Cailin O'Connor

In Epistemic Networks, Is Less Really More?

We show that previous results from epistemic network models by Kevin J. S. Zollman and Erich Kummerfeld 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.

Evolving Perceptual Categories

This article uses sim-max games to model perceptual categorization with the goal of answering the following question: To what degree should we expect the perceptual categories of biological actors to track properties of the world around them? I argue that an analysis of these games suggests that the relationship between real-world structure and evolved perceptual categories is mediated by successful action in the sense that organisms evolve to categorize together states of nature for which similar actions lead to similar results. This conclusion indicates that both strongly realist and strongly antirealist views about perceptual categories are too simple.

Black Holes, Black Scholes, and Prairie Voles: an Essay Review of Simulation and Similarity by Michael Weisberg

Michael Weisberg’s Simulation and Similarity reflects the state of the art in the philosophical literature on scientific models. It presents an account of modeling that aims to accommodate essentially all examples of scientific models discussed in the extant philosophical literature, and many more besides. The view that results is pluralist, in the sense that Weisberg tends to divide and conquer. He recognizes three broad classes of models, and within each class, he identifies multiple sub-classes. Cutting across these classes, he draws distinctions between “targeted” and “untargeted” modeling practices, and analyzes these separately. Cutting in yet another direction, he identifies three varieties of idealization, which he argues function in different ways, but all of which involve some kind of distortion of the model with respect to its target. Many of these distinctions are intended to broaden the tent, to draw attention to features of the practice that others have neglected: the philosophical literature, he argues, has tended to focus on a few kinds of model, rather than addressing the breadth of modeling practice. All of this is to the good: drawing attention to the richness and diversity of models in the wild is a valuable contribution in itself, and faced with this diversity, his pluralism serves him well. There are also striking successes in his attempts to regiment the subject. Indeed, the framework he has devised has already given form to the modeling literature in the wake of the book. He has set the terms for future work on this subject. Perhaps it goes without saying that this is a major accomplishment on two counts: the book succeeds in its goal of

The cultural Red King effect

ABSTRACT Why do minority groups tend to be discriminated against when it comes to situations of bargaining and resource division? In this article, I explore an explanation for this disadvantage that appeals solely to the dynamics of social interaction between minority and majority groups—the cultural Red King effect (Bruner, 2017). As I show, in agent-based models of bargaining between groups, the minority group will tend to get less as a direct result of the fact that they frequently interact with majority group members, while majority group members meet them only rarely. This effect is strengthened by certain psychological phenomenon—risk aversion and in-group preference—is robust on network models, and is strengthened in cases where preexisting norms are discriminatory. I will also discuss how this effect unifies previous results on the impacts of institutional memory on bargaining between groups.

How to Beat Science and Influence People: Policy Makers and Propaganda in Epistemic Networks

In their recent book, Oreskes and Conway ([2010]) describe the ‘tobacco strategy’, which was used by the tobacco industry to influence policymakers regarding the health risks of tobacco products. The strategy involved two parts, consisting of (i) promoting and sharing independent research supporting the industry’s preferred position and (ii) funding additional research, but selectively publishing the results. We introduce a model of the tobacco strategy, and use it to argue that both prongs of the strategy can be extremely effective—even when policymakers rationally update on all evidence available to them. As we elaborate, this model helps illustrate the conditions under which the tobacco strategy is particularly successful. In addition, we show how journalists engaged in ‘fair’ reporting can inadvertently mimic the effects of industry on public belief. 1. Introduction2. Epistemic Network Models3. Selective Sharing4. Biased Production5. Journalists as Unwitting Propagandists6. Conclusion Appendix Introduction Epistemic Network Models Selective Sharing Biased Production Journalists as Unwitting Propagandists Conclusion Appendix

Games and Kinds

In response to those who argue for ‘property cluster’ views of natural kinds, I use evolutionary models of similarity-maximizing games to assess the claim that linguistic terms appropriately track sets of objects that cluster in property spaces. As I show, there are two sorts of ways this can fail to happen. First, evolved terms that do respect property structure in some senses can be conventional nonetheless. Second, and more crucially, because the function of linguistic terms is to facilitate successful action in the world, when such success is based on something other than property clusters, we should not expect our terms to track those clusters. The models help make this second point salient by highlighting a dubious assumption underlying some versions of the cluster kinds view—that property clusters lead to successful generalization and induction in a straightforward way. As I point out, those who support property cluster kinds as natural can revert to a promiscuous realism in response to these observations. 1. Introduction2. Cluster Kinds3. Models and Results 3.1. Similarity-maximizing games3.2. Voronoi languages, clusters, and categories4. Conventionality and Functionality in Kind Terms 4.1. Conventional categories4.2. Functionality and categories5. Unnatural Perceptual Categories6. The Payoff Relevance of Property Space7. Conclusion Introduction Cluster Kinds Models and Results 3.1. Similarity-maximizing games3.2. Voronoi languages, clusters, and categories Similarity-maximizing games Voronoi languages, clusters, and categories Conventionality and Functionality in Kind Terms 4.1. Conventional categories4.2. Functionality and categories Conventional categories Functionality and categories Unnatural Perceptual Categories The Payoff Relevance of Property Space Conclusion

Discrimination and Collaboration in Science

We use game theoretic models to take an in-depth look at the dynamics of discrimination and academic collaboration. We find that in collaboration networks, small minority groups may be more likely to end up being discriminated against while collaborating. We also find that discrimination can lead members of different social groups to mostly collaborate with in-group members, decreasing the effective diversity of the social network. Drawing on previous work, we discuss how decreases in the diversity of scientific collaborations might negatively affect the progress of epistemic communities.

When it's Good to Feel Bad: an evolutionary model of guilt and apology

We use techniques from evolutionary game theory to analyze the conditions under which guilt can provide individual fitness benefits, and so evolve. In particular, we focus on the benefits of guilty apology. We consider models where actors err in an iterated prisoner’s dilemma and have the option to apologize. Guilt either improves the trustworthiness of apology or imposes a cost on actors who apologize. We analyze the stability and likelihood of evolution of such a “guilt-prone” strategy against cooperators, defectors, grim triggers, and individuals who offer fake apologies, but continue to defect. We find that in evolutionary models guilty apology is more likely to evolve in cases where actors interact repeatedly over long periods of time, where the costs of apology are low or moderate, and where guilt is hard to fake. Researchers interested in naturalized ethics, and emotion researchers, can employ these results to assess the plausibility of fuller accounts of the evolution of guilt.

Philosophy of Biology

Review of Peter Godfrey-Smith’s Philosophy of Biology Cailin O’Connor March 9, 2015 In Philosophy of Biology, Peter Godfrey-Smith provides an introductory overview of some of the most important areas in the field. Each chapter of the book focuses on a major research topic (or cluster of topics) in philosophy of biology. Since most readers here will be interested in the book for teaching purposes, this review will outline the main topics of the book and then briefly discuss its merits as a course text. Godfrey-Smith begins with a discussion of laws, mechanisms and models in biology. As he points out, philosophers of biology have largely rejected the idea that biological knowledge is usefully thought of as law-like. It is famously difficult to make broad gener- alizations about the biological world—exceptions abound. 1 Recent attempts to account for biological knowledge have instead focused on mechanistic explanation (especially in molecular and neurobiology) or on mathetmatical modeling (especially in evolutionary biology and ecology). Godfrey-Smith seems to support a pluralistic view of biological knowledge where ‘resilient’ or ‘stable’ generalizations can take the place of biological laws and where both mechanistic and mathematical analyses can generate genuine biological knowledge. Godfrey-Smith moves on to discuss evolution by natural selection. He lays out sev- eral influential accounts of evolutionary change rather than committing to one. As he points out, any such account that is simple and predictive will struggle with difficult cases, whereas accounts that attempt to cover all interesting cases of change by natural selection will be trivial. Godfrey-Smith’s discussion of fitness concepts in this chapter is particularly clear. He distinguishes those that focus on the actual structure of an organ- ism as embodying its fitness and those that are based on offspring count, including both propensity definitions and definitions that simply associate fitness with actual offspring produced. He finishes the chapter with a very brief overview of work on levels of selection and on Darwinian concepts as applied to non-biological realms. In fact, researchers have just discovered an exception to the central dogma of molecular biology: that codes for amino acid sequences are derived from DNA and RNA (Shen et al. 2015).