Scientific research is almost always conducted by communities of scientists of varying size and complexity. Such communities are effective, in part, because they divide their cognitive labor: not every scientist works on the same project. Philip Kitcher and Michael Strevens have pioneered efforts to understand this division of cognitive labor by proposing models of how scientists make decisions about which project to work on. For such models to be useful, they must be simple enough for us to understand their dynamics, (...) but faithful enough to reality that we can use them to analyze real scientific communities. To satisfy the first requirement, we must employ idealizations to simplify the model. The second requirement demands that these idealizations not be so extreme that we lose the ability to describe real-world phenomena. This paper investigates the status of the assumptions that Kitcher and Strevens make in their models, by first inquiring whether they are reasonable representations of reality, and then by checking the models' robustness against weakenings of these assumptions. To do this, we first argue against the reality of the assumptions, and then develop a series of agent-based simulations to systematically test their effects on model outcomes. We find that the models are not robust against weakenings of these idealizations. In fact we find that under certain conditions, this can lead to the model predicting outcomes that are qualitatively opposite of the original model outcomes. (shrink)

A scientific community cannot practice its trade without some set of received beliefs. These beliefs form the foundation of the "educational initiation that prepares and licenses the student for professional practice". The nature of the "rigorous and rigid" preparation helps ensure that the received beliefs are firmly fixed in the student's mind. Scientists take great pains to defend the assumption that scientists know what the world is like...To this end, "normal science" will often suppress novelties which undermine its foundations. Research (...) is therefore not about discovering the unknown, but rather "a strenuous and devoted attempt to force nature into the conceptual boxes supplied by professional education". (shrink)

Striving to boldly redirect the philosophy of science, this book by renowned philosopher Philip Kitcher examines the heated debate surrounding the role of science in shaping our lives. Kitcher explores the sharp divide between those who believe that the pursuit of scientific knowledge is always valuable and necessary--the purists--and those who believe that it invariably serves the interests of people in positions of power. In a daring turn, he rejects both perspectives, working out a more realistic image of the sciences--one (...) that allows for the possibility of scientific truth, but nonetheless permits social consensus to determine which avenues to investigate. He then proposes a democratic and deliberative framework for responsible scientists to follow. Controversial, powerful, yet engaging, this volume will appeal to a wide range of readers. Kitcher's nuanced analysis and authorititative conclusion will interest countless scientists as well as all readers of science--scholars and laypersons alike. (shrink)

During the last three decades, reflections on the growth of scientific knowledge have inspired historians, sociologists, and some philosophers to contend that scientific objectivity is a myth. In this book, Kitcher attempts to resurrect the notions of objectivity and progress in science by identifying both the limitations of idealized treatments of growth of knowledge and the overreactions to philosophical idealizations. Recognizing that science is done not by logically omniscient subjects working in isolation, but by people with a variety of personal (...) and social interests, who cooperate and compete with one another, he argues that, nonetheless, we may conceive the growth of science as a process in which both our vision of nature and our ways of learning more about nature improve. Offering a detailed picture of the advancement of science, he sets a new agenda for the philosophy of science and for other "science studies" disciplines. (shrink)

Thomas S. Kuhn's classic book is now available with a new index.

Social epistemology is the study of the social dimensions of knowledge or information. There is little consensus, however, on what the term "knowledge" comprehends, what is the scope of the "social", or what the style or purpose of the study should be. According to some writers, social epistemology should retain the same general mission as classical epistemology, revamped in the recognition that classical epistemology was too individualistic. According to other writers, social epistemology should be a more radical departure from classical (...) epistemology, a successor discipline that would replace epistemology as traditionally conceived. The classical approach could be realized in at least two forms. One would emphasize the traditional epistemic goal of acquiring true beliefs. It would study social practices in terms of their impact on the truth-values of agents’ beliefs. A second version of the classical approach would focus on the epistemic goal of having justified or rational beliefs. Applied to the social realm, it might concentrate, for example, on when a cognitive agent is justified or warranted in accepting the statements and opinions of others. Proponents of the anti-classical approach have little or no use for concepts like truth and justification. In addressing the social dimensions of knowledge, they understand "knowledge" as simply what is believed, or what beliefs are "institutionalized" in this or that community, culture, or context. They seek to identify the social forces and influences responsible for knowledge production so conceived. Social epistemology is theoretically significant because of the central role of society in the knowledge-forming process. It also has practical importance because of its possible role in the redesign of information-related social institutions. (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)

A small consortium of philosophers has begun work on the implications of epistemic networks (Zollman 2008 and forthcoming; Grim 2006, 2007; Weisberg and Muldoon forthcoming), building on theoretical work in economics, computer science, and engineering (Bala and Goyal 1998, Kleinberg 2001; Amaral et. al., 2004) and on some experimental work in social psychology (Mason, Jones, and Goldstone, 2008). This paper outlines core philosophical results and extends those results to the specific question of thresholds. Epistemic maximization of certain types does show (...) clear threshold effects. Intriguingly, however, those effects appear to be importantly independent from more familiar threshold effects in networks. (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)

To arrive at their final evaluation of a manuscript or grant proposal, reviewers must convert a submission’s strengths and weaknesses for heterogeneous peer review criteria into a single metric of quality or merit. I identify this process of commensuration as the locus for a new kind of peer review bias. Commensuration bias illuminates how the systematic prioritization of some peer review criteria over others permits and facilitates problematic patterns of publication and funding in science. Commensuration bias also foregrounds a range (...) of structural strategies for realigning peer review practices and institutions with the aims of science. (shrink)

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)

Current theories of the division of cognitive labor are confined to the “context of justification,” assuming exogenous theories. But new theories are made from the same labor that is used for developing existing theories, and if none of this labor is ever allocated to create new alternatives, then scientific progress is impossible. A unified model is proposed in which theories are no longer given but a function of the division of labor in the model itself. The interactions of individuals balancing (...) the exploitation of existing theories and the exploration of new theories results in a robust cyclical pattern. (shrink)

Epistemology has historically focused on individual inquirers conducting their private intellectual affairs independently of one another. As a descriptive matter, however, what people believe and know is largely a function of their community and culture, narrowly or broadly construed. Most of what we believe is influenced, directly or indirectly, by the utterances and writings of others. So social epistemology deserves at least equal standing alongside the individual sector of epistemology.

When should a scientific community be cognitively diverse? This article presents a model for studying how the heterogeneity of learning heuristics used by scientist agents affects the epistemic efficiency of a scientific community. By extending the epistemic landscapes modeling approach introduced by Weisberg and Muldoon, the article casts light on the micro-mechanisms mediating cognitive diversity, coordination, and problem-solving efficiency. The results suggest that social learning and cognitive diversity produce epistemic benefits only when the epistemic community is faced with problems of (...) sufficient difficulty. (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)

In 1988, David Hull presented an evolutionary account of science. This was a direct analogy to evolutionary accounts of biological adaptation, and part of a generalized view of Darwinian selection accounts that he based upon the Universal Darwinism of Richard Dawkins. Criticisms of this view were made by, among others, Kim Sterelny, which led to it gaining only limited acceptance. Some of these criticisms are, I will argue, no longer valid in the light of developments in the formal modeling of (...) evolution, in particular that of Sergey Gavrilets’ work on adaptive landscapes. If we can usefully recast the Hullian view of science as being driven by selection in terms of Gavrilets’ and Kaufmann’s view of there being “giant components” of high-fitness networks through any realistic adaptive landscape, we may now find it useful to ask what the adaptive pressures on science are, and to extend the metaphor into a full analogy. This is in effect to reconcile the Fisherianism of the Dawkins–Hull approach to selection and replicators, with a Wrightean drift account of social constructionist views of science, preserving, it is to be hoped, the valuable aspects of both. (shrink)

Exploratory inquiry has difficulty attracting research funding because funding agencies have little sense of how to detect good science in exploratory contexts. After documenting and explaining the focus on hypothesis testing among a variety of institutions responsible for distinguishing between good and bad science, I analyze the NIH grant review process. I argue that a good explanation for the focus on hypothesis testing—at least at the level of science funding agencies—is the fact that hypothesis-driven research is relatively easy to appraise. (...) I then explore one method by which we might gauge the epistemic merits of different styles of inquiry. (shrink)