Preferably scientific investigations would promote true rather than false beliefs. The phenomenon of fraud represents a standing challenge to this veritistic ideal. When scientists publish fraudulent results they knowingly enter falsehoods into the information stream of science. Recognition of this challenge has prompted calls for scientists to more consciously adopt the veritistic ideal in their own work. In this paper I argue against such promotion of the veritistic ideal. It turns out that a sincere desire on the part of scientists (...) to see the truth propagated may well promote more fraud rather than less. (shrink)

Philosophers of science debate the proper role of non-epistemic value judgements in scientific reasoning. Many modern authors oppose the value free ideal, claiming that we should not even try to get scientists to eliminate all such non-epistemic value judgements from their reasoning. W. E. B. Du Bois, on the other hand, has a defence of the value free ideal in science that is rooted in a conception of the proper place of science in a democracy. In particular, Du Bois argues (...) that the value free ideal must be upheld in order to, first, retain public trust in science and, second, ensure that those best placed to make use of scientifically acquired information are able to do so. This latter argument turns out to relate Du Bois’ position on the value free ideal in science to his defence of epistemic democracy. In this essay I elaborate, motivate, and relate to the modern debate, Du Bois’ under-appreciated defence of the value free ideal. (shrink)

Epistemic accounts of scientific collaboration usually assume that, one way or another, two heads really are more than twice better than one. We show that this hypothesis is unduly strong. We present a deliberately crude model with unfavorable hypotheses. We show that, even then, when the priority rule is applied, large differences in successfulness can emerge from small differences in efficiency, with sometimes increasing marginal returns. We emphasize that success is sensitive to the structure of competing communities. Our results suggest (...) that purely epistemic explanations of the efficiency of collaborations are less plausible but have much more powerful socioepistemic versions. (shrink)

It is often assumed that democracies can make good use of the epistemic benefi ts of diversity among their citizenry, but difficult to show why this is the case. In a deliberative democracy, epistemically relevant diversity has three aspects: the diversity of opinions, values, and perspectives. Deliberative democrats generally argue for an epistemic form of Rawls' difference principle: that good deliberative practice ought to maximize deliberative inputs, whatever they are, so as to benefi t all deliberators, including the least eff (...) ective. The proper maximandum of such a principle is not the pool of reasons, but rather the availability of perspectives. Th is sort of diversity makes robustness across different perspectives the proper epistemic aim of deliberative processes. Robustness also offers a measure of success for those democratic practices of inquiry based on the deliberation of all citizens. (shrink)

This article examines two questions about scientists’ search for knowledge. First, which search strategies generate discoveries effectively? Second, is it advantageous to diversify search strategies? We argue pace Weisberg and Muldoon, “Epistemic Landscapes and the Division of Cognitive Labor”, that, on the first question, a search strategy that deliberately seeks novel research approaches need not be optimal. On the second question, we argue they have not shown epistemic reasons exist for the division of cognitive labor, identifying the errors that led (...) to their conclusions. Furthermore, we generalize the epistemic landscape model, showing that one should be skeptical about the benefits of social learning in epistemically complex environments. (shrink)

For the last forty years, two claims have been at the core of disputes about scientific change: that scientists reason rationally and that science is progressive. For most of this time discussions were polarized between philosophers, who defended traditional Enlightenment ideas about rationality and progress, and sociologists, who espoused relativism and constructivism. Recently, creative new ideas going beyond the polarized positions have come from the history of science, feminist criticism of science, psychology of science, and anthropology of science. Addressing the (...) traditional arguments as well as building on these new ideas, Miriam Solomon constructs a new epistemology of science. After discussions of the nature of empirical success and its relation to truth, Solomon offers a new, social account of scientific rationality. She shows that the pursuit of empirical success and truth can be consistent with both dissent and consensus, and that the distinction between dissent and consensus is of little epistemic significance. In building this social epistemology of science, she shows that scientific communities are not merely the locus of distributed expert knowledge and a resource for criticism but also the site of distributed decision making. Throughout, she illustrates her ideas with case studies from late-nineteenth- and twentieth-century physical and life sciences. Replacing the traditional focus on methods and heuristics to be applied by individual scientists, Solomon emphasizes science funding, administration, and policy. One of her goals is to have a positive influence on scientific decision making through practical social recommendations. (shrink)

Theories of scientific rationality typically pertain to belief. In this paper, the author argues that we should expand our focus to include motivations as well as belief. An economic model is used to evaluate whether science is best served by scientists motivated only by truth, only by credit, or by both truth and credit. In many, but not all, situations, scientists motivated by both truth and credit should be judged as the most rational scientists.

There is growing interest in understanding and eliciting division of labor within groups of scientists. This paper illustrates the need for this division of labor through a historical example, and a formal model is presented to better analyze situations of this type. Analysis of this model reveals that a division of labor can be maintained in two different ways: by limiting information or by endowing the scientists with extreme beliefs. If both features are present however, cognitive diversity is maintained indefinitely, (...) and as a result agents fail to converge to the truth. Beyond the mechanisms for creating diversity suggested here, this shows that the real epistemic goal is not diversity but transient diversity. (shrink)

Methodological norms are seen as rules defining a competitive game, and it is argued that rational recognition-seeking scientists can reach a collective agreement about which specific norms serve better their individual interests, especially if the choice is made `under a veil of ignorance', i.e. , before knowing what theory will be proposed by each scientist. Norms for theory assessment are distinguished from norms for theory choice (or inference rules), and it is argued that pursuit of recognition only affects this second (...) type of rule. An inference rule similar to `eliminative induction' is defended on the basis of such a possible agreement. According to this contractarian approach, both the explanation and the justification of scientific norms only need to refer to the preferences of individual scientists, without assuming the existence of `collective' points of view. (shrink)

Because of its complexity, contemporary scientific research is almost always tackled by groups of scientists, each of which works in a different part of a given research domain. We believe that understanding scientific progress thus requires understanding this division of cognitive labor. To this end, we present a novel agent-based model of scientific research in which scientists divide their labor to explore an unknown epistemic landscape. Scientists aim to climb uphill in this landscape, where elevation represents the significance of the (...) results discovered by employing a research approach. We consider three different search strategies scientists can adopt for exploring the landscape. In the first, scientists work alone and do not let the discoveries of the community as a whole influence their actions. This is compared with two social research strategies, which we call the follower and maverick strategies. Followers are biased towards what others have already discovered, and we find that pure populations of these scientists do less well than scientists acting independently. However, pure populations of mavericks, who try to avoid research approaches that have already been taken, vastly outperform both of the other strategies. Finally, we show that in mixed populations, mavericks stimulate followers to greater levels of epistemic production, making polymorphic populations of mavericks and followers ideal in many research domains. (shrink)

In this article we argue that self-deception evolved to facilitate interpersonal deception by allowing people to avoid the cues to conscious deception that might reveal deceptive intent. Self-deception has two additional advantages: It eliminates the costly cognitive load that is typically associated with deceiving, and it can minimize retribution if the deception is discovered. Beyond its role in specific acts of deception, self-deceptive self-enhancement also allows people to display more confidence than is warranted, which has a host of social advantages. (...) The question then arises of how the self can be both deceiver and deceived. We propose that this is achieved through dissociations of mental processes, including conscious versus unconscious memories, conscious versus unconscious attitudes, and automatic versus controlled processes. Given the variety of methods for deceiving others, it should come as no surprise that self-deception manifests itself in a number of different psychological processes, and we discuss various types of self-deception. We then discuss the interpersonal versus intrapersonal nature of self-deception before considering the levels of consciousness at which the self can be deceived. Finally, we contrast our evolutionary approach to self-deception with current theories and debates in psychology and consider some of the costs associated with self-deception. (shrink)

Some scientists are happy to follow in the footsteps of others; some like to explore novel approaches. It is tempting to think that herein lies an epistemic division of labor conducive to overall scientific progress: the latter point the way to fruitful areas of research, and the former more fully explore those areas. Weisberg and Muldoon’s model, however, suggests that it would be best if all scientists explored novel approaches. I argue that this is due to implausible modeling choices, and (...) I present an alternative ‘epistemic landscape’ model that demonstrates the alleged benefits from division of labor, with one restriction. (shrink)

Thagard provides a review and assessment of central aspects of rationality in science, dealing first with the traditional question: What is the nature of the reasoning by which individual scientists accept and reject conflicting hypotheses? He also discusses the nature of practical reason in science and then turns to the question of the nature of group rationality in science. In this latter context, Thagard discusses, among other matters, his CCC model, which shows how epistemic group rationality can arise in agents (...) who communicate with each other while focusing on the explanation of observed phenomena. In the remainder of the chapter he examines whether scientists are in fact rational, that is, whether they conform to normative standards of individual and group rationality. He considers various psychological and sociological factors that have been taken to undermine the rationality of science. (shrink)

Science's priority rule rewards those who are first to make a discovery, at the expense of all other scientists working towards the same goal, no matter how close they may be to making the same discovery. I propose an explanation of the priority rule that, better than previous explanations, accounts for the distinctive features of the rule. My explanation treats the priority system, and more generally, any scheme of rewards for scientific endeavor, as a device for achieving an allocation of (...) resources among different research programs that provides as much benefit as possible to society. I show that the priority system is especially well suited to finding an efficient allocation of resources in those situations, characteristic of scientific inquiry, in which any success in an endeavor subsequent to the first success brings little additional benefit to society. (shrink)

A number of formal models, including a highly influential model from Hong and Page, purport to show that functionally diverse groups often beat groups of individually high-performing agents in solving problems. Thompson argues that in Hong and Page’s model, that the diverse groups are created by a random process explains their success, not the diversity. Here, I defend the diversity interpretation of the Hong and Page result. The failure of Thompson’s argument shows that to understand the value of functional diversity, (...) we should be clearer about how we conceive of and measure that diversity. (shrink)

Data which constitute satisfactory evidence in other contexts are sometimes not treated as valid evidence in the context of scientic conrmation. I give a justicatory explanation of this fact, appealing to the incentives, biases, and social situatedness of scientists.

We show that previous results from epistemic network models showing the benefits of decreased connectivity in epistemic networks are not robust across changes in parameter values. Our findings motivate discussion about whether and how such models can inform real-world epistemic communities. As we argue, only robust results from epistemic network models should be used to generate advice for the real-world, and, in particular, decreasing connectivity is a robustly poor recommendation.

In this fascinating autobiography from the foremost genius of twentieth-century physics, Max Planck tells the story of his life, his aims, and his thinking. Published posthumously, the papers in this volume were written for the general reader and make accessible his scientific theories as well as his philosophical ideals, including his thoughts on ethics and morals. Max (Karl Ernst Ludwig) Planck was a German physicist and philosopher known for his quantum theory, for which he won the Nobel Prize in Physics (...) in 1918. Planck was born in Kiel, Germany, in 1858 to an academic family, and he valued education from a young age. He attended the Universities of Munich and Berlin to study physics under the great scientific leaders Kirchhoff and Helmholtz. His early work mainly focused on the study of thermodynamics, and in 1900 he published a paper on his quantum theory that would change the face of modern physics. Planck worked as a professor at Berlin University his entire life, and he also served as the president of the Kaiser Wilhelm Society for the Promotion of Science. During World War II, Planck experienced great hardships while he remained in Germany but openly opposed the Nazi regime. One of his two sons was executed during this time for an unsuccessful attempt on Hitler's life, and Planck's home in Berlin was eventually bombed. He continued to write on physics and philosophy until his death in 1947. (shrink)

In the latter half of the twentieth century, philosophers of science have argued (implicitly and explicitly) that epistemically rational individuals might compose epistemically irrational groups and that, conversely, epistemically rational groups might be composed of epistemically irrational individuals. We call the conjunction of these two claims the Independence Thesis, as they together imply that methodological prescriptions for scientific communities and those for individual scientists might be logically independent of one another. We develop a formal model of scientific inquiry, define four (...) criteria for individual and group epistemic rationality, and then prove that the four definitions diverge, in the sense that individuals will be judged rational when groups are not and vice versa. We conclude by explaining implications of the inconsistency thesis for (i) descriptive history and sociology of science and (ii) normative prescriptions for scientific communities. (shrink)

There is a commonly made distinction between two types of scientists: risk-taking, trailblazing mavericks and detail-oriented followers. A number of recent papers have discussed the question what a desirable mixture of mavericks and followers looks like. Answering this question is most useful if a scientific community can be steered toward such a desirable mixture. One attractive route is through credit incentives: manipulating rewards so that reward-seeking scientists are likely to form the desired mixture of their own accord. Here I argue (...) that this idea is less straightforward than it may seem. Interpreting mavericks as scientists who prioritize rewards over speed and risk, I show in a deliberatively simple model that there is a fixed mixture which is not particularly likely to be desirable and which credit incentives cannot alter. I consider a way around this result, but this has some major drawbacks. I conclude that credit incentives are not as promising a way to create a desirable mixture of mavericks and followers as one might have thought. (shrink)

The communist norm requires that scientists widely share the results of their work. Where did this norm come from, and how does it persist? Michael Strevens provides a partial answer to these questions by showing that scientists should be willing to sign a social contract that mandates sharing. However, he also argues that it is not in an individual credit-maximizing scientist's interest to follow this norm. I argue against Strevens that individual scientists can rationally conform to the communist norm, even (...) in the absence of a social contract or other ways of socially enforcing the norm, by proving results to this effect in a game-theoretic model. This shows that the incentives provided to scientists through the priority rule are sufficient to explain both the origins and the persistence of the communist norm, adding to previous results emphasizing the benefits of the incentive structure created by the priority rule. (shrink)

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others’, in (...) order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. (shrink)

The Hong and Page ‘diversity trumps ability’ result has been used to argue for the more general claim that a diverse set of agents is epistemically superior to a comparable group of experts. Here we extend Hong and Page’s model to landscapes of different degrees of randomness and demonstrate the sensitivity of the ‘diversity trumps ability’ result. This analysis offers a more nuanced picture of how diversity, ability, and expertise may relate. Although models of this sort can indeed be suggestive (...) for diversity policies, we advise against interpreting such results overly broadly. (shrink)

The article presents an agent-based model of scientific interaction aimed at examining how different degrees of connectedness of scientists impact their efficiency in knowledge acquisition. The model is built on the basis of Zollman’s ABM by changing some of its idealizing assumptions that concern the representation of the central notions underlying the model: epistemic success of the rivalling scientific theories, scientific interaction and the assessment in view of which scientists choose theories to work on. Our results suggest that whether and (...) to what extent the degree of connectedness of a scientific community impacts its efficiency is a highly context-dependent matter since different conditions deem strikingly different results. More generally, we argue that simplicity of ABMs may come at a price: the requirement to run extensive robustness analysis before we can specify the adequate target phenomenon of the model.1 1Introduction2Zollman's 2010 Model3Static versus Dynamic Epistemic Success 3.1Introducing the notion of dynamic epistemic success3.2Implementation and results for the basic setup4Critical Interaction 4.1Introducing critique4.2Implementation and results5Inertia of Inquiry 5.1Introducing rational inertia5.2Implementation and results6Threshold Below Which Theories Are Equally Promising 6.1An inquiry that is even more difficult6.2Implementation and results7Discussion8Conclusion. (shrink)

It is valuable for inquiry to have researchers who are committed advocates of their own theories. However, in light of pervasive disagreement, such a commitment is not well explained by the idea that researchers believe their theories. Instead, this commitment, the rational attitude to take toward one’s favored theory during the course of inquiry, is what I call endorsement. Endorsement is a doxastic attitude, but one which is governed by a different type of epistemic rationality. This inclusive epistemic rationality is (...) sensitive to reasons beyond those to think the particular proposition in question is true. Instead, it includes extrinsic epistemic reasons, which concern the health of inquiry more generally. Such extrinsic reasons include the distribution of cognitive labor that a researcher will contribute to by endorsing a particular theory. Recognizing endorsement and inclusive epistemic rationality thus allows us to smooth a tension between individual rationality and collective rationality. It does so by showing how it can be epistemically rational to endorse a theory on the basis of the way this endorsement will benefit collective inquiry. I provide a decision theoretic treatment for inclusive epistemic rationality and endorsement which illustrates how this can be accomplished. (shrink)

"Legend is overdue for replacement, and an adequate replacement must attend to the process of science as carefully as Hull has done. I share his vision of a serious account of the social and intellectual dynamics of science that will avoid both the rosy blur of Legend and the facile charms of relativism.... Because of [Hull's] deep concern with the ways in which research is actually done, Science as a Process begins an important project in the study of science. It (...) is one of a distinguished series of books, which Hull himself edits."—Philip Kitcher, Nature "In Science as a Process, [David Hull] argues that the tension between cooperation and competition is exactly what makes science so successful.... Hull takes an unusual approach to his subject. He applies the rules of evolution in nature to the evolution of science, arguing that the same kinds of forces responsible for shaping the rise and demise of species also act on the development of scientific ideas."—Natalie Angier, New York Times Book Review "By far the most professional and thorough case in favour of an evolutionary philosophy of science ever to have been made. It contains excellent short histories of evolutionary biology and of systematics (the science of classifying living things); an important and original account of modern systematic controversy; a counter-attack against the philosophical critics of evolutionary philosophy; social-psychological evidence, collected by Hull himself, to show that science does have the character demanded by his philosophy; and a philosophical analysis of evolution which is general enough to apply to both biological and historical change."—Mark Ridley, Times Literary Supplement "Hull is primarily interested in how social interactions within the scientific community can help or hinder the process by which new theories and techniques get accepted.... The claim that science is a process for selecting out the best new ideas is not a new one, but Hull tells us exactly how scientists go about it, and he is prepared to accept that at least to some extent, the social activities of the scientists promoting a new idea can affect its chances of being accepted."—Peter J. Bowler, Archives of Natural History "I have been doing philosophy of science now for twenty-five years, and whilst I would never have claimed that I knew everything, I felt that I had a really good handle on the nature of science, Again and again, Hull was able to show me just how incomplete my understanding was.... Moreover, [Science as a Process] is one of the most compulsively readable books that I have ever encountered."—Michael Ruse, Biology and Philosophy. (shrink)