 and Tichý 1974 ). Other formal approaches, such as the similarity approach (see, e.g., Niiniluoto 1984 , 1987 ) and the type hierarchy approach (see, e.g., Aronson et al. 1994 ), also suffer from technical problems (see, e.g., Aronson 1990 and Psillos 1999). For these reasons, realists have tried to explicate approximate truth in non-formal, qualitative terms (see, e.g., Leplin 1997 , Boyd 1990 , Weston 1992 , and Smith 1998 ). For example, it has been suggested that T 2 is more approximately true than its predecessor T 1 if T 1 can be described as a “limiting case” of T 2 (see, e.g., Post 1971 ; French and Kamminga 1993 ). But there are problems with these informal approaches as well (Chakravartty 2010 ).

It is important to note that Bird does not take knowledge to be justified true belief. Rather, he thinks that his arguments support Williamson’s ( 1997 , 2000 ) view that knowledge is a foundational concept in epistemology and that it does not have an analysis. Henceforth, I will take knowledge to be an unanalyzable primitive in the same way that Williamson and Bird do.

Both Bird and Rowbottom basically argue as follows: “Upon considering hypothetical case C , it seems to me that p ; therefore, p .” However, the problem is that, while it seems to Bird that p , it seems to Rowbottom that not- p . That is why the methodological significance of such appeals to intuition is unclear (see Mizrahi 2012 , 2013 ).

Cf. Lakatos ( 1970 , 91); Bird ( 2008 , 73); Leplin ( 1997 , 99 and 102).

It is worth noting that scientific practices are quite diverse and vary across scientific disciplines and historical periods. See, e.g., Boon ( 2011 ) and the essays collected in Pickering ( 1992 ).

Available at http://nobelprize.org/nobel_prizes/medicine/laureates/1930/press.html .

Available at http://nobelprize.org/nobel_prizes/medicine/laureates/1930/landsteiner-lecture.pdf .

Available at http://nobelprize.org/nobel_prizes/medicine/laureates/1904/pavlov-lecture.html .

See, e.g., the 1907 Nobel Prize in Physiology or Medicine awarded to Laveran “in recognition of his work on the role played by protozoa in causing diseases” ( http://www.nobelprize.org/nobel_prizes/medicine/laureates/1907/laveran.html ), the 1908 Nobel Prize in Physiology or Medicine awarded to Mechnikov and Ehrlich “in recognition of their work on immunity” ( http://www.nobelprize.org/nobel_prizes/medicine/laureates/1908/ ), and the 1953 Nobel Prize in Physiology or Medicine awarded to Krebs “for his discovery of the citric acid cycle” ( http://www.nobelprize.org/nobel_prizes/medicine/laureates/1953/ #). See also Zuckerman ( 1996 ).

Admittedly, I am painting EK with a rather broad brush. Finer distinctions can be made, for example, between “raw data,” i.e., the sort of data we get from immediate observation, and “models of data” (Suppes 1962 , 252–261). Other distinctions can be drawn between types of data processing, such as data assessment and data reduction (Hacking 1992 , 29–64). For present purposes, however, the important point is that advancements in terms of EK count as scientific progress.

Cf. Feyerabend ( 1987 ). As an anonymous referee pointed out, historians of science are becoming increasingly hostile to the “Great Men” style of historiography. This increasing hostility is partly due to the recognition that these “Great Men” stood on the shoulders of others, including lab technicians and assistants, data collectors, experimenters, inventors, and the like. See also Fissell and Cooter ( 2003 , 156).

By necessary and sufficient conditions here, I do not mean “individually necessary and jointly sufficient” as in conceptual analysis, but rather collectively sufficient for scientific progress, or better yet, constitutive criteria for progress.

Baird and Faust suggest that philosophers of science talk about knowledge, but when they do, it is theoretical knowledge exclusively. On the other hand, Kitcher ( 2002 , 385) says that, within the philosophy of science, “there is little explicit discussion of scientific knowledge.”

Available at http://nationalhumanitiescenter.org/pds/becomingamer/ideas/text4/amerphilsociety.pdf .

As an anonymous reviewer pointed out, one reason why philosophers of science focus on theories (the end result of science) rather than practices might be that most of them are not practicing scientists (although there are exceptions, such as Michael Weisberg). In that respect, it is also important to mention the Society for Philosophy of Science in Practice (SPSP) whose “aim [is] to change [the fact that concern with practice has always been somewhat outside the mainstream of English-language philosophy of science] through a conscious and organized programme of detailed and systematic study of scientific practice that does not dispense with concerns about truth and rationality” ( http://www.philosophy-science-practice.org/en/mission-statement/ ).

As an anonymous referee pointed out, in order to understand scientific progress, we might need to expand our notion of scientific knowledge even further—beyond know-that and know-how—to include something like Baird’s ( 2004 ) notion of “thing knowledge.” Roughly speaking, Baird’s idea is that things, e.g., scientific instruments, bear knowledge. Doing justice to Baird’s material epistemology is beyond the scope of this paper.

Available at http://www.nytimes.com/2009/10/13/science/13conv.html?emc=eta1 .

As an anonymous referee pointed out, we might add to this list the 1939 Nobel Prize in Physics, awarded to E. O. Lawrence “for the invention and development of the cyclotron and for results obtained with it” ( http://www.nobelprize.org/nobel_prizes/physics/laureates/1939/ ), apropos Baird and Faust’s discussion of the cyclotron quoted in Sect. 5 .

Available at http://ppc08.astro.spbu.ru/index.html .

For more on the philosophy of space exploration, see Munevar (unpublished manuscript). Available at http://philosophyofspaceexploration.blogspot.com/ .

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Acknowledgments

A version of this paper was presented at the Third Biennial Conference of the Society for Philosophy of Science in Practice in the University of Exeter, UK in June 2011. I would like to thank the audience and members of the organization committee for their helpful feedback. Special thanks are due to Marcel Boumans. I am grateful to the PSC-CUNY for the generous financial aid and travel funds. I am also indebted to Alberto Cordero, Catherine Wilson, and Joseph Dauben for their comments on earlier drafts of this paper.

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Mizrahi, M. What is Scientific Progress? Lessons from Scientific Practice. J Gen Philos Sci 44 , 375–390 (2013). https://doi.org/10.1007/s10838-013-9229-1

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Thomas Kuhn: the man who changed the way the world looked at science

F ifty years ago this month, one of the most influential books of the 20th century was published by the University of Chicago Press. Many if not most lay people have probably never heard of its author, Thomas Kuhn, or of his book, The Structure of Scientific Revolutions , but their thinking has almost certainly been influenced by his ideas. The litmus test is whether you've ever heard or used the term "paradigm shift", which is probably the most used – and abused – term in contemporary discussions of organisational change and intellectual progress. A Google search for it returns more than 10 million hits, for example. And it currently turns up inside no fewer than 18,300 of the books marketed by Amazon . It is also one of the most cited academic books of all time . So if ever a big idea went viral, this is it.

The real measure of Kuhn's importance, however, lies not in the infectiousness of one of his concepts but in the fact that he singlehandedly changed the way we think about mankind's most organised attempt to understand the world. Before Kuhn, our view of science was dominated by philosophical ideas about how it ought to develop ("the scientific method"), together with a heroic narrative of scientific progress as "the addition of new truths to the stock of old truths, or the increasing approximation of theories to the truth, and in the odd case, the correction of past errors", as the Stanford Encyclopaedia of Philosophy puts it. Before Kuhn, in other words, we had what amounted to the Whig interpretation of scientific history, in which past researchers, theorists and experimenters had engaged in a long march, if not towards "truth", then at least towards greater and greater understanding of the natural world.

Kuhn's version of how science develops differed dramatically from the Whig version. Where the standard account saw steady, cumulative "progress", he saw discontinuities – a set of alternating "normal" and "revolutionary" phases in which communities of specialists in particular fields are plunged into periods of turmoil, uncertainty and angst. These revolutionary phases – for example the transition from Newtonian mechanics to quantum physics – correspond to great conceptual breakthroughs and lay the basis for a succeeding phase of business as usual. The fact that his version seems unremarkable now is, in a way, the greatest measure of his success. But in 1962 almost everything about it was controversial because of the challenge it posed to powerful, entrenched philosophical assumptions about how science did – and should – work.

What made it worse for philosophers of science was that Kuhn wasn't even a philosopher: he was a physicist, dammit. Born in 1922 in Cincinnati, he studied physics at Harvard, graduating summa cum laude in 1943, after which he was swept up by the war effort to work on radar. He returned to Harvard after the war to do a PhD – again in physics – which he obtained in 1949. He was then elected into the university's elite Society of Fellows and might have continued to work on quantum physics until the end of his days had he not been commissioned to teach a course on science for humanities students as part of the General Education in Science curriculum. This was the brainchild of Harvard's reforming president, James Conant , who believed that every educated person should know something about science.

The course was centred around historical case studies and teaching it forced Kuhn to study old scientific texts in detail for the first time. (Physicists, then as now, don't go in much for history.) Kuhn's encounter with the scientific work of Aristotle turned out to be a life- and career-changing epiphany.

"The question I hoped to answer," he recalled later , "was how much mechanics Aristotle had known, how much he had left for people such as Galileo and Newton to discover. Given that formulation, I rapidly discovered that Aristotle had known almost no mechanics at all… that conclusion was standard and it might in principle have been right. But I found it bothersome because, as I was reading him, Aristotle appeared not only ignorant of mechanics, but a dreadfully bad physical scientist as well. About motion, in particular, his writings seemed to me full of egregious errors, both of logic and of observation."

What Kuhn had run up against was the central weakness of the Whig interpretation of history. By the standards of present-day physics, Aristotle looks like an idiot. And yet we know he wasn't. Kuhn's blinding insight came from the sudden realisation that if one is to understand Aristotelian science, one must know about the intellectual tradition within which Aristotle worked. One must understand, for example, that for him the term "motion" meant change in general – not just the change in position of a physical body, which is how we think of it. Or, to put it in more general terms, to understand scientific development one must understand the intellectual frameworks within which scientists work. That insight is the engine that drives Kuhn's great book.

Kuhn remained at Harvard until 1956 and, having failed to get tenure, moved to the University of California at Berkeley where he wrote Structure… and was promoted to a professorship in 1961. The following year, the book was published by the University of Chicago Press. Despite the 172 pages of the first edition, Kuhn – in his characteristic, old-world scholarly style – always referred to it as a mere "sketch". He would doubtless have preferred to have written an 800-page doorstop.

But in the event, the readability and relative brevity of the "sketch" was a key factor in its eventual success. Although the book was a slow starter, selling only 919 copies in 1962-3, by mid-1987 it had sold 650,000 copies and sales to date now stand at 1.4 million copies . For a cerebral work of this calibre, these are Harry Potter-scale numbers.

Kuhn's central claim is that a careful study of the history of science reveals that development in any scientific field happens via a series of phases. The first he christened "normal science" – business as usual, if you like. In this phase, a community of researchers who share a common intellectual framework – called a paradigm or a "disciplinary matrix" – engage in solving puzzles thrown up by discrepancies (anomalies) between what the paradigm predicts and what is revealed by observation or experiment. Most of the time, the anomalies are resolved either by incremental changes to the paradigm or by uncovering observational or experimental error. As philosopher Ian Hacking puts it in his terrific preface to the new edition of Structure : "Normal science does not aim at novelty but at clearing up the status quo. It tends to discover what it expects to discover."

The trouble is that over longer periods unresolved anomalies accumulate and eventually get to the point where some scientists begin to question the paradigm itself. At this point, the discipline enters a period of crisis characterised by, in Kuhn's words, "a proliferation of compelling articulations, the willingness to try anything, the expression of explicit discontent, the recourse to philosophy and to debate over fundamentals". In the end, the crisis is resolved by a revolutionary change in world-view in which the now-deficient paradigm is replaced by a newer one. This is the paradigm shift of modern parlance and after it has happened the scientific field returns to normal science, based on the new framework. And so it goes on.

This brutal summary of the revolutionary process does not do justice to the complexity and subtlety of Kuhn's thinking. To appreciate these, you have to read his book. But it does perhaps indicate why Structure… came as such a bombshell to the philosophers and historians who had pieced together the Whig interpretation of scientific progress.

As an illustration, take Kuhn's portrayal of "normal" science. The most influential philosopher of science in 1962 was Karl Popper, described by Hacking as "the most widely read, and to some extent believed, by practising scientists". Popper summed up the essence of "the" scientific method in the title of one of his books: Conjectures and Refutations . According to Popper, real scientists (as opposed to, say, psychoanalysts) were distinguished by the fact that they tried to refute rather than confirm their theories. And yet Kuhn's version suggested that the last thing normal scientists seek to do is to refute the theories embedded in their paradigm!

Many people were also enraged by Kuhn's description of most scientific activity as mere "puzzle-solving" – as if mankind's most earnest quest for knowledge was akin to doing the Times crossword. But in fact these critics were over-sensitive. A puzzle is something to which there is a solution. That doesn't mean that finding it is easy or that it will not require great ingenuity and sustained effort. The unconscionably expensive quest for the Higgs boson that has recently come to fruition at Cern, for example, is a prime example of puzzle-solving because the existence of the particle was predicted by the prevailing paradigm, the so-called "standard model" of particle physics.

But what really set the cat among the philosophical pigeons was one implication of Kuhn's account of the process of paradigm change. He argued that competing paradigms are "incommensurable": that is to say, there exists no objective way of assessing their relative merits. There's no way, for example, that one could make a checklist comparing the merits of Newtonian mechanics (which applies to snooker balls and planets but not to anything that goes on inside the atom) and quantum mechanics (which deals with what happens at the sub-atomic level). But if rival paradigms are really incommensurable, then doesn't that imply that scientific revolutions must be based – at least in part – on irrational grounds? In which case, are not the paradigm shifts that we celebrate as great intellectual breakthroughs merely the result of outbreaks of mob psychology?

Kuhn's book spawned a whole industry of commentary, interpretation and exegesis. His emphasis on the importance of communities of scientists clustered round a shared paradigm essentially triggered the growth of a new academic discipline – the sociology of science – in which researchers began to examine scientific disciplines much as anthropologists studied exotic tribes, and in which science was regarded not as a sacred, untouchable product of the Enlightenment but as just another subculture.

As for his big idea – that of a "paradigm" as an intellectual framework that makes research possible –well, it quickly escaped into the wild and took on a life of its own. Hucksters, marketers and business school professors adopted it as a way of explaining the need for radical changes of world-view in their clients. And social scientists saw the adoption of a paradigm as a route to respectability and research funding, which in due course led to the emergence of pathological paradigms in fields such as economics, which came to esteem mastery of mathematics over an understanding of how banking actually works, with the consequences that we now have to endure.

The most intriguing idea, however, is to use Kuhn's thinking to interpret his own achievement. In his quiet way, he brought about a conceptual revolution by triggering a shift in our understanding of science from a Whiggish paradigm to a Kuhnian one, and much of what is now done in the history and philosophy of science might be regarded as "normal" science within the new paradigm. But already the anomalies are beginning to accumulate. Kuhn, like Popper, thought that science was mainly about theory, but an increasing amount of cutting-edge scientific research is data- rather than theory-driven . And while physics was undoubtedly the Queen of the Sciences when Structure… was being written, that role has now passed to molecular genetics and biotechnology. Does Kuhn's analysis hold good for these new areas of science? And if not, isn't it time for a paradigm shift?

In the meantime, if you're making a list of books to read before you die, Kuhn's masterwork is one.

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Scientific Progress

Science is often distinguished from other domains of human culture by its progressive nature: in contrast to art, religion, philosophy, morality, and politics, there exist clear standards or normative criteria for identifying improvements and advances in science. For example, the historian of science George Sarton argued that “the acquisition and systematization of positive knowledge are the only human activities which are truly cumulative and progressive,” and “progress has no definite and unquestionable meaning in other fields than the field of science” (Sarton 1936). However, the traditional cumulative view of scientific knowledge was effectively challenged by many philosophers of science in the 1960s and the 1970s, and thereby the notion of progress was also questioned in the field of science. Debates on the normative concept of progress are at the same time concerned with axiological questions about the aims and goals of science. The task of philosophical analysis is to consider alternative answers to the question: What is meant by progress in science? This conceptual question can then be complemented by the methodological question: How can we recognize progressive developments in science? Relative to a definition of progress and an account of its best indicators, one may then study the factual question: To what extent, and in which respects, is science progressive?

1. The Study of Scientific Change

• 2.The Concept of Progress

3. Theories of Scientific Progress

4. is science progressive, bibliography, other internet resources, related entries.

The idea that science is a collective enterprise of researchers in successive generations is characteristic of the Modern Age (Nisbet 1980). Classical empiricists (Francis Bacon) and rationalists (René Descartes) of the seventeenth century urged that the use of proper methods of inquiry guarantees the discovery and justification of new truths. This cumulative view of scientific progress was an important ingredient in the optimism of the eighteenth century Enlightenment, and it was incorporated in the 1830s in Auguste Comte's program of positivism: by accumulating empirically certified truths science also promotes progress in society. Other influential trends in the nineteenth century were the Romantic vision of organic growth in culture, Hegel's dynamic account of historical change, and the theory of evolution. They all inspired epistemological views (e.g., among Marxists and pragmatists) which regarded human knowledge as a process. Philosopher-scientists with an interest in the history of science (William Whewell, Charles Peirce, Ernst Mach, Pierre Duhem) gave interesting analyses of some aspects of scientific change.

In the early twentieth century, analytic philosophers of science started to apply modern logic to the study of science. Their main focus was the structure of scientific theories and patterns of inference (Suppe 1977). This “synchronic” investigation of the “finished products” of scientific activities was questioned by philosophers who wished to pay serious attention to the “diachronic” study of scientific change. Among these contributions one can mention N.R. Hanson's Patterns of Discovery (1958), Karl Popper's The Logic of Scientific Discovery (1959) and Conjectures and Refutations (1963), Thomas Kuhn's The Structure of Scientific Revolutions (1962), Paul Feyerabend's incommensurability thesis (Feyerabend 1962), Imre Lakatos' methodology of scientific research programmes (Lakatos and Musgrave 1970), and Larry Laudan's Progress and Its Problems (1977). Darwinist models of evolutionary epistemology were advocated by Popper's Objective Knowledge: An Evolutionary Approach (1972) and Stephen Toulmin's Human Understanding (1972). These works challenged the received view about the development of scientific knowledge and rationality. Popper's falsificationism, Kuhn's account of scientific revolutions, and Feyerabend's thesis of meaning variance shared the view that science does not grow simply by accumulating new established truths upon old ones. Except perhaps during periods of Kuhnian normal science, theory change is not cumulative or continuous: the earlier results of science will be rejected, replaced, and reinterpreted by new theories and conceptual frameworks. Popper and Kuhn differed, however, in their definitions of progress: the former appealed to the idea that successive theories may approach towards the truth, while the latter characterized progress in terms of the problem-solving capacity of theories.

Since the mid-1970s, a great number of philosophical works have been published on the topics of change, development, and progress in science (Harré 1975; Stegmüller 1976; Howson 1976; Rescher 1978; Radnitzky and Andersson 1978, 1979; Niiniluoto and Tuomela 1979; Dilworth 1981; Smith 1981; Hacking 1981; Schäfer 1983; Niiniluoto 1984; Laudan 1984a; Rescher 1984; Pitt 1985; Radnitzky and Bartley 1987; Callebaut and Pinxten 1987; Balzer et al. 1987; Hull 1988; Gavroglu et al. 1989; Kitcher 1993; Pera 1994). These studies have also led to many important novelties being added to the toolbox of philosophers of science. One of them is the systematic study of inter-theory relations, such as reduction (Balzer et al. 1984; Pearce 1987; Balzer 2000; Jonkisz 2000), correspondence (Krajewski 1977; Nowak 1980; Pearce and Rantala 1984; Niiniluoto 1999; Nowakowa and Nowak 2000; Rantala 2002), and belief revision (Gärdenfors, 1988; Aliseda, 2006). Another was the recognition that, besides individual statements and theories, there is also a need to consider temporally developing units of scientific activity and achievement: Kuhn's paradigm-directed normal science, Lakatos' research programme, Laudan's research tradition, Wolfgang Stegmüller's (1976) dynamic theory evolution, Philip Kitcher's (1993) consensus practice. A new tool that is employed in many defenses of realist views of scientific progress (Niiniluoto 1980, 1984, 1999; Aronson, Harré, and Way 1994; Kuipers 2000) is the notion of truthlikeness or verisimilitude (Popper 1963, 1970; Niiniluoto 1987).

New interest about the development of science promoted close co-operation between historians and philosophers of science. For example, case studies of historical examples (e.g., the replacement of Newton's classical mechanics by quantum theory and theory of relativity) have inspired many philosophical treatments of scientific revolutions. Further interesting material for philosophical discussions about scientific progress is provided by quantitative approaches in the study of the growth of scientific publications (de Solla Price 1963; Rescher 1978) and science indicators (Elkana et al . 1978). Sociologists of science have studied the dynamic interaction between the scientific community and other social institutions. One of their favorite topics has been the emergence of new scientific specialties (Mulkay 1975; Niiniluoto 1995b). Sociologists are also concerned with the pragmatic problem of progress: what is the best way of organizing research activities in order to promote scientific advance. In this way, models of scientific change turn out to be relevant to issues of science policy (Böhme 1977; Schäfer 1983; Niiniluoto 1984).

2. The Concept of Progress

2.1 aspects of scientific progress.

Science is a multi-layered complex system involving a community of scientists engaged in research using scientific methods in order to produce new knowledge. Thus, the notion of science may refer to a social institution, the researchers, the research process, the method of inquiry, and scientific knowledge. The concept of progress can be defined relative to each of these aspects of science. Hence, different types of progress can be distinguished relative to science: economical (the increased funding of scientific research), professional (the rising status of the scientists and their academic institutions in the society), educational (the increased skill and expertise of the scientists), methodical (the invention of new methods of research, the refinement of scientific instruments), and cognitive (increase or advancement of scientific knowledge). These types of progress have to be conceptually distinguished from advances in other human activities, even though it may turn out that scientific progress has at least some factual connections with technological progress (increased effectiveness of tools and techniques) and social progress (economic prosperity, quality of life, justice in society).

All of these aspects of scientific progress may involve different considerations, so that there is no single concept that would cover all of them. For our purposes, it is appropriate here to concentrate only on cognitive progress, i.e., to give an account of advances of science in terms of its success in knowledge-seeking.

2.2 Progress vs. Development

“Progress” is an axiological or a normative concept, which should be distinguished from such neutral descriptive terms as “change” and “development” (Niiniluoto 1995a). In general, to say that a step from stage A to stage B constitutes progress means that B is an improvement over A in some respect, i.e., B is better than A relative to some standards or criteria. In science, it is a normative demand that all contributions to research should yield some cognitive profit, and their success in this respect can be assessed before publication by referees (peer review) and after publication by colleagues. Hence, the theory of scientific progress is not merely a descriptive account of the patterns of developments that science has in fact followed. Rather, it should give a specification of the values or aims that can be used as the constitutive criteria for “good science.”

The “naturalist” program in science studies suggests that normative questions in the philosophy of science can be reduced to historical and sociological investigations of the actual practice of science. In this spirit, Laudan has defended the project of testing philosophical models of scientific change by the history of science: such models, which are “often couched in normative language,” can be recast “into declarative statements about how science does behave” (Laudan et al. 1986; Donovan et al. 1988). It may be the case that most scientific work, at least the best science of each age, is also good science. But it is also evident that scientists often have different opinions about the criteria of good science, and rival researchers and schools make different choices in their preference of theories and research programs. Therefore, it can be argued against the naturalists that progress should not be defined by the actual developments of science: the definition of progress should give us a normative standard for appraising the choices that the scientific communities have made, could have made, are just now making, and will make in the future. The task of finding and defending such standards is a genuinely philosophical one which can be enlightened by history and sociology but which cannot be reduced to empirical studies of science.

2.3 Progress, Quality, Impact

For many goal-directed activities it is important to distinguish between quality and progress . Quality is primarily an activity-oriented concept, concerning the skill and competence in the performance of some task. Progress is a result-oriented concept, concerning the success of a product relative to some goal. All acceptable work in science has to fulfill certain standards of quality. But it seems that there are no necessary connections between quality and progress in science. Sometimes very well-qualified research projects fail to produce important new results, while less competent but more lucky works lead to success. Nevertheless, the skillful use of the methods of science will make progress highly probable. Hence, the best practical strategy in promoting scientific progress is to support high-quality research.

Following the pioneering work of Derek de Solla Price (1963) in “scientometrics,” quantitative science indicators have been proposed as measures of scientific activity (Elkana et al . 1978). For example, output measures like publication counts are measures of scholarly achievement, but it is problematic whether such a crude measure is sufficient to indicate quality (cf. Chotkowski La Follette 1982). The number of articles in refereed journals is an indicator of the quality of their author, but it is clear that this indicator cannot yet define what progress means, since publications may contribute different amounts to the advance of scientific knowledge. “Rousseau's Law” proposed by Nicholas Rescher (1978) marks off a certain part of the total number of publications as “important” or “first-rate,” but this is merely an alleged statistical regularity.

Another example of a science indicator, citation index , is an indicator for the “impact” of a publication and for the “visibility” of its author within the scientific community. Martin and Irvine (1983) suggest that the concept of scientific progress should be linked to the notion of impact , i.e., the actual influence of research to the surrounding scientific activities at a given time. It is no doubt correct that one cannot advance scientific knowledge without influencing the epistemic state of the scientific community. But the impact of a publication as such only shows that it has successfully “moved” the scientific community in some direction. If science is goal-directed, then we must acknowledge that movement in the wrong direction does not constitute progress (Niiniluoto 1984).

The failure of science indicators to function as definitions of scientific progress is due to the fact that they do not take into account the semantic content of scientific publications. To determine whether a work W gives a contribution to scientific progress, we have to specify what W says (alternatively: what problems W solves) and then relate this content of W to the knowledge situation of the scientific community at the time of the publication of W . For the same reason, research assessment exercises may use science indicators as tools, but ultimately they have to rely on the judgment of peers who have substantial knowledge in the field.

2.4 Progress and Goals

Progress is a goal-relative concept. But even when we consider science as a knowledge-seeking cognitive enterprise, there is no reason to assume that the goal of science is one-dimensional. In contrast, as Isaac Levi's classic Gambling With Truth (1967) argued, the cognitive aim of scientific inquiry has to be defined as a weighted combination of several different, and even conflicting, epistemic utilities . As we shall see in Section 3, alternative theories of scientific progress can be understood as specifications of such epistemic utilities. For example, they might include truth and information (Levi 1967; see also Popper 1959, 1963) or explanatory and predictive power (Hempel 1965). Kuhn's (1977) list of the values of science includes accuracy, consistency, scope, simplicity, and fruitfulness.

A goal may be accessible in the sense that it can be reached in a finite number of steps in a finite time. A goal is utopian if it cannot be reached or even approached. Thus, utopian goals cannot be rationally pursued, since no progress can be made in an attempt to reach them. Walking to the moon is a utopian task in this sense. However, not all inaccessible goals are utopian: an unreachable goal, such as being morally perfect, can function as a regulative principle in Kant's sense, if it guides our behavior so that we are able to make progress towards it.

The classical sceptic argument against science, repeated by Laudan (1984a), is that knowing the truth is a utopian task. Kant's answer to this argument was to regard truth as a regulative principle for science. Charles S. Peirce, the founder of American pragmatism, argued that the access to the truth as the ideal limit of scientific inquiry is “destined” or guaranteed in an “indefinite” community of investigators (cf. Niiniluoto 1980, 1984). Almeder's (1983) interpretation of Peirce's view of scientific progress is that there is only a finite number of scientific problems and they will all be solved in a finite time. However, there does not seem to be any reason to think that truth is generally accessible in this strong sense. Therefore, the crucial question is whether it is possible to make rational appraisals that we have made progress in the direction of the truth (see Section 3.4).

A goal is effectively recognizable if there are routine or mechanical tests for showing that the goal has been reached or approached. If the defining criteria of progress are not recognizable in this strong sense, we have to distinguish true or real progress from our perceptions or estimations of progress . In other words, claims of the form ‘The step from stage A to stage B is progressive’ have to be distinguished from our appraisals of the form ‘The step from stage A to stage B seems progressive on the available evidence’. The latter appraisals, as our own judgments, are recognizable, but the former claims may be correct without our knowing it. Characteristics and measures that help us to make such appraisals are then indicators of progress .

Laudan requires that a rational goal for science should be accessible and effectively recognizable (Laudan 1977, 1984a). This requirement, which he uses to rule out truth as a goal of science, is very strong. The demands of rationality cannot dictate that a goal has to be given up, if there are reasonable indicators of progress towards it.

A goal may be backward-looking or forward-looking : it may refer to the starting point or to the destination point of an activity. If my aim is to travel as far from home as possible, my success is measured by my distance from Helsinki. If I wish to become ever better and better piano player, my improvement can be assessed relative to my earlier stages, not to any ideal Perfect Pianist. But if I want to travel to San Francisco, my progress is a function of my distance from the destination. Only in the special case, where there is only one way from A to B , the backward-looking and the forward-looking criteria (i.e., distance from A and distance to B ) determine each other.

Kuhn and Stegmüller were advocating backward-looking criteria of progress. In arguing against the view that “the proper measure of scientific achievement is the extent to which it brings us closer to” the ultimate goal of “one full, objective true account of nature,” Kuhn suggested that we should “learn to substitute evolution-from-what-we-know for evolution-toward-what-we-wish-to-know” (Kuhn 1970, p. 171). In the same spirit, Stegmüller (1976) argued that we should reject all variants of “a teleological metaphysics” defining progress in terms of “coming closer and closer to the truth.”

A compromise between forward-looking and backward-looking criteria can be proposed in the following way. If science is viewed as a knowledge-seeking activity, it is natural to define real progress in forward-looking terms: the cognitive aim of science is to know something that is still unknown, and our real progress depends on our distance from this destination. But, as this goal is unknown to us, our estimates or perceptions of progress have to be based on backward-looking evidential considerations. This kind of view of the aims of science does not presuppose the existence of one unique ultimate goal. To use Levi's words, our goals may be “myopic” rather than “messianic” (Levi 1985): the particular target that we wish to hit in the course of our inquiry has to be redefined “locally,” relative to each cognitive problem situation. Furthermore, in addition to the multiplicity of the possible targets, there may be several roads that lead to the same destination. The forward-looking character of the goals of inquiry does not exclude what Stegmüller calls “progress branching.” This is analogous to the simple fact that we may approach San Francisco from New York along two different ways—via Chicago or St Louis.

2.5 Progress and Rationality

Some philosophers use the concepts of progress and rationality as synonyms: progressive steps in science are precisely those that are based upon the scientists' rational choices. One possible objection is that scientific discoveries are progressive when they introduce novel ideas, even though they cannot be fully explained in rational terms (Popper 1959; cf. Hanson 1958; Kleiner 1993). However, another problem is more relevant here: By whose lights should such steps be evaluated? This question is urgent especially if we acknowledge that standards of good science have changed in history (Laudan 1984a).

As we shall see, the main rival philosophical theories of progress propose absolute criteria, such as problem-solving capacity or increasing truthlikeness, that are applicable to all developments of science throughout its history. On the other hand, rationality is a methodological concept which is historically relative : in assessing the rationality of the choices made by the past scientists, we have to study the aims, standards, methods, alternative theories and available evidence accepted within the scientific community at that time (cf. Doppelt, 1983, Laudan, 1987; Niiniluoto 1999). If the scientific community SC at a given point of time t accepted the standards V , then the preference of SC for theory T over T ′ on evidence e was rational just in case the epistemic utility of T relative to V was higher than that of T ′. But in a new situation, where the standards were different from V , a different preference might have been rational.

3.1 Realism and Instrumentalism

A major controversy among philosophers of science is between instrumentalist and realist views of scientific theories (Leplin 1984; Psillos 1999; Niiniluoto 1999). The instrumentalists follow Duhem in thinking that theories are merely conceptual tools for classifying, systematizing and predicting observational statements, so that the genuine content of science is not to be found on the level of theories (Duhem 1954). Scientific realists , by contrast, regard theories as attempts to describe reality even beyond the realm of observable things and regularities, so that theories can be regarded as statements having a truth value. Excluding naive realists, most scientists are fallibilists in Peirce's sense: scientific theories are hypothetical and always corrigible in principle. They may happen to be true, but we cannot know this for certain in any particular case. But even when theories are false, they can be cognitively valuable if they are closer to the truth than their rivals (Popper 1963). Theories should be testable by observational evidence, and success in empirical tests gives inductive confirmation (Hintikka 1968; Niiniluoto and Tuomela 1973; Kuipers 2000) or non-inductive corroboration to the theory (Popper 1959).

It might seem natural to expect that the main rival accounts of scientific progress would be based upon the positions of instrumentalism and realism. But this is only partly true. To be sure, naive realists as a rule hold the accumulation-of-truths view of progress, and many philosophers combine the realist view of theories with the axiological thesis that truth is an important goal of scientific inquiry. A non-cumulative version of the realist view of progress can be formulated by using the notion of truthlikeness. But there are also philosophers who accept the possibility of a realist treatment of theories, but still deny that truth is a relevant value of science which could have a function in the characterization of scientific progress. Bas van Fraassen's (1980) constructive empiricism takes the desideratum of science to be empirical adequacy : what a theory says about the observable should be true. The acceptance of a theory involves only the claim that it is empirically adequate, not its truth on the theoretical level. Van Fraassen has not developed an account of scientific progress in terms of his constructive empiricism, but presumably such an account would be close to empiricist notions of reduction and Laudan's account of problem-solving ability (see Section 3.2).

An instrumentalist who denies that theories have truth values usually defines scientific progress by referring to other virtues theories may have, such as their increasing empirical success. In 1908 Duhem expressed this idea by a simile: scientific progress is like a mounting tide, where waves rise and withdraw, but under this to-and-fro motion there is a slow and constant progress. However, he gave a realist twist to his view by assuming that theories classify experimental laws, and progress means that the proposed classifications approach a “natural classification” (Duhem 1954).

Evolutionary epistemology is open to instrumentalist (Toulmin) and realist (Popper) interpretations. A biological approach to human knowledge naturally gives emphasis to the pragmatist view that theories function as instruments of survival. Darwinist evolution in biology is not goal-directed with a fixed forward-looking goal; rather, species adapt themselves to an ever changing environment. In applying this account to the problem of knowledge-seeking, the fitness of a theory can be taken to mean that the theory is accepted by members of the scientific community. But a realist can reinterpret the evolutionary model by taking fitness to mean the truth or truthlikeness of a theory.

3.2 Empirical Success and Problem-Solving

For a constructive empiricist, it would be natural to think that among empirically adequate theories one theory T 2 is better than another theory T 1 if T 2 entails more true observational statements than T 1 . Such a comparison makes sense at least if the observation statements entailed by T 1 are a proper subset of those entailed by T 2 . Kemeny and Oppenheim (1956) gave a similar condition in their definition of reduction: T 1 is reducible to T 2 if and only if T 2 is at least as well systematized as T 1 and T 2 is observationally stronger than T 1 , i.e., all observational statements explained by T 1 are also consequences of T 2 . Variants of such an empirical reduction relation has been given by the structuralist school in terms of set-theoretical structures (Stegmüller 1976; Scheibe 1986; Balzer et al. 1987; Moulines 2000). A similar idea, but applied to cases where the first theory T 1 has been falsified by some observational evidence, was used by Lakatos in his definition of empirically progressive research programmes: the new superseding theory T 2 should have corroborated excess content relative to T 1 and T 2 should contain all the unrefuted content of T 1 (Lakatos and Musgrave 1970). The definition of Kuipers (2000) allows that even the new theory T 2 is empirically refuted: T 2 should have (in the sense of set-theoretical inclusion) more empirical successes, but fewer empirical counter-examples than T 1 .

Against these cumulative definitions it has been argued that definitions of empirical progress have to take into account an important complication. A new theory often corrects the empirical consequences of the previous one, i.e., T 2 entails observational statements e 2 which are in some sense close to the corresponding consequences e 1 of T 1 . Various models of approximate explanation and approximate reduction have been introduced to handle these situations. An important special case is the limiting correspondence relation: theory T 2 approaches theory T 1 (or the observational consequences of T 2 approach those of T 1 ) when some parameter in its laws approaches a limit value (e.g., theory of relativity approaches classical mechanics when the velocity of light c grows without limit). Here T 2 is said to be a concretization of the idealized theory T 1 (Nowak 1980; Nowakowa and Nowak 2000). However, these models do not automatically guarantee that the step from an old theory to a new one is progressive. For example, classical mechanics can be related by the correspondence condition to an infinite number of alternative and mutually incompatible theories, and some additional criteria are needed to pick out the best among them.

Kuhn's (1962) strategy was to avoid the notion of truth and to understand science as a problem-solving activity. Paradigm-based normal science is cumulative in terms of the problems solved, and even paradigm-changes or revolutions are progressive in the sense that “a relatively large part” of the problem-solving capacity of the old theory is preserved in the new paradigm. But, as Kuhn argued, it may happen that some problems solved by the old theory are no longer relevant or meaningful for the new theory. These cases are called “Kuhn-losses.” A more systematic account of these ideas is given by Laudan (1977): the problem-solving effectiveness of a theory is defined by the number and importance of solved empirical problems minus the number and importance of the anomalies and conceptual problems that the theory generates. Here the concept of anomaly refers to a problem that a theory fails to solve, but is solved by some of its rivals. For Laudan the solution of a problem by a theory T means that the “statement of the problem” is deduced from T . A good theory is thus empirically adequate, strong in its empirical content, and—Laudan adds—avoids conceptual problems.

One difficulty for the problem-solving account is to find a proper framework for identifying and counting problems (Rescher 1984; Kleiner 1993). When Newton's mechanics is applied to determine the orbit of the planet Mars, this can be counted as one problem. But, given an initial position of Mars, the same theory entails a solution to an infinite number of questions concerning the position of Mars at time t . Perhaps the most important philosophical issue is whether one may consistently hold that the notion of problem-solving may be entirely divorced from truth and falsity: the realist may admit that science is a problem-solving activity, if this means the attempt to find true solutions to predictive and explanatory questions (Niiniluoto 1984).

A different view of problem-solving is involved in those theories which discuss problems of decision and action . A radical pragmatist view treats science as a systematic method of solving such decision problems relative to various kinds of practical utilities. According to the view called behavioralism by the statistician LJ. Savage, science does not produce knowledge, but rather recommendations for actions: to accept a hypothesis is always a decision to act as if that hypothesis were true. Progress in science can then be measured by the achievement of the practical utilities of the decision maker. An alternative methodological version of pragmatism is defended by Rescher (1977) who accepts the realist view of theories with some qualifications, but argues that the progress of science has to be understood as “the increasing success of applications in problem-solving and control.” In this view, the notion of scientific progress is in effect reduced to science-based technological progress.

3.3 Explanatory Power, Unification, and Simplicity

Already the ancient philosophers regarded explanation as an important function of science. The status of explanatory theories was interpreted either in an instrumentalist or realist way: Plato's school started the tradition of “saving the appearances” in astronomy, while Aristotle took theories to be necessary truths. Both parties can take explanatory power to be a criterion of a good theory, as shown by van Fraassen's (1980) constructive empiricism and Wilfrid Sellars' scientific realism (Pitt 1981; Tuomela 1984). When it is added that a good theory should also yield true empirical predictions, the notions of explanatory and predictive power can be combined within the notion of systematic power (Hempel 1965). If the demand of systematic power simply means that a theory has many true deductive consequences in the observational language, this concept is essentially equivalent to the notion of empirical success and empirical problem-solving ability discussed in Section 3.2, but normally explanation is taken to include additional conditions besides mere deduction. Inductive systematization should also be taken into account (Hempel 1965; Niiniluoto and Tuomela 1973).

One important idea regarding systematization is that a good theory should unify empirical data and laws from different domains (Kitcher 1993). For Whewell, the paradigm case of such “consilience” was the successful unification of Kepler's laws and Galileo's laws by means of Newton's theory.

If theories are underdetermined by observational data, then one is often advised to choose the simplest theory compatible with the evidence (Foster and Martin 1966). Simplicity may be an aesthetic criterion of theory choice, but it may also have a cognitive function in helping us in our attempt to understand the world in an “economical” way. Ernst Mach's notion of the economy of thought is related to the demand of manageability , which is important especially in the engineering sciences and other applied sciences: for example, a mathematical equation can be made “simpler” by suitable approximations, so that it can be solved by a computer. Simplicity has also been related to the notion of systematic or unifying power. This is clear in Eino Kaila's concept of relative simplicity , defined as the ratio between the explanatory power and the structural complexity of a theory (cf. Niiniluoto 1980, 1999). According to this conception, progress can be achieved by finding structurally simpler explanations of the same data, or by increasing the scope of explanations without making them more complex. Laudan's formula of solved empirical problems minus generated conceptual problems is a variation of the same idea.

3.4 Truth and Information

Realist theories of scientific progress take truth to be an important goal of inquiry. This view is built into the classical definition of knowledge as justified true belief: if science is a knowledge-seeking activity, then it is also a truth-seeking activity. However, truth cannot be the only relevant epistemic utility of inquiry. This is shown in a clear way by the cognitive decision theory (Levi 1967; Niiniluoto 1987).

Let us denote by B = { h 1 , …, h n } a set of mutually exclusive and jointly exhaustive hypotheses. Here the hypotheses in B may be the most informative descriptions of alternative states of affairs or possible worlds within a conceptual framework L . For example, they may be complete theories expressible in a finite first-order language. If L is interpreted on a domain U , so that each sentence of L has a truth value (true or false), it follows that there is one and only one true hypothesis (say h *) in B . Our cognitive problem is to identify the target h * in B . The elements h i of B are the (potential) complete answers to the problem. The set D ( B ) of partial answers consists of all non-empty disjunctions of complete answers. The trivial partial answer in D ( B ), corresponding to ‘I don't know’, is represented by a tautology, i.e., the disjunction of all complete answers.

For any g in D ( B ), we let u ( g , h j ) be the epistemic utility of accepting g if h j is true. We also assume that a rational probability measure P is associated with language L , so that each h j can be assigned with its epistemic probability P ( h j / e ) given evidence e . Then the best hypothesis in D ( B ) is the one g which maximizes the expected epistemic utility

(1) U ( g / e ) = n ∑ i =1 P ( h j / e ) u ( g , h j )

For comparative purposes, we may say that one hypothesis is better than another if it has a higher expexted utility than the other by formula (1).

If truth is the only relevant epistemic utility, all true answers are equally good and all false answers are equally bad. Then we may take u ( g , h j ) simply to be the truth value of g relative to h j :

u ( g , h j ) = 1 if h j is in g   = 0 otherwise.

Hence, u ( g , h *) is the real truth value tv ( g ) of g relative to the domain U . It follows from (1) that the expected utility U ( g / e ) equals the posterior probability P ( g / e ) of g on e . In this sense, we may say that posterior probability equals expected truth value. The rule of maximizing expected utility leads now to an extremely conservative policy: the best hypotheses g on e are those that satisfy P ( g / e ) = 1, i.e., are completely certain on e (e.g. e itself and tautologies). On this account, if we are not certain of the truth, then it is always progressive to change an uncertain answer to a logically weaker one.

The argument against using high probability as a criterion of theory choice was made already by Popper in 1934 (see Popper 1959). He proposed that good theories should be bold or improbable. This idea has been made precise in the theory of semantic information.

Levi (1967) measures the information content I ( g ) of a partial answer g in D ( B ) by the number of complete answers it excludes. With a suitable normalization, I ( g ) = 1 if and only if g is one of the complete answers h j in B , and I ( g ) = 0 for a tautology. If we now choose u ( g , h j ) = I ( g ), then U ( g / e ) = I ( g ), so that all the complete answers in B have the same maximal expected utility 1. This measure favors strong hypotheses, but it is unable to discriminate between the strongest ones. For example, the step from a false complete answer to the true one does not count as progress. Therefore, information cannot be the only relevant epistemic utility.

Another measure of information content is cont ( g ) = 1 − P ( g ) (Hintikka 1968). If we choose u ( g , h j ) = cont ( g ), then the expected utility U ( g / e ) = 1 − P ( g ) is maximized by a contradiction, as the probability of a contradictory sentence is zero. Any false theory can be improved by adding new falsities to it. Again we see that information content alone does not give a good definition of scientific progress. The same remark can be made about explanatory and systematic power.

Levi's (1967) proposal for epistemic utility is the weighted combination of the truth value tv ( g ) of g and the information content I ( g ) of g :

(2) a I ( g ) + (1 − a ) tv ( g ),

where 0 < a < 1/2 is an “index of boldness,” indicating how much the scientist is willing to risk error, or to “gamble with truth,” in his attempt to be relieved from agnosticism. The expected epistemic utility of g is then

(3) a I ( g ) + (1 − a ) P ( g / e ).

A comparative notion of progress ‘ g 1 is better than g 2 ’ could be defined by requiring that both I ( g 1 ) > I ( g 2 ) and P ( g 1 / e ) > P ( g 2 / e ), but most hypotheses would be incomparable by this requirement. By using the weight a , formula (3) expresses a balance between two mutually conflicting goals of inquiry. It has the virtue that all partial answers g in D ( B ) are comparable with each other: g is better than g ′ if and only if the value of (3) is larger for g than for g ′.

If epistemic utility is defined by information content cont(g) in a truth-dependent way, so that

U ( g , e ) = cont ( g ) if g is true   = − cont (¬ g ) if g is false,

(i,e., in accepting hypothesis g , we gain the content of g if g is true, but we lose the content of the true hypothesis ¬ g if g is false), then the expected utility U ( g / e ) is equal to

(4) P ( g / e ) − P ( g )

This measure combines the criteria of boldness (small prior probability P ( g )) and high posterior probability P ( g / e ). Similar results can be obtained if cont ( g ) is replaced by Hempel's (1965) measure of systematic power syst ( g , e ) = P (¬ g /¬ e ).

For Levi, the best hypothesis in D ( B ) is the complete true answer. But his utility assignment also makes assumptions that may seem problematic: all false hypotheses (even those that make a very small error) are worse than all truths (even the uninformative tautology); all false complete answers have the same utility (see, however, the modified definition in Levi, 1980); among false hypotheses utility covaries with logical strength. These features are motivated by Levi's project of using epistemic utility as a basis of acceptance rules. But if such utilities are used for ordering rival theories, then the theory of truthlikeness suggests other kinds of principles.

3.5 Truthlikeness

Popper's notion of truthlikeness is also a combination of truth and information (Popper 1963, 1972). For him, verisimilitude represents the idea of “approaching comprehensive truth.” Popper's explication used the cumulative idea that the more truthlike theory should have (in the sense of set-theoretical inclusion) more true consequences and less false consequences, but it turned that this comparison is not applicable to pairs of false theories. An alternative method of defining verisimilitude, initiated in 1974 by Pavel Tichy and Risto Hilpinen, relies essentially on the concept of similarity (Oddie 1986; Niiniluoto 1987).

In the similarity approach, as developed in Niiniluoto (1987), closeness to the truth is explicated “locally” by means of the distances of partial answers g in D ( B ) to the target h * in a cognitive problem B . For this purpose, we need a function d which expresses the distance d ( h i , h j ) = d ij between two arbitrary elements of B . By normalization, we may choose 0 ≤ d ij ≤ 1. The choice of d depends on the cognitive problem B , and makes use of the metric structure of B (e.g., if B is a subspace of the real numbers ℜ) or the syntactic similarity between the statements in B . Then, for a partial answer g , we let D min ( h i , g ) be the minimum distance of the disjuncts in g from h i , and D sum ( h i , g ) the normalized sum of the distances of the disjuncts of g from h i . Then D min ( h i , g ) tells how close to h i hypothesis g is, so that the degree of approximate truth of g (relative to the target h *) is 1 − D min ( h *, g ). On the other hand, D sum ( h i , g ) includes a penalty for all the mistakes that g allows relative to h i . The mini-sum measure

(5) D ms ( h i , g ) = a D min ( h i , g ) + bD sum ( h i , g ),

where a > 0 and b > 0, combines these two aspects. Then the degree of truthlikeness of g is

(6) Tr ( g , h *) = 1 − D ms ( h *, g ).

Thus, parameter a indicates our cognitive interest in hitting close to the truth, and parameter b indicates our interest in excluding falsities that are distant from the truth. In many applications, choosing a to be equal to 2 b gives intuitively reasonable results.

If the distance function d on B is trivial, i.e., d ij = 1 if and only if i = j , and otherwise 0, then Tr ( g , h *) reduces to the variant (2) of Levi's definition of epistemic utility.

Obviously Tr ( g , h *) takes its maximum value 1 if and only if g is equivalent to h *. If g is a tautology, i.e., the disjunction of all elements h i of B , then Tr ( g , h *) = 1 − b . If Tr ( g , h *) < 1 − b , g is misleading in the strong sense that its cognitive value is smaller than that of complete ignorance.

When h * is unknown, the degree of truthlikeness (6) cannot be calculated. But the expected degree of verisimilitude of a partial answer g given evidence e is given by

(7) ver ( g / e ) = n ∑ i =1 P ( h i / e ) Tr ( g , h i )

If evidence e entails some h j in B , or makes h j completely certain (i.e., P ( h j / e ) = 1), then ver ( g / e ) reduces to Tr ( g , h j ). If all the complete answers h i in B are equally probable on e , then ver ( h i / e ) is also constant for all h i .

The truthlikeness function Tr allows us to define an absolute concept of real progress :

(RP) Step from g to g ′ is progressive if and only if Tr ( g , h *) < Tr ( g ′, h *),

and the expected truthlikeness function ver gives the relative concept of estimated progress :

(EP) Step from g to g ′ seems progressive on evidence e if and only if ver ( g / e ) < ver ( g ′/ e ).

(Cf. Niiniluoto 1980.) According to definition RP, it is meaningful to say that one theory g ′ satisfies better the cognitive goal of answering problem B than another theory g . This is an absolute standard of scientific progress in the sense of Section 2.5. Definition EP shows how claims of progress can be fallibly evaluated on the basis of evidence: if ver ( g / e ) < ver ( g ′/ e ), it is rational to claim on evidence e that the step from g to g ′ in fact is progressive. This claim may of course be mistaken, since estimation of progress is relative to two factors: the available evidence e and the probability measure P employed in the definition of ver . Both evidence e and the epistemic probabilities P ( h i / e ) may mislead us. In this sense, the problem of estimating verisimilitude is as difficult as the problem of induction.

In Section 3.5., we made a distinction between real and estimated progress in terms of the truthlikeness measures. A similar distinction can be made in connection with measures of empirical success. For example, one may distinguish two notions of the problem-solving ability of a theory: the number of problems solved so far , and the number of solvable problems. Real progress could be defined by the latter, while the former gives us an estimate of progress.

The scientific realist may continue this line of thought by arguing that all measures of empirical success in fact are at best indicators of real cognitive progress, measured in terms of truth or truthlikeness. For example, if T explains e , then it can be shown that e also confirms T , or increases the probability of T . A similar reasoning can be employed to give the so-called “ultimate argument” for scientific realism: theoretical realism is the only assumption that does not make the empirical success of science a miracle (Putnam, 1978; Psillos 1999; Niiniluoto 1999; cf. criticism in Laudan 1984b). This means that the best explanation of the empirical progress of science is the hypothesis that science is also progressive on the level of theories.

The thesis that science is progressive is an overall claim about scientific activities. It does not imply that each particular step in science has in fact been progressive: individual scientists make mistakes, and even the scientific community is fallible in its collective judgments. For this reason, we should not propose such a definition that the thesis about the progressive nature of science becomes a tautology or an analytic truth. This undesirable consequence follows if we define truth as the limit of scientific inquiry (this is sometimes called the consensus theory of truth), as then it is a mere tautology that the limit of scientific research is the truth (Laudan 1984a). But this “trivialization of the self-corrective thesis” cannot be attributed to Peirce who realized that truth and the limit of inquiry coincide at best with probability one (Niiniluoto 1980). The notion of truthlikeness allows us to make sense of the claim that science converges towards the truth. But the characterization of progress as increasing truthlikeness, given in Section 3.5, does not presuppose “teleological metaphysics” (Stegmüller 1976), “convergent realism” (Laudan 1984), or “scientific eschatology” (Moulines 2000), as it does not rely on any assumption about the future behavior of science.

The claim about scientific progress can still be questioned by the theses that observations and ontologies are relative to theories. If this is true, the comparison of rival theories appears to be impossible on cognitive or rational grounds. Kuhn (1962) compared paradigm-changes to Gestalt switches (Dilworth 1981). Feyerabend (1984) concluded from his methodological anarchism that the development of science and art resemble each other.

Hanson, Popper, Kuhn, and Feyerabend agreed that all observation is theory-laden , so that there is no theory-neutral observational language. Accounts of reduction and progress, which take for granted the preservation of some observational statements within theory-change, thus run into troubles. Even though Laudan's account of progress allows Kuhn-losses, it can be argued that the comparison of the problem-solving capacity of two rival theories presupposes some kind of correlation or translation between the statements of these theories (Pearce 1987). Various replies have been proposed to this issue. One is the movement from language to structures (Stegmüller 1976; Moulines 2000), but it turns out that a reduction on the level structures already guarantees commensurability, since it induces a translation between conceptual frameworks (Pearce 1987). Another has been the point that an evidence statement e may happen to be neutral with respect to rival theories T 1 and T 2 , even though it is laden with some other theories. The realist may also point that the theory-ladenness of observations concerns at most the estimation of progress (EP), but the definition of real progress (RP) as increasing truthlikeness does not mention the notion of observation at all.

Even though Popper accepted the theory-ladenness of observations, he rejected the more general thesis about incommensurability as “the myth of the framework” (Lakatos and Musgrave 1970). Popper insisted that the growth of knowledge is always revolutionary in the sense that the new theory contradicts the old one by correcting it, but there is still continuity in theory-change, as the new theory should explain why the old theory was successful to some extent. Feyerabend tried to claim that successive theories are both inconsistent and incommensurable with each other, but this combination makes little sense. Kuhn argued against the possibility of finding complete translations between the languages of rival theories, but in his later work he admitted the possibility that a scientist may learn different theoretical languages (Hoyningen-Huene 1993). Kuhn kept insisting that there is “no theory-independent way to reconstruct phrases like ‘really there’,” i.e., each theory has its own ontology. Convergence to the truth seems to be impossible, if ontologies change with theories. The same idea has been formulated by Putnam (1978) and Laudan (1984a) in the so-called “pessimistic meta-induction”: as many past theories in science have turned out to be non-referring, there is all reason to expect that even the future theories fail to refer—and thus also fail to be approximately true or truthlike.

The difficulties for realism seem to be reinforced by the observation that measures of truthlikeness are relative to languages. The choice of conceptual frameworks cannot be decided by means of the notion of truthlikeness, but needs additional criteria. In defense of the truthlikeness approach, one may point to the fact that the comparison of two theories is relevant only in those cases where they are considered (perhaps via a suitable translation) as rival answers to the same cognitive problem. It is interesting to compare Newton's and Einstein's theories for their truthlikeness, but not Newton's and Darwin's theories.

Another line is to appeal to theories of reference in order to show that rival theories can after all be regarded as speaking about the same entities (Psillos 1999). For example, Thompson, Bohr, and later physicists are talking about the same electrons, even though their theories of the electron differ from each other. This is not possible on the standard descriptive theory of reference: a theory T can only refer to entities about which it gives a true description. Kuhn's and Feyerabend's meaning holism, with devastating consequences for realism, presupposes this account of reference. A similar argument is used by Moulines (2000), who denies that progress could be understood as “knowing more about the same,” but his own structuralist reconstruction of progress with “partial incommensurability” assumes that rival theories share some intended applications. Causal theories of reference allow that reference is preserved even within changes of theories (Kitcher 1993). The same result is obtained if the descriptive account is modified by introducing a Principle of Charity (Putnam 1975; Smith 1981; Niiniluoto 1999): a theory refers to those entities about which it gives the most truthlike description. This makes it possible that even false theories are referring. Moreover, there can be reference invariance between two successive theories, even though both of them are false; progress means then that the latter theory gives a more truthlike description about their common domain than the old theory.

Does this mean that, by choosing to be charitable, we can simply decide that some theory sequences are progressive? The answer is negative, since charitable reference fixing is not arbitrary: the relevant degrees of truthlikeness depend on the relations between theories and reality.

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5 Scientific Progress

Chapter 5: scientific progress.

In previous chapters we’ve established that the theories within mosaics change through time. We have also made the case that these changes occur in a regular, law-like fashion. Recognizing that a mosaic’s theories change through time, and understanding how they change, is important. But what makes us think that our empirical theories succeed in describing the world? Yes, we can make a claim that they change in a law-governed fashion, i.e. that theories only become accepted when they meet the expectations of the respective community. But does that mean that our accepted physical, chemical, biological, psychological, sociological, or economic theories actually manage to tell us anything about the processes, entities, and relations they attempt to describe? In other words, we know that the process of scientific change exhibits certain general patterns of change, but does that necessarily mean that this law-governed process of changes in theories and methods actually takes us closer to the true description of the world as it really is?

The position of fallibilism that we have established in chapter 2 suggests that all empirical theories are – at best – approximations. Yet, it doesn’t necessarily follow from this that our empirical theories actually succeed in approximating the world they attempt to describe. To begin this chapter, we will tackle the following question:

Do our best theories correctly describe the mind-independent external world?

Before we go any further, it’s important to keep in mind that we are committed to the philosophical viewpoint of fallibilism . As such, whenever we talk about an empirical theory’s correctness , success , or truth , those theories are always subject to the problems of sensations, induction, and theory-ladenness, and are therefore not absolutely certain, not absolutely true. So, the question is not whether our empirical theories provide absolutely correct descriptions of the world; it is nowadays accepted that they do not . The question is whether we can claim that our best theories get at least something correct, i.e. whether they succeed as approximate descriptions of the world as it really is.

Scientific Realism vs. Instrumentalism

Many theories of empirical science attempt to explain easily observable facts, events, processes, relations, etc. Let’s take the free-fall of a coffee cup as an example. We can easily observe a coffee cup fall from a table to the ground, i.e. the fall itself is an observable process. For instance, we can measure the time it took the coffee cup to hit the ground and formulate a proposition that describes the results of our observation: “it takes 0.5 seconds for the cup to hit the ground”. But what about explaining why the coffee cup falls from the table to the ground? To describe the underlying mechanism which produces the fall, we nowadays cite the theory of general relativity. The explanation provided by general relativity is along these lines: the coffee cup falls to the ground because the Earth’s mass bends the space-time around it in such a way that the inertial motion of the cup is directed towards the ground. While a cup falling through the air is easily observable, the bent space-time which general relativity invokes is not. Similarly, many scientific theories postulate the existence of entities, structures, or processes that are too small (like quarks, atoms, or microbes), too large (like clusters of galaxies), too fast (like the motion of photons), or too slow (like the process of biological evolution by natural selection) to be directly detected with the human senses. In addition, some of the entities, structures, or processes invoked by our scientific theories are such that cannot be directly observed even in principle (like space-time itself). Any process, structure, or entity that can be perceived without any technological help (“with the naked eye”) is typically referred to as observable . In contrast, any process, structure, or entity that can’t be observed with the naked eye is referred to as unobservable .

Since many – if not most – empirical scientific theories invoke unobservables to help explain their objects, this makes our initial question a little bit more interesting. When we ask whether our theories correctly describe the external world, we are actually asking the more specific question:

Do our scientific theories correctly describe both observables and unobservables ?

Generally speaking, scientists and philosophers of science today accept that our theories succeed in correctly describing observables, even as fallibilists. When we’ve dropped that coffee cup for the 400 th time and still clock its airtime at 0.5 seconds, we’ll consider the claim “it takes 0.5 seconds for the cup to hit the ground” confirmed and correct. But how should we understand claims concerning unobservables, like invoking the notion of bent space-time to explain the fall of the coffee cup? Can we legitimately make any claims about unobservable processes, entities, or relations? Thus, the question of interest here is that concerning unobservables :

Do our scientific theories correctly describe unobservables ?

There are many philosophers who are relatively pessimistic when it comes to our ability to make legitimate claims about the unobservable entities, structures, or processes posited by our scientific theories. Those who hold this position would answer “no” to the question of whether scientific theories correctly describe unobservables. This is the position of anti-realism . While anti-realists don’t deny that our theories often correctly describe observables , they do deny that we can make any legitimate claims about the reality of unobservable entities, processes, or relations invoked by our scientific theories. For instance, according to an anti-realist, we are not in a position to say that there is such a thing as bent space-time:

The position of anti-realism is also often called instrumentalism because it treats scientific theories generally – and the parts of those theories that make claims about unobservables specifically – merely as tools or instruments for calculating, predicting, or intervening in the world of observables. Thus, according to instrumentalists, the notion of bent space-time is merely a useful mathematical tool that allows us to calculate and predict how the locations of observable objects change through time; we can’t make any legitimate claims concerning the reality of that bent space-time. In other words, while instrumentalism holds that theories invoking unobservables often yield practical results, those same theories might not actually be succeeding in describing genuine features of the world.

But there are also those who are optimistic when it comes to our ability to know or describe unobservables posited by our scientific theories. This position is called scientific realism . Those who hold this position would answer “yes” to the question of whether scientific theories correctly describe unobservables. For them, unobservables like quarks, bosons, natural selection, and space-time are not merely useful instruments for making predictions of observable phenomena, but denote entities, processes, and relations that likely exist in the world.

It is important to note that the question separating scientific realists from instrumentalists doesn’t concern the existence of the external mind-independent world. The question of whether our world of observable phenomena is all that there is or whether there is an external world beyond what is immediately observable is an important question. It is within the domain of metaphysics, a branch of philosophy concerned with the most general features of the world. However, that metaphysical question doesn’t concern us here. Our question is not about the existence of the external mind-independent world, but about the ability or inability of our best scientific theories to tell us anything about the features of that mind-independent external world. Thus, an instrumentalist doesn’t necessarily deny the existence of a reality beyond the world of observable phenomena. Her claim is different: that even our best scientific theories fail to reveal anything about the world as it really is.

To help differentiate scientific realism from instrumentalism more clearly, let’s use the distinction between acceptance and use we introduced in chapter 3. Recall that it is one thing to accept a theory as the best available description of its respective domain, and it’s another thing to use it in practical applications. While a community can use any number of competing theories in practice (like different tools in a toolbox), it normally accepts only one of the competing theories as the best available description of its object .

Now, instrumentalists and scientific realists don’t deny that communities do in fact often accept theories and use them in practice; the existence of these epistemic stances in the actual practice of science is beyond question. What separates instrumentalists and scientific realists is the question of the legitimacy of those stances. The question is not whether scientists have historically accepted or used their theories – it is clear that they have – but whether it is justifiable to do so. Since we are dealing with two different stances ( acceptance and use ) concerning two different types of claims (about observables and unobservables ), there are four distinct questions at play here:

Can we legitimately use a theory about observables ?

Can we legitimately accept a theory as describing observables ?

Can we legitimately use a theory about unobservables ?

Can we legitimately accept a theory as describing unobservables ?

As far as the practical use of theories is concerned, there is no disagreement between instrumentalists and realists: both parties agree that any type of theory can legitimately become useful. This goes both for theories about observable phenomena and theories about unobservables. Instrumentalists and realists also agree that we can legitimately accept theories about observables. Where the two parties differ, however, is in their attitude concerning the legitimacy of accepting theories about unobservables . The question that separates the two parties is whether we can legitimately accept any theories about unobservables . In other words, of the above four questions, realists and instrumentalists only differ in their answer to one of them. This can be summarized in the following table:

For an instrumentalist, theories concerning unobservables can sometimes be legitimately used in practice (e.g. bridge building, telescope construction, policy making) but never legitimately accepted. For a realist, theories concerning unobservables can sometimes be legitimately used, sometimes legitimately accepted, and sometimes both legitimately used and accepted. The following table summarizes the difference between the two conceptions:

Now that we’ve established the basic philosophical difference between instrumentalism and realism, let’s consider some historical examples to help illustrate these contemporary distinctions.

For centuries, astronomers accepted Ptolemy’s model of a geocentric universe, including the Ptolemaic theory of planetary motion. First, recall that for Ptolemy the observable paths of the planets, including their retrograde motion, were produced by a combination of epicycles and deferents. Here is the diagram we considered in chapter 3:

In addition to being accepted, this epicycle-on-deferent theory and its corresponding mathematics was used for centuries to predict the position of the planets with remarkable accuracy. The tables of planetary positions composed by means of that theory (the so-called ephemerides ) would then be used in astrology, medicine, navigation, agriculture, etc. (See chapter 7 for more detail.)

Now let’s break this example down a little bit. On the one hand, the planets and their retrograde motion are observables : we don’t need telescopes to observe most of the planets as points of light, and over time we can easily track their paths through the night sky with the naked eye. On the other hand, from the perspective of an observer on the Earth, deferents and epicycles are unobservables , as they cannot be observed with the naked eye. Instead, they are the purported mechanism of planetary motion – the true shape of the orbs which underlie the wandering of the planets across the night sky.

So how would we understand Ptolemy’s epicycle-on-deferent theory of planetary motion from the perspectives of instrumentalism and realism? For both instrumentalists and realists, the Ptolemaic account of the meandering paths of the planets through the night sky would be acceptable since these paths are observable. That is, both conceptions agree that, at the time, the Ptolemaic predictions and projections for planets’ locations in the night sky could be legitimately considered the best description of those phenomena. But instrumentalism and realism disagree over whether it was legitimate for medieval astronomers to also accept those parts of Ptolemy’s theory which referred to unobservable deferents and epicycles. According to realism, at the time, Ptolemy’s theory about epicycles and deferents could be legitimately accepted as the best available description of the actual mechanism of planetary motion. In contrast, the instrumentalist would insist that astronomers had to refrain from committing themselves to the reality of epicycles and deferents, and instead had to focus on whether the notions of epicycle and deferent were useful in calculating planetary positions.

Consider a slightly more recent historical example: the standard model of quantum physics. According to the standard model, there are six quarks , six leptons , four gauge bosons and one scalar boson , the recently discovered Higgs boson. Here is a standard depiction of the standard model:

For our purposes, we can skip the specific roles each type of elementary particle plays in this model. For the purposes of our discussion it is important to note that all of these elementary particles are unobservables; due to their minute size, they cannot be observed with the naked eye. In any event, it is a historical fact that this standard model is currently accepted by physicists as the best available description of its domain.

The philosophical question separating instrumentalists and scientific realists is whether it is legitimate to believe that these particles are more than just a useful calculating tool. Both scientific realists and instrumentalists hold that we can legitimately use the standard model to predict what will be observed in a certain experimental setting given such-and-such initial conditions. However, according to instrumentalists, scientists should not accept the reality of these particles, but should consider them only as useful instruments that allow them to calculate and predict the results of observations and experiments. In contrast, scientific realists would claim that we can legitimately accept the standard model as the best available description of the world of elementary particles, i.e. that the standard model is not a mere predicting tool.

Species of Scientific Realism

A historical note is in order here. In the good-old days of infallibilism, most philosophers believed that, in one way or another, we manage to obtain absolute knowledge about the mind-independent external world. Naturally, the list of theories that were considered strictly true changed through time. For instance, in the second half of 18 th century, philosophers would cite Newtonian physics as an exemplar of infallible knowledge, while the theories of the Aristotelian-medieval mosaic would be considered strictly true in the 15 th century. But regardless of which scientific theories were considered absolutely certain, it was generally accepted that such absolute knowledge does exist. In other words, most philosophers accepted that our best scientific theories provide us with a picture of the world just as it really is. This species of scientific realism is known as naïve realism .

However, as we have seen in chapter 2, philosophers have gradually come to appreciate that all empirical theories are, in principle, fallible. Once the transition from infallibilism to fallibilism was completed, the position of naïve realism could no longer be considered viable, i.e. it was no longer possible to argue that our best theories provide us with the exact image of the world as it really is. Instead, the question became:

Do our scientific theories approximate the mind-independent external world (i.e. the world of unobservables)?

Thus, the question that fallibilists ask is not whether our theories succeed in providing an exact picture of the external world – which, as we know, is impossible – but whether our theories at least succeed in approximating the external world. It is this question that nowadays separates scientific realists from instrumentalists. Thus, the version of scientific realism that is available to fallibilists is not naïve realism, but critical realism , which holds that empirical theories can succeed in approximating the world. While critical realists believe that at least some of our best theories manage to provide us with some, albeit fallible, knowledge of the world of unobservables, nowadays there is little agreement among critical realists as to how this approximation is to be understood.

Once the fallibility of our empirical theories became accepted, many critical realists adopted the so-called selective approach. Selective scientific realists attempt to identify those aspects of our fallible theories which can be legitimately accepted as approximately true. On this selective approach, while any empirical theory taken as a whole is strictly speaking false, it may nevertheless have some parts which scientists can legitimately accept as approximately true. Thus, the task of a selective scientific realist is to identify those aspects of our theories which warrant such an epistemic stance. While selective scientific realism has many sub-species, here we will focus on two of the most common varieties – entity realism and structural realism . These two varieties of selective realism differ in their answers to the question which aspects of our theories scientists can legitimately consider approximating reality.

According to entity realism , scientists can be justified in accepting the reality of unobservable entities such as subatomic particles or genes, provided that they are able to manipulate these unobservable entities in such a way as to accurately bring about observable phenomena. For instance, since scientists can accurately predict what exactly will be observed when the putative features of an electron are being manipulated, then they have good reason to accept the reality of that electron. Entity realists hold that it is the scientists’ ability to causally manipulate unobservable entities and produce very precise observable outcomes that justifies their belief that these unobservable entities are real. The key reason why some philosophers find the position of entity realism appealing is that it allows one to continue legitimately accepting the reality of an entity despite of any changes in our knowledge concerning the behaviour of that entity. For example, according to entity realists, we can continue accepting the reality of an electron regardless of any additional knowledge concerning specific features of the electron and its behaviour that we may acquire in the future. Importantly, entity realism takes a selective approach as to which parts of our theories can be legitimately accepted and which parts can only be considered useful.

A different version of the selective approach is offered by structural realism . While structural realists agree with entity realists that scientists can legitimately accept only certain parts of the best scientific theories, they differ drastically in their take on which parts scientists are justified in accepting. According to structural realists, scientists can justifiably accept not the descriptions of unobservable entities provided by our best theories, but only the claims these theories make about unobservable structures , i.e. about certain relations that exist in the world. One key motivation for this view is a historical observation that often our knowledge of certain structural relations is being preserved despite fundamental changes in our views concerning the types of entities that populate the world. Consider, for instance, the law of refraction from optics:

The ratio of the sines of the angles of incidence θ 1 and angle of refraction θ 2 is equivalent to the ratio of phase velocities (v 1 / v 2 ) in the two media:

Setting aside the question of who is to be rightfully credited with the discovery of this law – René Descartes, or the Dutch astronomer Willebrord Snellius, or even the Persian scientist Ibn Sahl – we can safely say that all theories of light accepted since the 17 th century contained a version of this law. This goes for Descartes’ mechanistic optics, Newton’s optics, Fresnel’s wave theory of light, Maxwell’s electrodynamics, as well as contemporary quantum optics. These different optical theories had drastically opposing views on the nature of light: some of these theories understood light as a string of corpuscles (particles), while other theories treated light as a wave-like entity, while yet others considered light as both corpuscular and wave-like. But despite these drastic changes in our understanding of the nature of light, the law of refraction has maintained its state in our mosaic. Thus, according to structural realists, scientists are justified in accepting those claims of our theories which reveal certain underlying structures (relations), while the claims about unobservable entities are to be taken sceptically. In this view, scientists can still find the claims regarding unobservable entities useful , but they are not justified in accepting those claims. All they can legitimately do is to believe that their theories provide acceptable approximations of the underlying structures that produce observable phenomena. This is another example of selective scientific realism.

Upon closer scrutiny, however, both entity realism and structural realism fail to square with the history of science. Let us begin with entity realism, according to which, we are justified in accepting the existence of those unobservable entities which we manage to manipulate. This view assumes that there are claims about certain unobservable entities that maintain their positions in the mosaic despite all the changes in the claims concerning their behaviour and relations with other entities. Unfortunately, a quick glance at the history of science reveals many once-accepted unobservable entities that are no longer accepted. Consider, for instance, the theory of phlogiston accepted by chemists until the late 18 th century. According to this theory, what makes something combustible is the presence of a certain substance, called phlogiston. Thus, firewood burns because it contains phlogiston. When burning, so the story goes, the firewood is being de-phlogisticated while the air around it becomes phlogisticated. In other words, according to this theory, phlogiston moves from the burning substance to the air. What’s important for our discussion is that the existence of phlogiston, an unobservable entity, was accepted by the community of the time. Needless to say, we no longer accept the existence of phlogiston. In fact, the description of combustion we accept nowadays is drastically different. Presently, chemists accept that, in combustion, a substance composed primarily of carbon, hydrogen, and oxygen (the firewood) combines with the oxygen in the air, producing carbon dioxide and water vapor. In short, the chemical entities that we accept nowadays are very different from those that we used to accept in the 18 th century. When we study the history of science, we easily find many cases when a once-accepted entity is no longer accepted. Consider, for example, the four Aristotelian elements, the electric fluids of the 18 th century, or the luminiferous ether of the 19 th century. How then can entity realists argue that scientists are justified in accepting the existence of unobservable entities if our knowledge of these entities is as changeable as our knowledge of structures?

Of course, an entity realist can respond by saying that those entities that we no longer accept were accepted by mistake, i.e. that scientists didn’t really manage to causally manipulate them. But such a response is historically-insensitive, as it assumes that only our contemporary science succeeds in properly manipulating the unobservable entities, while the scientists of the past were mistaken in their beliefs that they managed to manipulate their unobservable entities. In reality, however, such “mistakes” only become apparent when a theory becomes rejected and replaced by a new theory that posits new unobservable entities. Chances are, one day our current unobservable entities will also be replaced by some new unobservables, as has happened repeatedly throughout history. Would entity realists be prepared to admit that our contemporary scientists weren’t really justified in accepting our current unobservable entities, such as quarks, leptons, or bosons, once these entities become replaced by other unobservable entities? Clearly, that would defeat the whole purpose of entity realism, which was to select those parts of our theories that successfully approximate the world.

Structural realism faces a similar objection. Yes, it is true that sometimes our claims concerning structural relations withstand major transitions from one set of unobservable entities to another. However, this is by no means a universal feature of science. We can think of many instances from the history of science where a long-accepted proposition describing a certain relation eventually becomes rejected. Consider the Aristotelian law of violent motion that was accepted throughout the medieval and early modern periods:

If the force (F) is greater than the resistance (R) then the object will move with a velocity (V) proportional to F/R. Otherwise the object won’t move.

Among other things, the law was accepted as the correct explanation of the motion of projectiles, such as that of an arrow shot by an archer. The velocity of the moving arrow was believed to depend on two factors: the force applied by the archer and the resistance of the medium, i.e. the air. The greater the applied force, the greater the velocity; the greater the resistance of the medium, the smaller the velocity. It was accepted that the initial force was due to the mover, i.e. the archer. But what type of force keeps the object, i.e. the arrow, moving after it has lost contact with the initial mover? Generations of medieval and early modern scholars have attempted to answer this question. Yet, it was accepted that any motion – including continued motion – necessarily requires a certain force, be it some external force or internal force stemming from the moving object itself.

Now compare this with the second law of Newtonian physics:

The acceleration (a) of a body is proportional to the net force (F) acting on the body and is inversely proportional to the mass (m) of the body:

How would we parse out the same archer-arrow case by means of this law? First, we notice that the mass of the arrow suddenly becomes important. We also notice that the force is now understood as the net force and it is proportional not to the velocity but to the acceleration , i.e. the change of velocity per unit time. According to the law, the greater the net force, the greater the acceleration, and the greater the mass, the smaller the acceleration. In short, the second law expresses relations that are quite different from those expressed by the Aristotelian law of violent motion. So how can a structural realist claim that our knowledge of relations is normally being preserved in one form or another?

There have been other historical cases where we accepted the existence of new relations and rejected the existence of previously accepted relations. Consider, for example, the transition from Newton’s law of gravity to Einstein’s field equations which took place as a result of the acceptance of general relativity ca. 1920. Here is a typical formulation of the law of gravity:

And here is a typical formulation of Einstein’s field equation:

The two equations are not only very different visually, but they also capture very different relations. The Newtonian law of gravity posits a certain relation between the masses of two objects, the distance between them, and the force of gravity with which they attract each other. In contrast, Einstein’s field equation posits a relation between the space-time curvature expressed by the Einstein tensor ( G μν ) and a specific distribution of matter and energy expressed by the stress-energy tensor ( T μν ). While Newton’s law tells us what the force of gravity will be given objects with certain masses at a certain distance, Einstein’s equation tells us how a certain region of space-time will be curved given a certain arrangement of mass and energy in that region. Saying that the two equations somehow capture the same relation would be an unacceptable stretch.

In short, there are strong historical reasons against both entity realism and structural realism. Both of these versions of selective scientific realism fail to square with the history of science, which shows clearly that both our knowledge of entities and our knowledge of structures have repeatedly changed through time. This is not surprising, since as fallibilists we know that no synthetic proposition is immune to change. Clearly, there is no reason why some of these synthetic propositions – either the ones describing entities or the ones describing relations – should be any different.

In addition, there is a strong theoretical reason against selective scientific realism. It is safe to say that both entity realism and structural realism fail in their attempts due to a fatal flaw implicit in any selective approach. The goal of any selective approach is to provide some criteria – i.e. some method – that would help us separate those parts of our theories that approximate the world from those parts that are at best mere useful instruments. We’ve seen how both entity realism and structural realism attempted to provide their distinct methods for distinguishing acceptable parts of theories from those that are merely useful. The entity realist method of selecting acceptable parts would go along these lines: “the existence of an unobservable entity is acceptable if that entity has been successfully manipulated”. Conversely, the structural realist method can be formulated as “the claim about unobservables is acceptable if it concerns a relation (structure)”. Importantly, these methods were meant to be both universal and transhistorical, i.e. applicable to all fields of science in all historical periods. After our discussions in chapters 3 and 4, it should be clear why any such attempt at identifying transhistorical and universal methods is doomed. We know that methods of science are changeable: what is acceptable to one community at one historical period need not necessarily be acceptable to another community at another historical period. Even the same community can, with time, change its attitude towards a certain relation or entity.

Take, for instance, the idea of quantum entanglement . According to quantum mechanics, sometimes several particles interact in such a way that the state of an individual particle is dependent on the state of the other particles. In such cases, the particles are said to be entangled : the current state of an entangled particle cannot be characterized independently of the other entangled particles. Instead, the state of the whole entangled system is to be characterized collectively. If, for instance, we have a pair of entangled electrons, then the measurement of one electron’s quantum state (e.g. spin, polarization, momentum, position) has an impact on the quantum state of the other electron. Importantly, according to quantum mechanics, entanglement can be nonlocal : particles can be entangled even when they are separated by great distances.

Needless to say, the existence of nonlocal entanglement didn’t become immediately accepted , for it seemingly violated one of the key principles of Einstein’s relativity theory, according to which nothing can move and no information can be transmitted faster than the speed of light. Specifically, it wasn’t clear how a manipulation on one of the entangled particles can possibly affect the state of the other particle far away without transmitting this information faster than the speed of light. This was one of the reasons why the likes of Albert Einstein and Erwin Schrödinger were against accepting the notion of nonlocal entanglement. Thus, for a long time, the idea of entanglement was considered a useful calculating tool. But the reality of entanglement was challenged, i.e. it wasn’t accepted as a real physical phenomenon. It was not until Alain Aspect’s experiments of 1982 that the existence of nonlocal entanglement became accepted. Nowadays, it is accepted by the physics community that subatomic particles can be entangled over large distances.

What this example shows is that the stance towards a certain entity or a relation can change even within a single community. A community may at first be instrumentalist towards an entity or a structure, like quantum entanglement, and then may later become realist about the same entity or structure. By the second law of scientific change, the acceptance or unacceptance of a claim about an entity or a relation by a certain community depends on the respective method employed by that community. Thus, to assume that we as philosophers are in a position to provide transhistorical and universal criteria for evaluating what’s acceptable and what’s merely useful would be not only anachronistic and presumptuous but would also go against the laws of scientific change. Therefore, we have to refrain from drawing any such transhistorical and universal lines between what is acceptable and what is merely useful. In other words, the approach of selective scientific realism is untenable.

This brings us to the position that can be called nonselective scientific realism . According to this view, our best scientific theories do somehow approximate the world and we are justified to accept them as the best available descriptions of their respective domains, but we can never tell which specific parts of our theories are acceptable approximations and which parts are not. Nonselective scientific realism holds that any attempt at differentiating acceptable parts of our theories from merely useful parts is doomed, since all synthetic propositions – both those describing entities and those describing relations/structures – are inevitably fallible and can be replaced in the future.

The following table summarizes the major varieties of scientific realism:

From Realism to Progress

Once we appreciate that the selective approach is not viable, we also understand that we can no longer attempt to draw transhistorical and universal lines between legitimate approximations and mere useful instruments. This is decided by an individual community’s employed method which can change through time. Thus, the question that a realist wants to address is not whether our theories approximate the world – they somehow do – but whether new accepted theories provide better approximations than the previously accepted theories. Thus, the question is that of progress:

Does science actually progress towards truth?

Note that, while there have been many different notions of progress , here we are interested exclusively in progress towards the correct description of the mind-independent external world. Indeed, nobody really questions the existence of technological, i.e. instrumental progress. It is a historical fact that our ability to manipulate different phenomena has constantly increased. After all, we can now produce self-driving cars, smartphones, and fidget spinners – something we weren’t able to do not long ago. In other words, the existence of technological (instrumental, empirical) progress is beyond question. What’s at stake here is whether we also progress in our descriptions of the world as it really is. This is worth explaining.

The question of whether scientific theories progress towards truth is important in many respects. Science, after all, seems to be one of very few fields of human endeavour where we can legitimately speak of progress. The very reason why we have grant agencies funding scientific research is that we assume that our new theories can improve our understanding of the world. The belief that contemporary scientific theories are much better approximations of the world than the theories of the past seems to be implicit in the whole scientific enterprise. In short, the belief that currently accepted theories are better than those of the past is integral to our culture. This is more than can be said about many other fields of human endeavour. For example, can anyone really show that contemporary rock music is better than that of the 1970s and the 1980s? How could we even begin to compare the two? Are contemporary authors better than Jane Austen, Leo Tolstoy, or Marcel Proust? Is contemporary visual art better than that of Da Vinci or Rembrandt? It is nowadays accepted that art manages to produce different forms, i.e. different ways of approaching the subject, but the very notion that one of these forms can be better than others is considered problematic. Yet, when it comes to science, our common attitude these days is that it steadily progresses towards an increasingly better approximation of the world. Our question here is to determine whether that is really the case. What we want to establish is whether it is the case that science gradually approximates the true description of the world, or whether we should concede that science can at best give us different ways of looking at the world, none of which is better or worse.

In science, we often postulate different hypotheses about unobservable entities or relations/structures so that we could explain observable phenomena. For instance, we hypothesized the existence of a certain attractive force that was meant to explain why apples fall down and why planets revolve in ellipses. Similarly, we hypothesized the existence of a certain atomic structure which allowed us to explain many observable chemical phenomena. We hypothesized the existence of evolution through mutation and natural selection to explain the observable features of biological species. In short, we customarily hypothesize and accept different unobservable entities and structures to explain the world of phenomena, i.e. to explain the world as it appears to us in experiments and observations.

In addition, as we have already seen, sometimes scientists change their views on what types of entities and structures populate the world: the entities and relations that were accepted a hundred years ago may or may not still be accepted nowadays. But if our views on unobservable entities and structures that purportedly populate the world change through time, can we really say that we are getting better in our knowledge of the world as it really is? There are two opposing views on this question – the progress thesis and the no-progress thesis . While the champions of the progress thesis believe that science gradually advances in its approximations of the world, the proponents of the no-progress thesis hold that we are not in a position to know whether our scientific theories progress towards truth.

How can we find out which of these opposing parties is right? Naturally, we might be inclined to refer to the laws of scientific change and see what they have to tell us on the subject. Yet, a quick analysis reveals that the laws of scientific change as they currently stand do not really shed light on the issue of scientific progress. Indeed, consider the second law, according to which theories normally become accepted by a community when they somehow meet the acceptance criteria of the community’s employed method. Now recall the definition of acceptance : to accept a theory means to consider it the best available description of whatever it is the theory attempts to describe. Thus, if we ask any community, they will say that their current theories are better approximation of their objects than the theories they accepted in the past. After all, we wouldn’t accept general relativity if we didn’t think that it is better than the Newtonian theory. Any community whatsoever will always believe that they have been getting better in their knowledge of the world. While some communities may also believe that they have already achieved the absolutely true description of a certain object, even these communities will accept that their current theories are better than the theories of the past. In other words, when we look at the process of scientific change from the perspective of any community, the historical sequence of their accepted theories will always appear progressive to them.

Clearly, this approach doesn’t take us too far, since the question wasn’t whether the process of scientific change appears progressive from the perspective of the scientific community, but whether the process is actually progressive. The laws of scientific change tell us that if we were to ask any scientific community, they would subscribe to the notion of progress. Yet, to find out whether we do, in fact, progress towards truth, we need a different approach. In the remainder of this chapter, we will consider the two most famous arguments for and against the progress thesis – the no-miracles argument and the pessimistic meta-induction argument.

As we have already established, nobody denies that science becomes increasingly successful in its ability to manipulate and predict observable phenomena. It is this empirical success of our theories that allows us to predict future events, construct all sorts of useful instruments, and drastically change the world around us. In its ability to accurately predict and manipulate observable phenomena, our 21 st -century science is undeniably head and shoulders above the science of the past. But does this increasing empirical success of our theories mean that we are also getting closer to the true picture of the world as it really is? This is where the progress thesis and no-progress thesis drastically differ.

According to the champions of the progress-thesis, the empirical success of our theories is a result of our ever-improving understanding of the world as it really is. This is because the world of phenomena cannot be altogether divorced from the external world. After all, so the argument goes, the world of phenomena is an effect of the external world upon our senses: what we see, hear, smell, taste, and touch should be somehow connected to how the world really is. Of course, nobody will claim that the world as it is in reality is exactly the way we perceive it – i.e. nobody will champion the view of naïve realism these days – but isn’t it reasonable to suggest that what we perceive depends on the nature of the external world at least to some degree ? Thus, the results of experiments and observations are at least partially affected by things as they really are. But this means that by getting better in our ability to deal with the world of observable phenomena, we are also gradually improving our knowledge of the world of unobservables. In other words, as the overall predictive power of our theories increases, this is, generally speaking, a good indication that our understanding of the world itself also improves. Here is the argument:

The underlying idea is quite simple: if our theories didn’t manage to get at least something right about the world as it really is, then the empirical success of our science would simply be a miracle. Indeed, how else could we explain the unparalleled empirical success of our science if not by the fact that it becomes better and better in its approximations of the external world? Surely, if we could manage to have this much empirical success without ever getting anything right about the external world, that would be a miracle. The only reasonable explanation, say the champions of the progress thesis, is that our approximations of the world also improve, i.e. that we gradually progress towards truth. This is the gist of the famous no-miracles argument for scientific progress.

How would a champion of the no-progress thesis reply to this? One common reply is the so-called pessimistic meta-induction argument . Let us first appreciate, says a champion of the no-progress thesis, a simple historical fact: we have been quite often mistaken in our hypotheses concerning unobservable entities or structures. When we try to hypothesize what unobservable entities or structures populate the world, i.e. when we try to guess the ontology of the world, we often end up accepting entities and structures which we eventually come to reject as wrong. The history of science, so the argument goes, provides several great examples of this. Consider the idea of the four terrestrial elements of earth , water , air , and fire which were an integral part of any Aristotelian-medieval mosaic all the way into the 17 th century. Similarly, recall the idea of phlogiston accepted in the 18 th century. Also recall the idea of the force of gravity acting at a distance between any two objects in the universe, which was accepted until ca. 1920. It is a historical fact, say the proponents of the no-progress thesis, that our knowledge about entities and structures that populate the world has changed through time. We are often wrong in our hypotheses concerning the ontology of the world. As a result, we often reject old ontologies and accept new ones. But if the ontologies of our past theories are normally considered mistaken from later perspectives, how can we ever claim that our theories gradually get better in approximating the world? Shouldn’t we rather be more modest and say that all we know is that we are often mistaken in our hypotheses concerning the unobservable entities and structures, that we often come to reject long-accepted ontologies, and that the ontologies of our current theories could be found, one day, to be equally mistaken? In other words, we should accept that there is no guarantee that our theories improve as approximations of the world as it really is; we are not in a position to claim that science provides increasingly correct descriptions of the world. But that is precisely what the idea of progress is all about! Thus, there is no progress in science. Here is the gist of the argument:

If the long-accepted ontology of four terrestrial elements was eventually considered mistaken, then why should our ontology of quarks, leptons, and bosons be any different? The unobservable entities and structures that our theories postulate come and go, which means that in the future the ontologies of our current theories will most likely be considered mistaken from the perspective of the theories that will come to replace them. This is the substance of what is known as the pessimistic meta-induction argument .

Now, why such a strange label – “pessimistic meta-induction”? In literature, the argument is often portrayed as inductive: because the ontologies of our past theories have been repeatedly rejected, we generalize and predict that the ontologies of the currently accepted theories will also one day be rejected. This is a meta- inductive step, as it concerns not our descriptions of the world, but our descriptions concerning our descriptions, i.e. our meta-theories (e.g. the claim that “ontologies of past theories have been rejected”). As the whole argument questions the ability of our current ontologies to withstand future challenges, it is also clearly pessimistic . It is important to note that while the inductive form of the argument is very popular in the philosophical literature, it can also be formulated as a deductive argument, as we have done above. The main reason why we nowadays believe that our claims about unobservable entities and structures can be rejected in the future is our fallibilism , for which we have several theoretical reasons, such as the problem of sensations, the problem of induction, and the problem of theory-ladenness. Since we accept that all our empirical theories are fallible, we don’t make any exceptions for our claims concerning the ontology of the world – and why should we? So, even though the argument is traditionally labelled as “pessimistic meta-induction”, it can be formulated in such a way as to avoid any direct use of induction. We don’t have to even mention the failure of our past ontologies; our fallibilism alone is sufficient to claim that our current ontologies are also likely doomed.

Regardless of how the argument is formulated, its main message remains the same: we are not in a position to say there is actual progress towards truth. Since the ontologies of past theories are usually considered mistaken from the perspective of future theories, the process of scientific change produces one faulty ontology of unobservable entities and structures after another. What we end up with is essentially a graveyard of rejected ontologies – a series of transitions where one false ontology replaces another false ontology and so on. All that science gives us is different perspectives, different ways of approaching the world, which can be more or less empirically successful, yet none of these can be said to be approximating the world better than others. Thus, all that we can legitimately claim, according to the no-progress thesis, is that science increases its overall predictive power but doesn’t take us closer to the truth.

Does this argument hold water? As opposed to the no-miracles argument, the pessimistic meta-induction argument divorces the empirical success of a theory from its ability to successfully approximate the world. Indeed, the fact that a theory is predictively accurate and can be used in practical applications doesn’t necessarily make its ontology any more truthlike. Consider, for instance, the Ptolemaic geocentric astronomy which was extremely successful in its predictions of planetary positions, but postulated an ontology of eccentrics, equants, epicycles, and deferents, which was considered dubious even in the Middle Ages. In addition, the history of science provides many examples in which several theories, with completely different ontologies, were equally successful in their predictions of observable phenomena. Thus, in the early 17 th century, the Copernican heliocentric theory and the Tychonic geo-heliocentric theory posited distinct ontologies but were almost indistinguishable in their predictions of observable planetary positions. Nowadays, we have a number of different quantum theories – the so-called “interpretations” of quantum mechanics – which make exactly the same predictions but postulate very different ontologies. If theories with completely different ontologies manage to be equally successful in their predictions, then how can we even choose which of these distinct ontologies to accept, let alone argue that one of them is a better approximation of the world? The champions of the no-progress thesis do a great job highlighting this discrepancy between empirical successes and approximating the world. In other words, they point out that, from the mere fact that the world of phenomena is affected by reality, it does not follow that by improving our knowledge of phenomena, we simultaneously improve our knowledge of the world as it really is. Thus, they question the validity of the no-miracles argument.

However, the pessimistic meta-induction argument has a fatal flaw, for it is based on the premise that our past ontologies are considered mistaken from the perspective of future theories. If we are truly fallibilists, then we should be very careful when deeming ontologies of the past as false in the absolute sense. Instead, we should accept that they are not absolutely false, but contain at least some grains of truth, i.e. that they somehow approximate the world, albeit imperfectly. For instance, we eventually came to reject the ontology of four elements, but we don’t think it was absolutely false. Instead, we think it contained some grains of truth, as it clear resembles the contemporary idea of the four states of matter: solid , liquid , gas, and plasma . Similarly, we no longer accept the theory of phlogiston, but saying that its ontology was absolutely wrong would be a stretch; it was an approximation – a pretty bad one to be sure, but an approximation nevertheless. The fallibilist position is not that the old ontologies are strictly false, but that the ontology of general relativity and quantum physics is slightly better than the ontology of classical physics, just as the ontology of classical physics was slightly better than the ontology of Descartes’ natural philosophy, which itself was slightly better than that of Aristotle. Thus, we can’t say we commit ontological mistakes in the absolute sense. The old ontologies are rejected not because they are considered absolutely false, but because we think we have something better. A useful way of thinking of it is as a series of photographs of the same person – from the blurriest to the sharpest. Compared to the sharp photographs, the blurry photographs would be worse approximations of the person’s appearance; yet, importantly, they are all approximations. In short, the premise of ontological “mistakes” doesn’t hold water, and thus the whole argument is unsound.

We commenced this chapter by posing the question of scientific realism: do our best theories correctly describe the mind-independent external world? We have learned that the central point of contention between realists and instrumentalists doesn’t concern our ability to describe what is immediately observable , but our ability to provide trustworthy descriptions of unobservable entities and structures. While both parties agree that theories can be legitimately used in practical applications, scientific realists also believe that we can legitimately accept the claims of our theories about unobservables. We have discussed a number of sub-species of scientific realism. We’ve also seen how selective approaches fail in their attempts to differentiate acceptable parts of our theories from those that are merely useful. Our knowledge about both unobservable entities and unobservable structures changes through time and there is no transhistorical and universal method that would indicate which parts of our theories are to be accepted and which only used. Only the actual methods employed by a given community at a given time can answer that question.

We then suggested that there is a more interesting question to discuss – that of scientific progress. While it is generally agreed that our scientific theories have been enormously successful in dealing with observable phenomena, there is a heated debate on whether scientific theories gradually progress towards ever-improving approximations of the world. We’ve seen that the main argument for the no-progress thesis – the pessimistic meta-induction argument – has a serious flaw. The debate can be summed up in the following table:

While we can tentatively say that the progress thesis seems slightly better supported, we have to be cautious not to jump to the conclusion that the debate is over. Far from it: the question of progress is central to the contemporary philosophy of science and is a subject of continuous discussions.

Introduction to History and Philosophy of Science Copyright © by Barseghyan, Hakob; Overgaard, Nicholas; and Rupik, Gregory is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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National Research Council (US) Committee on Assessing Behavioral and Social Science Research on Aging; Feller I, Stern PC, editors. A Strategy for Assessing Science: Behavioral and Social Research on Aging. Washington (DC): National Academies Press (US); 2007.

A Strategy for Assessing Science: Behavioral and Social Research on Aging.

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4 Progress in Science

This chapter examines theories and empirical findings on the overlapping topics of progress in science and the factors that contribute to scientific discoveries. It also considers the implications of these findings for behavioral and social science research on aging. The chapter first draws on contributions from the history and sociology of science to consider the nature of scientific progress and the paths that lead to realizing the potential scientific and societal outcomes of scientific activity. It considers indicators that might be used to assess progress toward these outcomes. The chapter then examines factors that contribute to scientific discovery, drawing eclectically on the history and sociology of science as well as on theories and findings from organizational behavior, policy analysis, and economics.

• THEORIES OF SCIENTIFIC PROGRESS

The history and sociology of science have produced extensive bodies of scholarship on some of these themes, generating in the process significant ongoing disagreements among scholars (see, e.g., Krige, 1980 ; Cole, 1992 ; Rule, 1997 ; Bowler and Morus, 2005 ). Most of this work focuses on processes and historical events in the physical and life sciences; relatively little of it addresses the social and behavioral sciences (or engineering, for that matter), except possibly subfields of psychology (e.g., Stigler, 1999 ). It is legitimate to ask whether this research even applies to the behavioral and social sciences ( Smelser, 2005 ). 1

We do not attempt an encyclopedic coverage nor a resolution of the debates, past and continuing, on such questions. Rather, we draw on this research to make more explicit the main issues underlying the tasks of prospective assessment of scientific fields for the purpose of setting priorities in federal research agencies, given the uncertain outcomes of research.

The history of science has produced several general theories about how science develops and evolves over long periods of time. A 19th century view is that of Auguste Comte, who argued that there is a hierarchy of the sciences, from the most general (astronomy), followed historically and in other ways by physics, chemistry, biology, and sociology. Sciences atop the hierarchy are characterized as having more highly developed theories; greater use of mathematical language to express ideas; higher levels of consensus on theory, methods, and the significance of problems and contributions to the field; more use of use theory to make verifiable predictions; faster obsolescence of research, to which citations drop off rapidly over time; and relatively fast progress. Sciences at the bottom of the hierarchy are said to exhibit the opposite characteristics ( Cole, 1983 ).

Many adherents to this hierarchical view place the natural sciences toward the top of the hierarchy and the social sciences toward the bottom. 2 In this view, advances in the “higher” sciences, conceived in terms of findings, concepts, methodologies, or technologies that are thought to be fundamental, are held to flow down to the “lower” sciences, while the reverse flow rarely occurs. Although evidence of such a unidirectional flow from donor to borrower disciplines does exist ( Losee, 1995 ), there are counterexamples. Historians and sociologists of science have offered evidence against several of these propositions, and particularly dispute the claimed association of natural science with the top of the hierarchy and social science with the bottom (e.g., Bourdieu, 1988 ; Cetina, 1999 ; Steinmetz, 2005 ). The picture is more complex, as noted below.

By far the best known modern theory of scientific progress is that of Thomas Kuhn (1962) , which focuses on the major innovations that have punctuated the history of science in the past 350 years, associated with such investigators as Copernicus, Galileo, Lavoisier, Darwin, and Einstein. Science, in Kuhn’s view, is usually a problem-solving activity within clear and accepted frameworks of theory and practice, or “paradigms.” Revolutions occur when disparities or anomalies arise between theoretical expectation and research findings that can be resolved only by changing fundamental rules of practice. These changes occur suddenly, Kuhn claims, in a process akin to Gestalt shifts: in a relative instant, the perceived relationships among the parts of a picture shift, and the whole takes on a new meaning. Canonical examples include the Copernican idea that the Earth revolves around the Sun, Darwin’s evolutionary theory, relativity in physics, and the helical model of DNA.

A quite different account is that of John Desmond Bernal (1939) . Inspired by Marxist social science and ideals of planned social progress, Bernal saw basic science progressing most vigorously when it was harnessed to practical efforts to serve humanity’s social and economic needs (material well-being, public health, social justice). Whereas in Kuhn’s view science progressed according to its inner logic, Bernal asserted that intellectual and practical advances could be engineered and managed.

Another tradition of thought, stemming from Derek Price’s (1963) vision of a quantitative “science of science,” has focused less on how innovations arise than on how they spread and how their full potential is exploited by small armies of scientists. Mainly pursued by sociologists of science, this line of analysis has focused on the social structure of research communities (e.g., Hagstrom, 1965 ), competition and cooperation in institutional systems ( Merton, 1965 ; Ben-David, 1971 ), and structured communication in schools of research or “invisible colleges” (e.g., Crane, 1972 ). These efforts, while focused mainly on how science works, may imply principles for stimulating scientific progress and innovation.

There are also evolutionary models of scientific development, such as that of the philosopher David Hull (1988) . Extending Darwin’s account of evolution by variation and selection, Hull argues that scientific concepts evolve in the same way, by social or communal selection of the diverse work of individual scientists. In evolutionary views, science continually produces new ideas, which, like genetic mutations, are essentially unpredictable. Their ability to survive and expand their niches depends on environmental factors.

Bruno Latour and Steve Woolgar (1979) also offer an account of a selective struggle for viability among scientific producers. The vast majority of scientific papers quickly disappear into the maw of the scientific literature. The few that are used by other scientists in their work are the ones that determine the general direction of science progress. In evolutionary and competitive models, a possible function of science managers is to shape the environment that selects for ideas so as to propagate research that is judged to promote the agency’s scientific and societal goals.

Stephen Cole (1992) emphasized a distinction between the frontier and the core of science that seems consistent with an evolutionary view. Work at the frontiers of sciences is characterized by considerable disagreement; as science progresses over time, disagreement decreases as processes such as empirical confirmation and paradigm shift select out certain ideas, while others become part of the received wisdom.

Although the view that different sciences have similar features at their respective frontiers is not unchallenged ( Hicks, 2004 ), we have found the idea of frontier and core science to be useful in examining the extent to which insights from the history and sociology of science, fields that have concentrated their attention predominantly on the natural sciences, also apply to the social and behavioral sciences.

Cole (1983 , 1992) reports considerable evidence to suggest that different fields of science have similar features at the frontier, even if they are very different at the core. In the review of research proposals and journal submissions, an activity at the frontier of knowledge, he concludes that consensus about the quality of research is not systematically higher in the natural sciences than in the social sciences, citing the standard deviations of reviewers’ ratings of proposals to the National Science Foundation, which were twice as large in meteorology as in economics.

In the core, represented by undergraduate textbooks, the situation appears to be quite different. Cole (1983) found that in textbooks published in the 1970s, the median publication date of the references cited in both physics and chemistry was before 1900, while the median publication date in sociology was post-1960. Sociology texts cited an average of about 800 references, while chemistry and physics texts generally cited only about 100. Moreover, a comparison of texts from the 1950s and the 1970s indicated that the material covered, as well as the sources cited, were much the same in both periods in physics and chemistry, whereas in sociology, the newer texts cited only a small proportion of the sources cited in the earlier texts.

Cole interpreted these findings as indicating that core knowledge in physics and chemistry was both more consensual and more stable over time than core knowledge in sociology. Such findings suggest that even though sciences may differ greatly at the core, for the purpose of assessing the progress of science at the frontiers of research fields, insights from the study of the natural sciences are likely to apply to the social sciences as well. They also point to the need to differentiate between “vitality,” as indicated by ferment at the frontier, and scientific progress as indicated by movement of knowledge from the frontier to the core. 3 These findings suggest that the policy challenges for research managers making prospective judgments at the frontiers of research fields are quite similar across the sciences.

• NATURE OF SCIENTIFIC PROGRESS

Scientific progress can be of various types—discoveries of phenomena, theoretical explanations or syntheses, tests of theories or hypotheses, acceptance or rejection of hypotheses or theories by the relevant scientific communities, development of new measurement or analytic techniques, application of general theory to specific theoretical or practical problems, development of technologies or useful interventions to improve human health and well-being from scientific efforts, and so forth. Consequently, many different developments might be taken as indicators, or measures, of progress in science.

Science policy decision makers need to consider the progress and potential of scientific fields in multiple dimensions, accepting that the absence of detectable advance on a particular dimension is not necessarily evidence of failure or poor performance. Drawing on Weinberg’s (1963) classification of internal and external criteria for formulating scientific choices, we make the practical distinction between internally defined types of scientific progress, that is, elements of progress defined by intellectual criteria, and externally defined types of progress, defined in terms of the contributions of science to society. Managers of public investments in science need to be concerned with both.

Scientific Progress Internally Defined

The literatures in the history of science and in science studies include various analyses and typologies of scientific and theoretical progress (e.g., Rule, 1997 ; Camic and Gross, 1998 ; Lamont, 2004 ). This section presents a distillation of insights from this research into a short checklist of major types of scientific progress. The list is intended as a reminder to participants in science policy decisions that assess the progress of scientific fields of the variety of kinds of progress science can make. Recognizing that these broad categories overlap and also that they are interdependent, with each kind of progress having the potential to influence the others, directly or indirectly, the list is intended to simplify a very complex phenomenon to a manageable level.

Types of Scientific Progress

Discovery. Science makes progress when it demonstrates the existence of previously unknown phenomena or relationships among phenomena, or when it discovers that widely shared understandings of phenomena are wrong or incomplete.

Analysis. Science makes progress when it develops concepts, typologies, frameworks of understanding, methods, techniques, or data that make it possible to uncover phenomena or test explanations of them. Thus, knowing where and how to look for discoveries and explanations is an important type of scientific progress. Improved theory, rigorous and replicable methods, measurement techniques, and databases all contribute to analysis.

Explanation. Science makes progress when it discovers regularities in the ways phenomena change over time or finds evidence that supports, rules out, or leads to qualifications of possible explanations of these regularities.

Integration. Science makes progress when it links theories or explanations across different domains or levels of organization. Thus, science progresses when it produces and provides support for theories and explanations that cover broader classes of phenomena or that link understandings emerging from different fields of research or levels of analysis.

Development. Science makes progress when it stimulates additional research in a field or discipline, including research critical of past conclusions, and when it stimulates research outside the original field, including interdisciplinary research and research on previously underresearched questions. It also develops when it attracts new people to work on an important research problem.

Recent scientific activities supported by the Behavioral and Social Research (BSR) Program of the National Institute on Aging (NIA) have yielded progress in the form of scientific advances of most of the above types. We cite only a few examples.

• Discovery: The improving health of elderly populations. An example is analyses of data from Sweden, which has the longest running national data set on longevity, that have shown that the maximum human life span has been increasing since the 1860s, that the rate of increase has accelerated since 1969, and that most of the change is due to improved probabilities of survival of individuals past age 70 ( Wilmoth et al., 2000 ). Parallel trends have been discovered among the elderly in the form of declining physical disability, which declined in the United States from 26 percent of the elderly population in 1982 to 20 percent in 1999 (e.g., Manton and Gu, 2001 ), and declining cognitive impairment (e.g., Freedman et al., 2001 , 2002 ). Such findings together suggest overall improvements in the health of elderly populations in high-income countries.
• Analysis: Longitudinal datasets for understanding processes of aging. The Health and Retirement Study ( Juster and Suzman, 1995 ), a major ongoing longitudinal study that assesses the health and socioeconomic condition of aging Americans in which BSR played a central entrepreneurial role, has provided data that made possible, among other things, some of the discoveries about declining disability already noted. International comparative data sets on health risk factors and health outcomes, such as the Global Burden of Disease dataset ( Ezzati et al., 2002 ), have also made significant scientific progress possible.
• Explanation: Questioning and refining understandings. Several BSR-funded research programs have yielded findings that called into question widely held views about aging processes. Examples include findings that question the beliefs that more health care spending leads to better health outcomes ( Fisher et al., 2003a , 2003b ), that increasing life expectancy implies increased health care expenditures ( Lubitz et al., 2003 ), that unequal access to health care is the main explanation for higher mortality rates among older people of lower socioeconomic status (e.g., Adda et al., 2003 ; Adams et al., 2003 ), and that aging is a purely biological process unaffected by personal or cultural beliefs ( Levy, 2003 ). Other BSR-sponsored research has provided evidence that a previously noted association of depression with heart disease may be explained in part by a process in which negative affect suppresses immune responses ( Rosenkranz et al., 2003 ).
• Integration and development: Creating a biodemography of aging. BSR supported and brought together “demographers, evolutionary theorists, genetic epidemiologists, anthropologists, and biologists from many different scientific taxa” ( National Research Council, 1997 :v) to seek coherent understandings of human longevity that are consistent with knowledge at levels from genes to populations and data from human and nonhuman species). This effort has helped to attract researchers from other fields into longevity studies, add vigor to this research field, and put the field on a broader and firmer interdisciplinary base of knowledge.

Paths to Scientific Progress

Scientific progress is widely recognized as nonlinear. Some new ideas have led to rapid revolutions, while other productive ideas have had lengthy gestation periods or met protracted resistance. Still other new ideas have achieved overly rapid, faddish acceptance followed by quick dismissal. An earlier generation of research in the history and sociology of science documented variety and surprise as characteristics of scientific progress, but it was not followed by broad transdisciplinary studies that developed and tested general theories of scientific progress.

No theory of scientific progress exists, or is on the horizon, that allows prediction of the future development of new scientific ideas or specifies how the different types of scientific progress influence each other—although they clearly are interdependent. Rather, recent studies by historians of science and practicing scientists typically emphasize the uncertainty surrounding which of a series of findings emerging at any point in time will be determinative of the most productive path for future scientific inquiries and indeed of the ways in which these findings will be used. Only in hindsight does the development of various experimental claims and theoretical generalizations appear to have the coherence that creates a sense of a linear, inexorable path.

Science policy seems to be in particular need of improved basic understanding of the apparently uncertain paths of scientific progress as a basis for making wiser, more efficient investments. Without this improved understanding, extensive investments into collecting and analyzing data on scientific outputs are unlikely to provide valid predictors of some of the most important kinds of scientific progress. Political and bureaucratic pressures to plan for steady progress and to assess it with reliable and valid performance indicators will not eliminate the gaps in basic knowledge that must be filled in order to develop such indicators.

Despite the incompleteness of knowledge, the findings of earlier research remain a suggestive and potentially useful resource for practical research managers. They suggest a variety of state-of-knowledge propositions that are consistent with our collective experience on multiple advisory and review panels across several federal science agencies. We consider the following propositions worthy of consideration in discussions of how science managers can best promote scientific progress:

• Scientific discoveries are initially the achievements of individuals or small groups and arise in varied and largely unpredictable ways: the larger and more important the discoveries, the less predictable they would have been.
• The great majority of scientific products have limited impact on their fields; there are only a few major or seminal outputs. Whether or not new scientific ideas or methods become productive research traditions depends on an uncertain process that may extend over considerable time. Sometimes the impacts of research are quite different from those anticipated by the initial research sponsors, the researchers, or the individuals or organizations that first make use of it. For example, the Internet, which was developed as a means of fostering scientific communication among geographically dispersed researchers, has now become a leading channel for entertainment and retail business, among other things.
• Existing procedures for allocating federal research funds are most effective at the mid-level of scientific innovation, where there is consensus among established fields about the importance of questions and the direction and content of emerging questions in those fields.
• The uncertainties of scientific discovery and the difficulties of accurately identifying turning points and sharp departures in scientific inquiry suggest that research managers will do best with a varied portfolio of projects, including both mainstream and discontinuous or exploratory research projects. These uncertainties also suggest that assessment of a program’s investments in research is most appropriately made at the portfolio rather than the project level.
• The portfolio concept also applies to a program’s investments in analysis: in advancing the state of theoretical understanding, tools, and databases. Scientific progress in both the natural and social sciences may either follow or precede the development of new tools (instruments, models, algorithms, databases) that apply to many problems. Contrary to simple models of scientific progress that have theory building as the grounding for empirical research or data collection as the foundation for theory building, the process is not linear or unidirectional. 4 Program investments in theory building, tool development, and data collection can all contribute to scientific progress, but it is very difficult to predict which kinds of investments will be most productive at any given time (see National Research Council, 1986 , 1988 ; Smelser, 1986 ).
• Scientific progress sometimes arises from efforts to solve technological or social problems in environments that combine concerns with basic research and with application. It can also arise in environments insulated from practical concerns. And progress can involve first one kind of setting and then the other (see Stokes, 1997 ).

Interdisciplinarity and Scientific Progress

The claim that the frontiers of science are generally located at the interstices between and intersections among disciplines deserves explicit attention because it is increasingly found in the conclusions and recommendations of national commissions and NRC committees (e.g., National Research Council, 2000b ; Committee on Science, Engineering, and Public Policy, 2004 ) and in statements by national science leaders. 5 Scholarship in the history and sociology of science is consistent with competing views on this claim. A considerable body of recent scholarship has noted that exciting developments often come at the edges of established research fields and at the boundaries between fields ( Dogan and Pahre, 1990 ; Galison, 1999 ; Boix-Mansilla and Gardner, 2003 ; National Research Council, 2005b ). Moreover, interdisciplinary thinking has become more integral to many areas of research because of the need to understand “the inherent complexity of nature and society” and “to solve societal problems” ( National Research Council, 2005b :2).

The idea is that scientific advances are most likely to arise, or are most easily promoted, when scientists from different disciplines are brought together and encouraged to free themselves from disciplinary constraints. A good example to support this idea is the rapid expansion and provocative results of research on the biodemography of aging that followed the 1996 NRC workshop on this topic ( National Research Council, 1997 ). The workshop occasioned serious efforts to develop and integrate related research fields.

To the extent that interdisciplinarity is important to scientific progress and for gaining the potential societal benefits of science, it is important for research managers to create favorable conditions for interdisciplinary contact and collaboration. In fact, for some time BSR has been seeking explicitly to promote both multidisciplinarity and interdisciplinarity ( Suzman, 2004 ). For example, when the Health and Retirement Study was started in 1990, it was explicitly designed to be useful to economists, demographers, epidemiologists, and psychologists, and explicit efforts were made to convince those research communities that the study was not for economists only. BSR has reorganized itself and redefined its areas of interest on issue-oriented, interdisciplinary lines; sought out leading researchers and funded them to do what was expected to be ground-breaking and highly visible research in interdisciplinary fields; supported workshops and studies to define new interdisciplinary fields (e.g., National Research Council, 1997 , 2000a , 2001c ); created broadly based multidisciplinary panels to review proposals in emerging interdisciplinary areas; and funded databases designed to be useful to researchers in multiple disciplines for addressing the same problems, thus creating pressure for communication across disciplines. Some of the results, such as those already mentioned, have been notably productive and potentially useful.

The available studies seem to support the following conclusions about the favorable conditions for interdisciplinary science ( Klein, 1996 ; Rhoten, 2003 ; National Research Council, 2005b ):

• Successful interdisciplinary research requires both disciplinary depth and breadth of interests, visions, and skills, integrated within research groups.
• The success of interdisciplinary research groups depends on institutional commitment and research leadership with clear vision and teambuilding skills.
• Interdisciplinary research requires communication among people from different backgrounds. This may take extra time and require special efforts by researchers to learn the languages of other fields and by team leaders to make sure that all participants both contribute and benefit.
• New modes of organization, new methods of recruitment, and modified reward structures may be necessary in universities and other research organizations to facilitate interdisciplinary interactions.
• Both problem-oriented organization of research organizations and the ability to reorganize as problems change facilitate interdisciplinary research.
• Funding organizations may need to design their proposal and review criteria to encourage interdisciplinary activities.

Several conditions favorable to interdisciplinary collaboration can be affected by the actions of funders of research. For example, science agencies can encourage or require interdisciplinary collaboration in the research they support, support activities that specifically bring researchers together from different disciplines to address a problem of common interest, provide additional funds or time to allow for the development of effective interdisciplinary communication in research groups or communities, and organize their programs internally and externally around interdisciplinary themes. They can ask review panels to consider how well groups and organizations that propose interdisciplinary research provide conditions, such as those above, that are commonly associated with successful interdisciplinary research. And they might also ensure that groups reviewing interdisciplinary proposals include individuals who have successfully led or participated in interdisciplinary projects.

Encouraging interdisciplinary research may have pitfalls, though. It is possible for funds to be offered but for researchers to fail to propose the kinds of interdisciplinary projects that were hoped for. Sometimes interdisciplinary efforts take hold, but they fail to produce important scientific advances or societal benefits. Interdisciplinarity can also become a mantra. If disciplines are at times presented as silos—independent units with no connections among them—interdisciplinary fields may also become silos that happen to straddle two fields. At any point in time, an observer can identify numerous new research trajectories, several involving novel combinations of existing disciplines. Thus, alongside recently institutionalized fields, such as biotechnology, materials science, information sciences, and cognitive (neuro)sciences, are claimants for scientific attention and programmatic support, such as vulnerability sciences, prevention science, and neuroeconomics.

Little is known about how to predict whether a new interdisciplinary field will take off in a productive way. Floral metaphors about budding fields are not always carried to the desired conclusion: many budding fields lack the intellectual or methodological germplasm to do more than pop up and quickly wither. It is at least as difficult to assess the prospects of interdisciplinary fields as of disciplinary ones, and probably more so ( Boix-Mansilla and Gardner, 2003 ; National Research Council, 2005b ). 6

Federal agency science managers can act as entrepreneurs of interdisciplinary fields, so that their expansion from an interest of a small number of researchers into a recognizable cluster of activity may reflect the level of external support from federal agencies and foundations. As a field develops, though, a good indicator of vitality may be the exchange of ideas with other fields and particularly the export of ideas from the new field to other scientific fields or to practical use. But progress in interdisciplinary fields may be hard to determine from recourse to such indicators alone. Fields can be vital without exporting ideas to other fields. Policy analysis, now a well-established academic field of instruction and research, engages researchers from several social science disciplines, but it is a net importer of ideas ( MacRae and Feller, 1998 ; Reuter and Smith-Ready, 2002 ).

It is worth noting that support for interdisciplinary research, although it has unique benefits, may be a relatively high-risk proposition because it requires high-level leadership skills and innovative organizational structures. These characteristics of interdisciplinary research may pose special challenges for research managers in times of tightening budgets, when pressures for risk aversion may conflict with the need to develop innovative approaches to scientific questions and societal needs.

Contributions of Science to Society

In government agencies with practical missions, investments in science are appropriately judged both on internal scientific grounds and on the basis of their contributions to societal objectives. In the case of NIA, these objectives largely concern the improved longevity, health, and well-being of older people ( National Institute on Aging, 2001 ). There are many ways research can contribute to these objectives. For simplicity, we group the societal objectives of science into four broad categories.

Identifying issues. Science can contribute to society by identifying problems relating to the health and well-being of older people that require societal action or sometimes showing that a problem is less serious than previously believed.

Finding solutions. Science can contribute to society by developing ways to address issues or solve problems, for example, by improving prevention or treatment of diseases, improving health care delivery systems, improving access to health care, or developing new products or services that contribute to the longevity, health, or quality of life for older people in America.

Informing choices. Science can contribute to society by providing accurate and compelling information to public officials, health care professionals, and the public and thus promoting better informed choices about life and health by older people and better informed policy decisions affecting them.

Educating the society. Science can contribute to society by producing fundamental knowledge and developing frameworks of understanding that are useful for people facing their own aging and the aging of family members, making decisions in the private sector, and participating as citizens in public policy decisions. Science can also contribute by educating the next generation of scientists.

Research on science utilization, a field that was most vital in the 1970s and that has seen some revival recently, has examined the ways in which scientific results, particularly social science results, may be used, particularly in government decisions (for recent reviews, see Landry et al., 2003 , and Romsdahl, 2005 , for some classic treatments, see Caplan, 1976 ; Weiss, 1977 , 1979 ; Lindblom and Cohen, 1979 ). In terms of the above typology, this research mainly examines the use or nonuse of research results for informing choices by public policy actors. It does not much address the use of results by ordinary citizens, medical practitioners, the mass media, or other users involved in identifying issues and finding solutions, other than policy solutions. The most general classification in this research tradition of the ways social science is used is for enlightenment (i.e., providing a broad conceptual base for decisions) and as instrumental input (e.g., providing specific policy-relevant data). In addition, researchers note that social science results may be used to provide justification or legitimization for decisions already reached or as a justification for postponing decisions ( Weiss, 1979 ; Oh, 1996 ; Romsdahl, 2005 ).

Federal science program managers face the challenges of establishing causal linkages between past research program activities and societal impacts and of projecting societal impacts from current and planned research activities. The challenges are substantial. Even when findings from social and behavioral science research influence policies and practices in the public and private sectors and may therefore be presumed to contribute to human well-being, they are seldom determinative. Indicators exist or could be created for many societal impacts of research ( Cozzens et al., 2002 ; Bozeman and Sarewitz, 2005 ). In addition, evidence that the results of research are used, for example, in government decisions, may be considered an interim indicator of ultimate societal benefit, presuming that the decisions promote people’s well-being.

Limits exist, however, to the ability of a mission agency to translate findings from the research it funds into practice. For the research findings of the National Institutes of Health (NIH) in general and NIA-BSR in particular, contributions to societal or individual well-being require the complementary actions of myriad other actors and organizations in government and the private sector, including state and local governments, insurance companies, nursing homes, physicians’ practices, and individuals. According to Balas and Boren (2000 :66), “studies suggest that it takes an average of 17 years for research evidence to reach clinical practice.” Similarly lengthy processes and circuitous connections link research findings to more enlightened or informed policy making ( Lynn, 1978 ).

A scientific development also may contribute to society in the above ways even if working scientists do not judge it to be a significant contribution on scientific grounds. For example, surveys sponsored by BSR produce data, for example on declining rates of disability among older people, that may be very useful for health care planning without, by themselves, contributing anything more to science than a phenomenon to be explained. Thus, it is appropriate for assessments of research progress to consider separately the effects of research activity on scientific and societal criteria. Scientific activities and outputs may contribute to either of these two kinds of desirable outcomes or to both.

Interpreting Scientific Progress

The extent to which particular scientific results constitute progress in knowledge or contribute to societal well-being is often contested. This is especially the case when scientific findings are uncertain or controversial and when they can be interpreted to support controversial policy choices. Many results in applied behavioral and social science have these characteristics. Disagreements arise over which research questions are important enough to deserve support (that is, over which issues constitute significant social problems), about whether or not a finding resolves a scientific dispute or has unambiguous policy implications, and about many other aspects of the significance of scientific outputs. The more controversial the underlying social issues, the further such disagreements are likely to penetrate into the details of scientific method. Interested parties may use their best rhetorical tools to “frame” science policy issues and may even attempt to exercise power by influencing legislative or administrative decision makers to support or curtail particular lines of research.

These aspects of the social context of science are relevant for the measurement and assessment of scientific progress and its societal impact. They underline the recognition that the meaning of assessments of scientific progress may not follow in any straightforward way from the evidence the assessments produce. Assessing science, no matter how rigorous the methods that may be used, is ultimately a matter of interpretation. The possibility of competing interpretations of evidence is ever-present when using science indicators or applying any other analytic method for measuring the progress and impact of science. In Chapter 5 , we discuss a strategy for assessing science that recognizes this social context while also seeking an appropriate role for indicators and other analytic approaches.

• INDICATORS OF SCIENTIFIC PROGRESS

Research managers understandably want early indicators of scientific progress to inform decisions that must be made before the above types of substantive progress can be definitively shown. Although scientific progress is sometimes demonstrable very quickly, recent histories of science, as noted above, tend to emphasize not only the length of time required for research findings to generate a new consensus but also the uncertainties at the time of discovery regarding what precisely constitutes the nature of the discovery. Time lag and impact may depend on various factors, including the type of research and publication and citation practices in the field. A longitudinal research project can be expected to take longer to yield demonstrable progress than a more conceptual project.

Research Vitality and Scientific Progress

Expressions of scientific interest and intellectual excitement, sometimes referred to as the vitality of a research field, have been suggested as a useful source of early indicators of scientific progress as defined from an internal perspective. Such indications of the development of science are of particular interest to science managers because many of them might potentially be converted into numerical indicators. They include the following:

• Established scientists begin to work in a new field.
• Students are increasingly attracted to a field, as indicated by enrollments in new courses and programs in the field.
• Highly promising junior scientists choose to pursue new concepts, methods, or lines of inquiry.
• The rate of publications in a field increases.
• Citations to publications in the field increase both in number and range across other scientific fields.
• Publications in the new field appear in prominent journals.
• New journals or societies appear.
• Ideas from a field are adopted in other fields.
• Researchers from different preexisting fields collaborate to work on a common set of problems.

Research on the nanoscale is an area that illustrates vitality by such indicators and that is beginning to have an impact on society and the economy. Zucker and Darby (2005 :9) point to the rate of increase in publishing and patenting in nanotechnology since 1986 as being of approximately the same order of magnitude as the “remarkable increase in publishing and patenting that occurred during the first twenty years of the biotechnology revolution…. Since 1990 the growth in nano S&T articles has been remarkable, and now exceeds 2.5 percent of all science and engineering articles.” Major scientific advances are often marked by flurries of research activity, and many observers expect that such indications of research vitality presage major progress in science and applications.

However, research vitality does not necessarily imply future scientific progress. For example, research on cold fusion was vital for a time precisely because most scientists believed it would not lead to progress. In the social sciences, many fields have shown great vitality for a period of time, as indicated by numbers of research papers and citations to the central works, only to decline rapidly in subsequent periods. Rule (1997) , in his study of progress in social science, discusses several examples from sociology, including the grand social theory of Talcott Parsons (1937 , 1964) , ethno-methodology (e.g., Garfinkel, 1967 ), and interaction process analysis (e.g., Bales, 1950 ). Although these fields were vital for a time, in longer retrospect many observers considered them to have been far less important to scientific progress than they had earlier appeared to be. Rule suggests several possible interpretations of this kind of historical trajectory: the fields that looked vital were in fact intellectual dead-ends; research in the fields did make important contributions that were so thoroughly integrated into thinking in the field that they became common knowledge and were no longer commonly cited; and the fields represented short-term intellectual tastes that lost currency with a shift in theoretical concerns. With enough hindsight, it may be possible to decide which interpretation is most correct, although disagreements remain in many specific cases. But the resource allocation challenge for a research manager, given multiple alternative fields whose aggregate claims for support exceed his or her program budget, is to make the correct interpretation of research vitality prospectively: that is, to project whether the field will be judged in hindsight to have produced valuable contributions or to have been no more than a fad or an intellectual dead-end.

Another trajectory of research is problematic for research managers who would use vitality as an indicator of future potential. Some research findings or fields lie dormant for considerable periods without showing signs of vitality, before the seminal contributions gain recognition as major scientific advances. Such findings have been labeled as “premature discoveries” ( Hook, 2002 ) and “sleeping beauties” ( van Raan, 2004b ). These are not findings that are resisted or rejected; rather, they are unappreciated, or their uses or implications are not initially recognized ( Stent, 2002 ). In effect, the contribution of such discoveries to scientific progress or societal needs or both lies dormant until there is some combination of independent discoveries that reveal the potency of the initial discovery. In such cases, vitality indicators focused predominately on the discovery and its related line of research would have been misleading as predictors of long-term scientific importance.

An instructive example of the limitations of vitality measures as early indicators in the social sciences is the intellectual history of John Nash’s approach to game theory—an approach that was recognized, applied, and then dismissed as having limited utility, only to reemerge again as a major construct (the Nash equilibrium), not only in the social and behavioral sciences but also in the natural sciences. As recounted by Nasar (1998) , the years following Nash’s seminal work at RAND in the early 1950s were a period of flagging interest in game theory. Luce and Raiffa’s authoritative overview of the field in 1957 observed: “We have the historical fact that many social scientists have become disillusioned with game theory. Initially there was a naïve band-wagon feeling that game theory solved innumerable problems of sociology and economics, or that, at least it made their solution a practical matter of a few years’ work. This has not turned out to be the case” (quoted in Nasar, 1998 :122). In later retrospect, game theory became widely influential in the social and natural sciences, and Nash was awarded the Nobel Memorial Prize in Economics in 1994.

The complexity of the relationship between the quantity of scientific activity being undertaken during a specific period and the pace of scientific progress (or the rate at which significant discoveries are made) can perhaps be illustrated by analogy to a bicycle race: a group of researchers, analogous to the peloton or pack in a bicycle race, proceeds along together over an extended period until a single individual or a small group attempts a breakaway to win the race. Some breakaways succeed and some fail, but because of the difficulties of making progress by working alone (wind resistance, in the bicycle race analogy), individuals need the cooperation of a group to make progress over the long run and to create the conditions for racing breakaways or scientific breakthroughs. When scientific progress follows this model, fairly intense activity is a necessary but not sufficient condition for progress. Alternatively, the pack may remain closely clustered together for extended periods of time, advancing apace yet with a sense that little progress toward victory, however specified, is being made ( Horan, 1996 ).

In our judgment, these various trajectories of scientific progress imply that quantitative indicators, such as citation counts, require interpretation if they are to be used as part of the prospective assessment of fields . Moreover, the implications of intensified activity in a research area may be quite different depending on the mission goals and the perspective of the agency funding the work. Significant research investments can create activity in a field by encouraging research and supporting communication among communities of researchers. But activity need not imply progress, at least not in terms of some of the indicators listed above, such as the export of ideas to other fields. If research managers conflate the concepts of scientific activity and progress, they can create self-fulfilling prophecies by simply creating scientific activity. These warnings become increasingly important as technical advances in data retrieval and mining make it easier to create and access quantitative indicators of research vitality and as precepts of performance assessment increase pressures on research managers to use quantitative indicators to assess the progress and value of the research they support.

Indicators of Societal Impact

A variety of events may indicate that scientific activities have generated results that are likely to have practical value, even though such value may not (yet) have been realized. Such events might function as leading indicators of the societal value of research. These events typically occur outside research communities. For example:

• Research is cited as the basis for patents that lead to licenses.
• Research is used to justify policies or laws or cited in court opinions.
• Research is prominently discussed in trade publications of groups that might apply it.
• Research is used as a basis for practice or training in medicine or other relevant fields of application.
• Research is cited and discussed in the popular press as having implications for personal decisions or for policy.
• Research attracts investments from other sources, such as philanthropic foundations.

Some of these potential indicators are readily quantifiable, so, like bibliometric indicators, they are attractive means by which science managers can document the value of their programs. But as with quantitative indicators of research vitality, the meaning of quantitative indicators of societal impact is subject to differing interpretations. For example, as studies of science utilization have emphasized, the use of research to justify policy changes may mean that the research has changed policy makers’ thinking or only that it provides legitimation for previously determined positions. Moreover, policy makers have been known to use research to justify a policy when the relevant scientific community is in fact sharply divided about the importance or even the validity of the cited research. Such research nevertheless has societal impact, even if not of the type the scientists may have expected.

• FACTORS THAT CONTRIBUTE TO SCIENTIFIC DISCOVERIES

Historically, analysis of the factors that contribute to scientific discoveries has occurred at least at three different levels of analysis. Macro-level studies have considered the effects of the structures of societies—their philosophical, social, political, religious, cultural, and economic systems ( Hart, 1999 ; Jones, 1988 ; Shapin, 1996 ). Meso-level analyses have examined the effects of functional and structural features of “national research and innovation systems”—for example, the relative apportionment of responsibility and public funding for scientific inquiry among government entities, quasi-independent research institutes, and universities ( Nelson, 1993 ). Microlevel studies have examined the associations between indicators of progress and such factors as the organization of research units and the age of the researcher ( Deutsch et al., 1971 ).

The programmatic latitude of any single federal science unit to adjust its actions to promote scientific discovery relates almost exclusively to micro-level factors. Even then, agency policies, legislation, and higher level executive branch policies may limit an agency’s options. For this reason, we look most closely at micro-level factors. It is nevertheless worth examining the larger structural factors affecting conditions for scientific discovery, if only to understand the implicit assumptrions likely to be accepted by BSR’s advisers and staff.

A convenient means of documenting contemporary thinking on the factors that contribute to scientific advances is to examine the series of “benchmarking” studies of the international standing of U.S. science in the fields of materials science, mathematics, and immunology made by panels of scientists under the auspices of the National Academies’ Committee on Science, Engineering, and Public Policy (COSEPUP). The benchmarking was conducted as a methodological experiment in response to a series of studies that had sought to establish national goals for U.S. science policy and to mesh these goals with the performance reporting requirements of the Government Performance and Results Act ( Committee on Science, Engineering, and Public Policy, 1993 , 1999a ; National Research Council, 1995a ).

The benchmarking reports covered the fields of mathematics ( Committee on Science, Engineering, and Public Policy, 1997 ), materials science ( Committee on Science, Engineering, and Public Policy, 1998 ), and immunology ( Committee on Science, Engineering, and Public Policy, 1999b ); they represented attempts to assess whether U.S. science was achieving the stated goals of the National Goals report ( Committee on Science, Engineering, and Public Policy, 1993 ) that the United States should be among the world leaders in all major areas of science and should maintain clear leadership in some major areas of science. These reports can be used to infer the collective beliefs across a broad range of the U.S. scientific community about the factors that contribute to U.S. scientific leadership, and implicitly to the factors that foster major scientific discoveries. The reports are also of interest because several of the factors they cite—for example, initiation of proposals by individual investigators, reliance on peer-based merit review—are the cynosures of proposals to modify the U.S. science system.

Across the three benchmarking reports, the core repeatedly cited as necessary for scientific progress was adequate facilities, quality and quantity of graduate students attracted to a field (and their subsequent early career successes in the field), diversity in funding sources, and adequate funding. In addition, with regard to the comparative international strength and the leadership position of U.S. science in these fields, the reports placed special emphasis on the “structure and financial-support mechanisms of the major research institutions in the United States” and on its organization of higher education research ( Committee on Science, Engineering and Public Policy, 1999b :35). Also highlighted as a contributing factor in “fostering innovation, creativity and rapid development of new technologies” was the “National Institutes of Health (NIH) model of research-grant allocation and funding: almost all research (except small projects funded by contracts) is initiated by individual investigators, and the decision as to merit is made by a dual review system of detailed peer review by experts in each subfield of biomedical science” (p. 36). 7

We accept the proposition that adequate funds to support research represents a necessary condition for sustained progress in a scientific field. Research progress also depends on the supply of researchers (including the number, age, and creativity of current and prospective researchers) and the organization of research, including the number and disciplinary mix of researchers engaged in a project or program and structure of the research team.

Supply of Researchers

The number, creativity, and age distribution of researchers in a field together affect the pace of scientific progress in the field. Numbers are important to the extent that the ability to generate scientific advances is randomly distributed through a population of comparably trained researchers. Fields with a larger number of active researchers can be expected to generate more scientific advance than fields with smaller such numbers. The pace of scientific advance across fields presumably also varies with their ability to attract the most able/creative/productive scientists. The attractiveness of a field at any point in time is likely to depend on its intellectual excitement (the challenges of the puzzles that it poses), its societal significance, the resources flowing to it to support research, and the prospects for longer term productive and gainful careers. Fields that exhibit these characteristics are likely to attract relatively larger cohorts of younger scientists; if scientific creativity is inversely correlated with age, such fields may be expected to exhibit greater vitality than those with aging cohorts of scientists.

This view is supported by much expert judgment and a number of empirical studies. For example, a study by the National Research Council (1998 :1) noted that “The continued success of the life-science research enterprise depends on the uninterrupted entry into the field of well-trained, skilled, and motivated young people. For this critical flow to be guaranteed, young aspirants must see that there are exciting challenges in life science research and they need to believe that they have a reasonable likelihood of becoming practicing independent scientists after their long years of training to prepare for their careers.”

Career opportunities for scientists affect the flow of young researchers into fields. Recent studies of career opportunities in the life sciences have noted that a “crisis of expectations” arises when career prospects fall short of scientific promise ( Freeman et al., 2001 ). Similar observations have been made at other times for the situations in physics, mathematics, computer science, and some fields of engineering. Studies also point, in general, to a decline in research productivity around midcareer. As detailed by Stephan and Levin (1992) , the decline reflects influences on both the willingness and ability of researchers to do scientific research. Older scientists are also seen to be slower to accept new ideas and techniques than are younger scientists. 8

Organization of Research

Since World War II, the social contract by which the federal government supports basic research has involved channeling large amounts of this support through awards to universities, much of that through grants to individual investigators. It is appropriate to consider whether such choices continue to be optimal and to consider related questions concerning the determinants of the research performance of individual faculty and of specific institutions or sets of institutions ( Guston and Keniston, 1994 ; Feller, 1996 ).

As detailed above, U.S. support of academic research across many fields, including aging research, is predicated on the proposition that “little science is the backbone of the scientific enterprise…. For those who believe that scientific discoveries are unpredictable, supporting many creative researchers who contribute to S&T, or the science base is prudent science policy” ( U.S. Congress Office of Technology Assessment, 1991 :146). Against this principle, trends toward “big science” and the requirements of interdisciplinary research have opened up the question of the optimal portfolio of funding mechanisms and award criteria to be employed by federal science agencies. Of special interest here as an alternative to the traditional model of single investigator–initiated research are what have been termed “industrial” models of research ( Ziman, 1984 ) or Mode II research; that is, research undertakings characterized by collaboration or teamwork among members of research groups participating in formally structured centers or institutes. Requests for proposals directed toward specific scientific, technological, and societal objectives; initiatives supporting collaborative, interdisciplinary modes of inquiry organized as centers rather than as single principal investigator projects; and use of selection criteria in addition to scientific merit are by now well-established parts of the research programs of federal science agencies, including NIH and the National Science Foundation. 9

A recurrent issue for federal science managers and for scientific communities is the relative rate of return to alternative arrangements, such as funding mechanisms. Making such comparisons is challenging. First, different research modes (e.g., single investigator–initiated proposals and multidisciplinary, center-based proposals submitted in response to a Request for Application) may produce different kinds of outputs. Single-investigator awards, typically described as the backbone of science, are intended cumulatively to build a knowledge base that affects clinical practice or public policy, to support the training of graduate students, to promote the development of networks of researchers and practitioners, and more—but no single awardee is expected to do all these things. Center awards also are expected to contribute to scientific progress—indeed to yield “value added” above the progress that can come from multiple single-investigator awards—but unlike single-investigator awards, they are typically expected to devote explicit attention to the other outcomes, such as translating the results of basic research into clinical practice. Because different modes of research support are expected to support different mixes of program objectives, direct comparisons of “performance” or “productivity” between or among them involves a complex set of weightings and assessments, both in terms of defining and measuring scientific progress and in assigning weights to the different kinds of scientific, programmatic, and societal objectives against which research is evaluated.

Little empirical evidence exists to inform comparisons among modes of research support. Empirical studies, most frequently in the form of bibliometric analyses, exist to compare the productivity of interdisciplinary research units, but these studies are not designed to answer the question of how much scientific progress would have been achieved had the funds allocated to such units been apportioned instead among a larger and more diverse number of single investigator awards ( Feller, 1992 ). Detailed criteria, for example, have been advanced to evaluate the performance of NIH’s center programs ( Institute of Medicine, 2004 ), and a number of center programs have been evaluated. However, these evaluations have not added up to a systematic assessment. 10

Expert judgment, historical assessment, and analysis of trends in science provide some support for core propositions about the sources of the vitality of U.S. science: adequate and sustainable funding; multiple, decentralized, funding streams; strong reliance on investigator-initiated proposals selected through competitive, merit-based review; coupling basic research with graduate education; and supplementary funding for capital-intensive modes of inquiry, interdisciplinary collaboration, targeted research objectives, and translation of basic research findings into clinical practice or technological innovations. Still, these principles may not provide wise guidance for the support of behavioral and social science research on aging, for three reasons. First, these observations come from experience with the life sciences, engineering sciences, and physical sciences, and it is not known whether the dynamics of scientific inquiry and progress are the same in the social and behavioral sciences. Second, it is not known whether recent trends in scientific inquiry, such as in the direction of interdisciplinarity, will continue, stop, or soon lead to a fundamental transformation in the way in which cutting-edge science (including in research on aging) is done. Third and perhaps most important, applying these principles presumes an environment of increasing total funds for research. In the more austere budget environment now projected for NIH and its subunits, it will not be possible to increase funding for all modes of support. Turning to existing research for guidance may prove of limited value for making trade-offs among competing funding paradigms.

• IMPLICATIONS FOR DECISION MAKING
• No theory exists that can reliably predict which research activities are most likely to lead to scientific advances or to societal benefit. The gulf between the decision-making environment of the research manager and the historian or other researcher retrospectively examining the emergence and subsequent development of a line of research is reflected in Weinberg’s (2001 :196) observation, “In judging the nature of scientific progress, we have to look at mature scientific theories, not theories at the moments when they are coming into being.” The history of science shows that evidence of past performance and current vitality, that is, of interest among scientists in a topic or line of research, are imperfect predictors of future progress. Thus, although it seems reasonable to expect that a newly developing field that generates excitement among scientists from other fields is a good bet to make progress in the near future, this expectation rests more on anecdote than on systematic empirical research. Notwithstanding the continuing search for improved quantitative measures and indicators for prospective assessment of scientific fields, practical choices about research investments will continue to depend on judgment. We address the prospects and potential roles of quantitative and other methods of science assessment in Chapter 5 .
• Science produces diverse kinds of benefits; consequently, assessing the potential of lines of research is a challenging task. Assessments should carefully apply multiple criteria of benefit. Science proceeds toward improving understanding and benefiting society on several fronts, but often at an uneven pace, so that a line of research may show rapid progress on one dimension or by one indicator while showing little or no progress on another. In setting research priorities among lines of research, it is important to consider evidence of past accomplishments on the several dimensions of scientific advances (discovery, analysis, explanation, integration, and development) and of contributions to society (e.g., identifying issues, finding solutions, informing choices). The policy implications of a finding that a line of research is not currently making much progress on one or more dimensions are not self-evident. Such an assessment might be used as a rationale for decreasing support (because the funds may be expected to be poorly spent), for increasing support (for example, if the poor performance is attributed to past underfunding), or for making investments to redirect the field so as to reinvigorate it. A field that appears unproductive may be stagnant, fallow, or pregnant. Telling which is not easy. Judgment can be aided by the assessments of people close to the field, although not just those so close as to have a vested interest in its survival or growth. The same kind of advice is useful for judging the proper timing for efforts to invest in fields in order to keep them alive or to reinvigorate them.
• Portfolio diversification strategies that involve investment in multiple fields and multiple kinds of research are appropriate for decision making, considering the inherent uncertainties of scientific progress. Through such strategies, research managers can minimize the consequences of overreliance on any single indicator of research quality or progress or any single presumption about what kinds of research are likely to be most productive. It is appropriate to diversify along several dimensions, including disciplines, modes of support, emphasis on theoretical or applied objectives, and so forth. Diversification is also advisable in terms of the kinds of evidence relied on to make decisions about what to support. For example, when quantitative indicators and informed peer judgment suggest supporting different lines of research, it is worth considering supporting some of each.
• Research managers should seek to emphasize investing where their investments are most likely to add value. This consideration may affect emphasis on types of scientific progress, research organizations and modes of support, and areas of support.
• Types of scientific progress. Even as they continue to pursue support of major scientific and programmatic advances, research managers may also find it productive to support improvements in databases and analytic techniques, efforts to integrate knowledge across fields and levels of analysis, efforts to examine underresearched questions, and the entry of new people to work on research problems.
• Research organizations and modes of support. Research managers should consider favoring support to research organizations or in modes that have been shown to have characteristics that are likely to promote progress, either generally or for specific fields or lines of scientific inquiry. NIH has multiple funding mechanisms available that would allow support for particular types of organizations ( Institute of Medicine, 2004 ). An ongoing study by Hollingsworth (2003 :8) identifies six organizational characteristics as “most important in facilitating the making of major discoveries” (see Box 4-1 ). Research managers might consider the findings of such studies in making choices about what kinds of organizations to support, especially in efforts to promote scientific innovation.
• Areas of support. Some fields may have sufficient other sources of funds that they do not need NIA support, or only need small investments from NIA to leverage funds from other sources. In other fields, however, BSR may be the only viable sponsor for the research. BSR managers may reasonably choose to emphasize supporting research in such fields because of the unlikelihood of leveraging funds. The value-added issue also affects decisions on modes of support and types of research to support.
• Interdisciplinary research. BSR should continue to support issue-focused interdisciplinary research to promote scientific activities and collaborations related to its mission that might not emerge from existing scientific communities and organizations structured around disciplines. Interdisciplinary research has significant potential to advance scientific objectives that research management can promote, such as scientific integration and development and scientists’ attention to societal objectives of science consistent with BSR’s mission. Moreover, BSR has a good track record of promoting these objectives through its support of selected areas of interdisciplinary, issue-focused research. BSR should continue to solicit research in areas that require interdisciplinary collaboration, to support data sets that can be used readily across disciplines, to fund interdisciplinary workshops and conferences, and to support cross-institution, issue-focused interdisciplinary research networks. Supporting such research requires special efforts and skills of research managers but holds the promise of yielding major advances that would not come from business-as-usual science.

Characteristics of Organizations That Produced Major Biomedical Discoveries: The Hollingsworth Study. Rogers Hollingsworth and colleagues (Hollingsworth and Hollingsworth, 2000; Hollingsworth, 2003) have been examining the characteristics of biomedical (more...)

It is often argued that progress in the behavioral and social sciences is qualitatively different from progress in the natural sciences. As noted in a National Research Council review of progress in the behavioral and social sciences ( Gerstein, 1986 :17), “Because they are embedded in social and technological change, subject to the unpredictable incidence of scientific ingenuity and driven by the competition of differing theoretical ideas, the achievements of behavioral and social science research are not rigidly predictable as to when they will occur, how they will appear, or what they might lead to.” The unstated (and untested) implication is that this unpredictability is more characteristic of the social sciences than the natural sciences. Another view states: “In the natural sciences, a sharp division of labor between the information-gathering and the theory-making functions is facilitated by an approximate consensus on the definition of research purposes and on the conceptual economizers guiding the systematic selection and organization of information. In the social sciences, where the subject matter of research and the comparatively lower level of theoretical agreement generally do not permit comparable consensus on the value and utility of information extracted from phenomena, sharp division of labor between empirical and theoretical tasks is less warranted” ( Ezrahi, 1978 :288). Even the same techniques are thought to have quite different roles in the social and natural sciences: “The role of statistics in social science is thus fundamentally different from its role in much of the physical science, in that it creates and defines the objects of study much more directly. Those objects are no less real than those of the physical science. They are even more often much better understood. But despite the unity of statistics—the same methods are useful in all areas—there are fundamental differences, and these have played a role in the historical development of all these fields” ( Stigler, 1999 :199).

Some observers even question the claims of the behavioral and social sciences to standing as sciences. As observed in a recent text on the history of science, “In the end, perhaps the most interesting question is: Did the drive to create a scientific approach to the study of human nature achieve its goal? For all the money and effort poured into creating a body of practical information on the topic, many scientists in better established areas remain suspicious, pointing to a lack of theoretical coherence that undermines the analogy with the ‘hard’ sciences” ( Bowler and Morus, 2005 :314–315).

According to Cole (2001 :37), “The problem with fields like sociology is that they have virtually no core knowledge. Sociology has a booming frontier but none of the activity at that frontier seems to enter the core.”

As noted by Galison (1999 :143), “Experimentalists … do not march in lockstep with theory…. Each subculture has its own rhythms of change, each has its own standards of demonstration, and each is embedded differently in the wider culture of institutions, practices, inventions and ideas.”

Rita Colwell, former director of the National Science Foundation, has stated that “Interdisciplinary connections are absolutely fundamental. They are synapses in this new capability to look over and beyond the horizon. Interfaces of the sciences are where the excitement will be the most intense” ( Colwell, 1998 ).

As stated in a recent National Research Council (2005b :150) report, “A remaining challenge is to determine what additional measures, if any, are needed to assess interdisciplinary research and teaching beyond those shown to be effective in disciplinary activities. Successful outcomes of an interdisciplinary research (IDR) program differ in several ways from those of a disciplinary program. First, a successful IDR program will have an impact on multiple fields or disciplines and produce results that feed back into and enhance disciplinary research. It will also create researchers and students with an expanded research vocabulary and abilities in more than one discipline and with an enhanced understanding of the interconnectedness inherent in complex problems.”

Consistent with the belief that competitive, merit-based review is key to creating the best possible conditions for scientific advance is the articulation of how “quality” is to be achieved and gauged under the Research and Development Investment Criteria established by the Office of Science and Technology Policy and the Office of Management and Budget on June 5, 2005: “A customary method for promoting quality is the use of a competitive, merit-based process” ( http://www ​.whitehouse ​.gov/omb/memoranda/m03-15.pdf , p. 7).

As Max Planck famously remarked, “a new scientific truth does not triumph by convincing its opponents and making them see the light, but because the its opponents eventually die, and a new generation grows up that is familiar with it.” Stephan and Levin (1992 :83) write: “empirical studies of Planck’s principle for the most part confirm the hypothesis that older scientists are slower than their younger colleagues are to accept new ideas and that eminent older scientists are the most likely to resist. The operative factor in resistance, however, is not age per se but, rather, the various indices of professional experience and prestige correlated with age …. [Y]oung scientists … may also be less likely to embrace new ideas, particularly if they assess such a course as being particularly risky.” Thus, a graying scientific community affects the rate of scientific innovation directly by being less productive and indirectly by being slow to accept new ideas as they emerge.

Interdisciplinary research and the industrial model of research are often found together, but they are not identical. One may organize centers based primarily on researchers from a single discipline, and researchers from several disciplines may collaborate, as co-principal investigators or as loosely coupled teams, on one-time awards. At NIH, research center grants “are awarded to extramural research institutions to provide support for long-term multidisciplinary programs of medical research. They also support the development of research resources, aim to integrate basic research with applied research and transfer activities, and promote research in areas of clinical applications with an emphasis on intervention, including prototype development and refinement of products, techniques, processes, methods, and practices” ( Institute of Medicine, 2004 ).

“NIH does not have formal regular procedures or criteria for evaluating center programs. From time to time, institutes conduct internal program reviews or appoint external review panels, but these ad hoc assessments are usually done in response to a perception that the program is no longer effective or appropriate rather than as part of a regular evaluation process. Most of these reviews rely on the judgment of experts rather than systematically collected objective data, although some formal program evaluations have been performed by outside firms using such data” ( Institute of Medicine, 2004 :121).

• Cite this Page National Research Council (US) Committee on Assessing Behavioral and Social Science Research on Aging; Feller I, Stern PC, editors. A Strategy for Assessing Science: Behavioral and Social Research on Aging. Washington (DC): National Academies Press (US); 2007. 4, Progress in Science.
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• Published: 06 June 2022

Perspectives on scientific progress

• Michela Massimi   ORCID: orcid.org/0000-0001-6626-9174 1  

Nature Physics volume  18 ,  pages 604–606 ( 2022 ) Cite this article

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Against the backdrop of various philosophical accounts, this Comment argues for the need of a human rights approach to scientific progress, which requires us to rethink how we view scientific knowledge.

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For the new Trump administration, one major challenge will be how to maintain national security in the face of technological change. Part of that discussion hinges on understanding the concept of dual use. There are three different dichotomies that could be at play when officials, scholars and scientists refer to dual use – and each uniquely influences the discussion around discovery and control.

For war or for peace

The first meaning of dual use describes technologies that can have both military and civilian uses . For example, technologies useful in industry or agriculture can also be used to create chemical weapons. In civilian life, a chemical called thiodiglycol is a common solvent, occasionally used in cosmetics and microscopy. Yet the same chemical is used in the creation of mustard gas, which decimated infantry in World War I .

This distinction is one of the clearest to be made about a particular technology or breakthrough. Often when governments recognize something has both civilian and military uses, they'll attempt to control how, and with whom, the technology is shared. For instance, the Australia Group is a collection of 42 nations that together agree to control the export of certain materials to countries which might use them to create chemical weapons.

Technologies can also be dual use because there are benefits that were secondary to their development. An obvious example is the internet: The packet switching that underlies the internet was originally created as a means to communicate between military installations in the event of nuclear war . It has since been released into the civilian domain, allowing you to read this article.

This distinction between military and civilian uses doesn't always mirror a distinction between good and bad uses. Some military uses, such as those that underpinned the internet, are good. And some civilian uses can be bad: Recent controversies over the militarization of police through the spread of technologies and tactics meant for war into the civilian sphere demonstrate how proliferation in the other direction can be controversial.

Dual use in this sense is about control. Both military and civilian uses could be valuable, as long as a country can maintain authority over its technologies. Because both uses can be valuable, dual use can also be used to justify expenditures, by providing incentives to governments to invest in technology that has multiple applications .

For good or for evil

In the January meeting at the NAS, however, the key distinction was between beneficent and malevolent uses. Today this is the most common way to think about dual-use science and technology.

Dual use, in this sense, is a distinctly ethical concept. It is, at its core, about what kinds of uses are considered legitimate or valuable, and what kinds are destructive. For example, some research on viruses allows us to better understand potential pandemic-causing pathogens. The work potentially opens the door to possible countermeasures and helps public health officials in terms of preparedness. There is, however, the risk that the same research could, through an act of terror or a lab accident , cause harm.

As of 2007, the U.S. National Science Advisory Board for Biosecurity provides advice on regulating " dual-use research of concern ." This is any life sciences research that could be misapplied to pose a threat to public health and safety, agricultural crops and other plants, animals, the environment or materiel.

The challenging ethical question is finding an acceptable trade-off between the benefits created by legitimate uses of dual-use research and the potential harms of misuse.

The recent NAS meeting discussed the spread of dual-use research's findings and methods, and who, if anyone, should be responsible for controlling its dispersal. Options that were considered included:

• subjecting biology research to security classifications, even in part;
• relying on scientists to responsibly control their own communications;
• export controls, of the type used by the Australia Group with its concerns about military/civilian dual-use of chemicals.

Participants reached no firm conclusions, and it will be an ongoing challenge for the Trump administration to tackle these continuing issues.

The other side of the equation, whether we should do some dual-use research in the first place, has also been considered. On Jan. 9, the outgoing Obama administration released its final guidance for "gain-of-function research" that may result in the creation of novel, virulent strains of infectious diseases – which may also be dual use. They recommended, among other things, that in order to proceed, the experiments at issue must be the only way to answer a particular scientific question, and must produce greater benefits than they do risks. The devil, of course, is in the details, and each government agency that conducts life sciences research will have decide how best to implement the guidance.

For offense or for defense

There's a third, little discussed meaning of "dual use" that distinguishes between offensive and defensive uses of biotechnology. A classic example of this kind of dual use is " Project Clear Vision ." From 1997 to 2000, American researchers set out to recreate Soviet bomblets used to disperse biological weapons. This kind of research treads the delicate area between a defensive project – the U.S. maintains Project Clear Vision's goal was to protect Americans against an attack – and an offensive project that might violate the Biological Weapons Convention.

What is offensive and what is defensive is to some degree in the eye of the beholder. The Kalashnikov submachine gun was designed in 1947 to defend Russia, but has since become the weapon of choice in conflicts the world over – to the point that its creator expressed regret for his invention . Regardless of intent, the question of how the weapon is used in these conflicts, offensively or defensively, will vary depending on which end of the barrel one is on.

Regulating science

When scientists and policy experts wrangle over how to deal with dual-use technologies, they tend to focus on the division between applications for good or evil. This is important: We don't necessarily want to hinder science without valid reason, because it provides substantial benefits to human health and welfare.

However, there are fears that the lens of dual use could stifle progress by driving scientists away from potentially controversial research: Proponents of gain of function have argued that graduate students or postdoctoral fellows could choose other research areas in order to avoid the policy debate. To date, however, the total number of American studies put on hold – as a result of safety concerns, much less dual-use concerns – was initially 18 , with all of these being permitted to resume with the implementation of the policies set out on Jan. 9 by the White House. As a proportion of scientific research , this is vanishingly small.

Arguably, in a society that views science as an essential part of national security, dual-use research is almost certain to appear. This is definitely the case in the U.S., where the work of neuroscientists, increasingly, is funded by the national military , or the economic competitiveness that emerges from biotech is considered a national security priority.

Making decisions about the security implications of science and technology can be complicated. That's why scientists and policymakers need clarity on the dual-use distinction to help consider our options.

Provided by The Conversation

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The Scientific Progress, Essay Example

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Scientific progress is a natural occurrence. However, in Frankenstein by Mary Shelley, the author shows us that technological advancement can be a negative phenomenon. Humanity has made many attempts to contribute to scientific understanding as a consequence of both interest and necessity. However, when ethical practices are breached, the result of scientific exploration can procure negative results. Overall, technology can behave as a negative force in Western Civilization. Those with technological prowess have the ability to gain control, which can result in death and disaster. Thus, it is beneficial to implement ethics in research to ensure that scientific advancement does not result in negativity. The monster created in Frankenstein is an important example of what should be avoided. Creating simply to satisfy curiosity can cause pain and suffering, not only on society, but on the individuals involved in our experiments as well.

It is apparent that many great risks need to be taken to contribute to scientific progress. According to Francis Bacon, a well-known scientist, “For the induction of which the logicians talk, which proceeds by simple enumeration, is a childish affair, unsafe in its conclusions, in danger from a contradictory instance, taking account of only what is familiar, and leading to no result” (Bacon). This demonstrates that without taking risks, it is impossible to find new information. However, risks can be taken while still protecting people. In Frankenstein , the scientist has little regards for the results of his creation. Instead, he toils to find a way to create a living person where no life had been present previously. However, his creation suffers because there is none like him. Such a disaster would not have occurred if the doctor had more foresight during his experimentation, and it is possible that his would have contributed to technology that is meaningful for society, rather than simply allowing him to create an abomination out of mere curiosity.

Many technological developments that have been beneficial to society have been due to extensive collaboration between different individuals. A primary example of technological advancement that has aided people is the technological advancement that occurred during the Renaissance (Principe). During this time period, there was a broad range of ideas exchanged between different people in Europe, which contributed to advances in art, the ability to revolutionize farming techniques and more. These advancements were beneficial because they allowed people to enjoy entertainment and acquire food more easily. However, the individuals that created new ideas and objects during the Renaissance did so not just out of curiosity, but because they wanted to improve the lives of others. As such, there is a significant difference between Frankenstein’s discovery and the discoveries made by individuals during the Renaissance. Frankenstein’s monster represents the evil that people are able to accomplish through scientific investigation, while the people of the Renaissance represent ethics and utility.

Throughout the book, Frankenstein shows that he desires to create the monster because he has a drive for scientific understanding. He explains, “Natural philosophy is the genius that has regulated my fate; I desire, therefore, in this narration, to state those facts which led to my predilection for that science” (Shelley). Frankenstein demonstrates that he feels that it is his responsibility to engage in this exploration because of his knowledge. He feels tied to the fate of science somehow and must act upon this in order to feel truly fulfilled. Frankenstein continues to explain, “My father was not scientific, and I was left to struggle with a child’s blindness, added to a student’s thirst for knowledge” (Shelley). Therefore, he feels that he must use science to cope for the lack of understanding on the behalf of his father. It is clear, therefore, that Frankenstein is engaging in science for purely selfish reasons. He wishes to satisfy his own personal curiosity and prove to the scientific world what he can do. Since he creates a monster for these reasons, this action can be considered truly detrimental to society, indicating an instance in which increased technology and scientific knowledge may be a negative thing.

In conclusion, it would be erroneous to call scientific progress completely positive or completely negative. Rather, scientific progress is good if the people that are engaged in the research are well-intended, while it can be bad if the researchers are self-interested and do not consider the ethical repercussions of their work. Frankenstein is the latter type of person, who only cares about pushing science forward without considering the impacts that it will have on those around him. With little regard for life, Frankenstein creates a living and breathing monster, who suffers as a consequence of his creation. Mary Shelley therefore shows us that technological advancement can have negative consequences if careful consideration is not taken during the experimental process. It is beneficial for researchers to fully account for the consequences of their experiments if they wish to continue to drive society forward. Technology itself cannot be considered good or bad; it is the people who create it that make this decision.

Bibliography

Bacon, F. Novum Organum.

Principle, L.M. The Scientific Revolution.

Shelley, M. Frankenstein. 1818.

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