Scientific consensus is widely deferred to in public debates as a social indicator of the existence of knowledge. However, it is far from clear that such deference to consensus is always justified. The existence of agreement in a community of researchers is a contingent fact, and researchers may reach a consensus for all kinds of reasons, such as fighting a common foe or sharing a common bias. Scientific consensus, by itself, does not necessarily indicate the existence of shared knowledge among (...) the members of the consensus community. I address the question of under what conditions it is likely that a consensus is in fact knowledge based. I argue that a consensus is likely to be knowledge based when knowledge is the best explanation of the consensus, and I identify three conditions—social calibration, apparent consilience of evidence, and social diversity, for knowledge being the best explanation of a consensus. (shrink)

Discussion of epistemic responsibility typically focuses on belief formation and actions leading to it. Similarly, accounts of collective epistemic responsibility have addressed the issue of collective belief formation and associated actions. However, there has been little discussion of collective responsibility for preventing epistemic harms, particularly those preventable only by the collective action of an unorganized group. We propose an account of collective epistemic responsibility which fills this gap. Building on Hindriks' (2019) account of collective moral responsibility, we introduce the Epistemic (...) Duty to Join Forces. Our theory provides an account of the responsibilities of scientists to prevent epistemic harms during inquiry. (shrink)

In the philosophy of science, it is a common proposal that values are illegitimate in science and should be counteracted whenever they drive inquiry to the confirmation of predetermined conclusions. Drawing on recent cognitive scientific research on human reasoning and confirmation bias, I argue that this view should be rejected. Advocates of it have overlooked that values that drive inquiry to the confirmation of predetermined conclusions can contribute to the reliability of scientific inquiry at the group level even when they (...) negatively affect an individual’s cognition. This casts doubt on the proposal that such values should always be illegitimate in science. It also suggests that advocates of that proposal assume a narrow, individualistic account of science that threatens to undermine their own project of ensuring reliable belief formation in science. (shrink)

In this paper I examine the epistemic function of agent-based models of scientific inquiry, proposed in the recent philosophical literature. In view of Boero and Squazzoni’s classification of ABMs into case-based models, typifications and theoretical abstractions, I argue that proposed ABMs of scientific inquiry largely belong to the last category. While this means that their function is primarily exploratory, I suggest that they are epistemically valuable not only as a temporary stage in the development of ABMs of science, but by (...) providing insights into theoretical aspects of scientific rationality. I illustrate my point with two examples of highly idealized ABMs of science, which perform two exploratory functions: Zollman’s ABM which provides a proof-of-possibility in the realm of theoretical discussions on scientific rationality, and an argumentation-based ABM, which provides insights into potential mechanisms underlying the efficiency of scientific inquiry. (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)

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)

This book exhibits deep philosophical quandaries and intricacies of the historical development of science lying behind a simple and fundamental item of common sense in modern science, namely the composition of water as H2O. Three main phases of development are critically re-examined, covering the historical period from the 1760s to the 1860s: the Chemical Revolution, early electrochemistry, and early atomic chemistry. In each case, the author concludes that the empirical evidence available at the time was not decisive in settling the (...) central debates and therefore the consensus that was reached was unjustified or at least premature. This leads to a significant re-examination of the realism question in the philosophy of science and a unique new advocacy for pluralism in science. Each chapter contains three layers, allowing readers to follow various parts of the book at their chosen level of depth and detail. The second major study in "complementary science", this book offers a rare combination of philosophy, history and science in a bid to improve scientific knowledge through history and philosophy of science. (shrink)

The literature on collective belief and collective intentionality has grown rapidly and is now quite extensive. Philosophers have applied the concepts of “collective belief” and “collective intentionality” in a variety of contexts, including political and legal contexts as well as scientific contexts, specifically to model the behavior of research teams and scientific specialties.

"--Richard Grandy, Rice University "This is the first compelling diagnosis of what has gone awry in the raging 'science wars.

Cognitive archaeologists attempt to infer the cognitive and cultural features of past hominins and their societies from the material record. This task faces the problem of _minimum necessary competence_: as the most sophisticated thinking of ancient hominins may have been in domains that leave no archaeological signature, it is safest to assume that tool production and use reflects only the lower boundary of cognitive capacities. Cognitive archaeology involves selecting a model from the cognitive sciences and then assessing some aspect of (...) the material record through that lens. We give examples to show that background theoretical commitments in cognitive science that inform those models lead to different minimum necessary competence results. This raises an important question: what principles should guide us in selecting a model from the cognitive sciences? We outline two complementary responses to this question. The first involves using _independent_ lines of evidence to converge on a particular capacity. This can then influence model choice. The second is a broader suggestion. Theoretical diversity is a good thing in science, but is only beneficial over a limited amount of time. According to recent modelling work, one way of limiting diversity is to introduce extreme priors. We argue that having a broad spectrum of views in the philosophy of cognitive science may actually help cognitive archaeologists address the problem of minimum necessary competence. (shrink)

This paper presents an account of how to evaluate formal models of science: models and simulations in social epistemology designed to draw normative conclusions about the social structure of scientific research. I argue that such models should be evaluated according to their representational and predictive accuracy. Using these criteria and comparisons with familiar models from science, I argue that most formal models of science are incapable of supporting normative conclusions.

Philosophy of science is expanding via the introduction of new digital data and tools for their analysis. The data comprise digitized published books and journal articles, as well as heretofore unpublished material such as images, archival text, notebooks, meeting notes, and programs. The growth in available data is matched by the extensive development of automated analysis tools. The variety of data sources and tools can be overwhelming. In this article, we survey the state of digital work in the philosophy of (...) science, showing what kinds of questions can be answered and how one can go about answering them. (shrink)

Those who comment on modern scientific institutions are often quick to praise institutional structures that leave scientists to their own devices. These comments reveal an underlying presumption that scientists do best when left alone—when they operate in what we call the ‘scientific state of nature’. Through computer simulation, we challenge this presumption by illustrating an inefficiency that arises in the scientific state of nature. This inefficiency suggests that one cannot simply presume that science is most efficient when institutional control is (...) absent. In some situations, actively encouraging unpopular, risky science would improve scientific outcomes. 1 Introduction2 Scientists and Bandits3 Choosing an ϵ4 Structure of Communication5 Discussion. (shrink)

"This is a wonderful book! In "How Scientists Explain Disease," Paul Thagard offers us a delightful essay combining science, its history, philosophy, and sociology.

Network analysis is increasingly appreciated as a methodology in the social sciences. In recent years, it is also receiving attention among historians of science. History of economics is no exception in that researchers have begun to use network analysis to study a variety of topics, including collaborations and interactions in scientific communities, the spread of economic theories within and across fields, or the formation of new specialties in the discipline of economics. Against this backdrop, a debate is emerging about how (...) network analysis can help address questions that are pertinent to the history of economics. With this paper, we want to push this debate one step forward by offering and discussing five reasons why network analysis should have a future in the history of economics. (shrink)

A new, social epistemology of science that addresses practical as well as theoretical concerns.

Modeling and computer simulations, we claim, should be considered core philosophical methods. More precisely, we will defend two theses. First, philosophers should use simulations for many of the same reasons we currently use thought experiments. In fact, simulations are superior to thought experiments in achieving some philosophical goals. Second, devising and coding computational models instill good philosophical habits of mind. Throughout the paper, we respond to the often implicit objection that computer modeling is “not philosophical.”.

This paper highlights some connections between work on truth approximation and work in social epistemology, in particular work on peer disagreement. In some of the literature on truth approximation, questions have been addressed concerning the efficiency of research strategies for approximating the truth. So far, social aspects of research strategies have not received any attention in this context. Recent findings in the field of opinion dynamics suggest that this is a mistake. How scientists exchange and take into account information about (...) each others’ beliefs may greatly influence the accuracy and speed with which the scientific community as a whole approximates the truth. On the other hand, social epistemologists concerned with peer disagreement have so far neglected the question of how practices of responding to disagreements with peers fare with respect to the goal of approximating the truth. Again, work on opinion dynamics shows that this may be a mistake, and that how we ought to respond to disagreements with our peers may depend on the specific purposes of our investigations. (shrink)

The literature on collective belief and collective intentionality has grown rapidly and is now quite extensive. Philosophers have applied the concepts of “collective belief” and “collective intentionality” in a variety of contexts, including political and legal contexts as well as scientific contexts, specifically to model the behavior of research teams and scientific specialties.