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The COVID-19 pandemic created enormously difficult decisions for individuals trying to navigate both the risks of the pandemic and the demands of everyday life. Good decision making in such scenarios can have life and death consequences. For this reason, it is important to understand what drives risk assessments during a pandemic, and to investigate the ways that these assessments might deviate from ideal risk assessments. In a preregistered online study of U.S. residents (N = 841) using two blocks of vignettes about potential COVID exposure scenarios, we investigated the effects of moral judgment, importance, and intentionality on COVID infection risk assessments. Results demonstrate that risk judgments are sensitive to factors unrelated to the objective risks of infection. Specifically, activities that are morally justified are perceived as safer while those that might subject people to blame or culpability, are seen as riskier, even when holding objective risk fixed. Similarly, unintentional COVID exposures are judged as safer than intentional COVID exposures. While the effect sizes are small, these findings may have implications for public health and risk communications, particularly if public health officials are themselves subject to these biases.

We use tools from evolutionary game theory to examine how power might influence the cultural evolution of inequitable conventions between discernible groups (such as gender or racial groups) in a population of otherwise identical individuals. Similar extant models always assume that power is homogeneous across a social group. As such, these models fail to capture situations where individuals who arenot themselves disempowered nonetheless end up disadvantaged in bargaining scenarios by dint of their social group membership. Our models show that even when most individuals in two discernible sub-groups are relevantly identical, powerful individuals can affect the social outcomes for their entire group under a range of conditions; this results in power by association for their in-group and a bargaining disadvantage for their out-group.

Sometimes retracted or refuted scientific information is used and propagated long after it is understood to be misleading. Likewise, retracted news items may spread and persist, despite being publicly established as false. In this article, we explore the dynamics of retraction using agent-based models of epistemic networks, to see why false beliefs might persist despite retraction. We find that, often, those who received false information simply fail to receive retractions because of social dynamics. Surprisingly, delaying retraction can increase its impact. Further, retractions are most successful when issued by the original source of misinformation, rather than a separate source.

Why are we good? Why are we bad? Questions regarding the evolution of morality have spurred an astoundingly large interdisciplinary literature. Some significant subset of this body of work addresses questions regarding our moral psychology: how did humans evolve the psychological properties which underpin our systems of ethics and morality? In this chapter I do three things. First, I discuss some methodological issues, and defend particularly effective methods for addressing many research questions in this area. Second, I give an in-depth example, describing how an explanation can be given for the evolution of guilt—one of the core moral emotions—using the methods advocated here. Last, I lay out which sorts of strategic scenarios generally are the ones that our moral psychology evolved to ‘solve’, and thus which models are the most useful in further exploring this evolution.

Why do people who disagree about one subject tend to disagree about other subjects as well? In this paper, we introduce a model to explore this phenomenon of ‘epistemic factionization’. Agents attempt to discover the truth about multiple propositions by testing the world and sharing evidence gathered. But agents tend to mistrust evidence shared by those who do not hold similar beliefs. This mistrust leads to the endogenous emergence of factions of agents with multiple, highly correlated, polarized beliefs.

Scientists are generally subject to social pressures, including pressures to conform with others in their communities, that affect achievement of their epistemic goals. Here we analyze a network epistemology model in which agents, all else being equal, prefer to take actions that conform with those of their neighbors. This preference for conformity interacts with the agents’ beliefs about which of two (or more) possible actions yields the better result. We find a range of possible outcomes, including stable polarization in belief and action. The model results are sensitive to network structure. In general, though, conformity has a negative effect on a community’s ability to reach accurate consensus about the world.

Diversity of practice is widely recognized as crucial to scientific progress. If all scientists perform the same tests in their research, they might miss important insights that other tests would yield. If all scientists adhere to the same theories, they might fail to explore other options which, in turn, might be superior. But the mechanisms that lead to this sort of diversity can also generate epistemic harms when scientific communities fail to reach swift consensus on successful theories. In this paper, we draw on extant literature using network models to investigate diversity in science. We evaluate different mechanisms from the modeling literature that can promote transient diversity of practice, keeping in mind ethical and practical constraints posed by real epistemic communities. We ask: what are the best ways to promote an appropriate amount of diversity of practice in scientific communities?

This panel brings experts together from law, philosophy, computer science and media studies to explore how digital platforms exercise power over which content is visible online, and which content is promoted to users, with a special focus on the use of algorithmic systems to achieve these ends.

In response to those who argue for `property cluster' views of natural kinds, I use evolutionary models of sim-max games to assess the claim that linguistic terms will 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.

Many societies have norms of equity – that those who make symmetric social contributions deserve symmetric rewards. Despite this, there are widespread patterns of social inequity, especially along gender and racial lines. It is often the case that members of certain social groups receive greater rewards per contribution than others. In this article, we draw on evolutionary game theory to show that the emergence of this sort of convention is far from surprising. In simple cultural evolutionary models, inequity is much more likely to emerge than equity, despite the presence of stable, equitable outcomes that groups might instead learn. As we outline, social groups provide a way to break symmetry between actors in determining both contribution and reward in joint projects.

Intersectionality theory explores the peculiar disadvantages that arise as the result of occupying multiple disadvantaged demographic categories. Addressing intersectionality theory through quantitative methods has proven difficult. Concerns have been raised about the sample size one would need in order to responsibly tease out evidence for the claims of intersectionality theorists. What is more, theorists have expressed concern about our ability to formulate novel intersectional hypotheses in a non-ad-hoc manner. We argue that simulation methods can help address these, and other, methodological problems, because they can generate novel hypotheses about causal dependencies in a relatively cheap way, and can thus guide future empirical work. We illustrate this point using models which show that intersectional oppression can arise under conditions where social groups are disadvantaged in the emergence of bargaining norms. As we show, intersectional disadvantage can arise even when actors from all social categories are completely identical in terms of preferences and abilities. And when actors behave in ways that reflect stronger intersectional identities, the potential for disadvantage increases. As we note, this exploration illustrates the usefulness of idealized models to real-world inquiry.

The COVID-19 pandemic has created enormously difficult decisions for individuals trying to navigate both the risks of the pandemic and the demands of everyday life. Good decision making in such scenarios can have life and death consequences. For this reason, it is important to understand what drives risk assessments during a pandemic, and, in particular, to investigate the ways that these assessments might deviate from ideal risk assessments. Two studies (N = 841) investigate risk judgments related to COVID-19. The results indicate that risk judgments are sensitive to factors unrelated to the objective risks of infection. In particular, activities that are morally justified are perceived as safer while those that might subject people to blame, or culpability, are seen as riskier.

In their recent book Merchants of Doubt [New York:Bloomsbury 2010], Naomi Oreskes and Erik Conway describe the "tobacco strategy" , which was used by the tobacco industry to influence policy makers regarding the health risks of tobacco products. The strategy involved two parts, consisting of (1) promoting and sharing independent research supporting the industry's preferred position and (2) 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 policy makers 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.

This is an Element surveying the most important literature using game theory and evolutionary game theory to shed light on questions in the philosophy of biology. There are two branches of literature that the book focuses on. It begins with a short introduction to game theory and evolutionary game theory. It then turns to working using signaling games to explore questions related to communication, meaning, language, and reference. The second part of the book addresses prosociality - strategic behavior that contributes to the successful functioning of social groups - using the prisoner's dilemma, stag hunt, and bargaining games.

We use models and historical cases to try and understand some of the ways scientific beliefs can go wrong. In particular, we consider questions like: how do conformity and social trust influence the spread of beliefs? What is the ideal network structure for a scientific community? And how do industrial propagandists influence the progress of science, as well as public belief?

Bruner (Synthese, 2017, https://doi.org/10.1007/s11229-017-1487-8 ) shows that in cultural interactions, members of minority groups will learn to interact with members of majority groups more quickly—minorities tend to meet majorities more often as a brute fact of their respective numbers—and, as a result, may come to be disadvantaged in situations where they divide resources. In this paper, we discuss the implications of this effect for epistemic communities. We use evolutionary game theoretic methods to show that minority groups can end up disadvantaged in academic interactions like bargaining and collaboration as a result of this effect. These outcomes are more likely, in our models, the smaller the minority group. They occur despite assumptions that majority and minority groups do not differ with respect to skill level, personality, preference, or competence of any sort. Furthermore, as we will argue, these disadvantaged outcomes for minority groups may negatively impact the progress of epistemic communities.

Biologists and philosophers of biology have argued that learning rules that do not lead organisms to play evolutionarily stable strategies (ESSes) in games will not be stable and thus not evolutionarily successful. This claim, however, stands at odds with the fact that learning generalization---a behavior that cannot lead to ESSes when modeled in games---is observed throughout the animal kingdom. In this paper, I use learning generalization to illustrate how previous analyses of the evolution of learning have gone wrong. It has been widely argued that the function of learning generalization is to allow for swift learning about novel stimuli. I show that in evolutionary game theoretic models learning generalization, despite leading to suboptimal behavior, can indeed speed learning. I further observe that previous analyses of the evolution of learning ignored the short term success of learning rules. If one drops this assumption, I argue, it can be shown that learning generalization will be expected to evolve in these models. I also use this analysis to show how ESS methodology can be misleading, and to reject previous justifications about ESS play derived from analyses of learning.

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.

Using evolutionary game theory, I consider how guilt can provide individual fitness benefits to actors both before and after bad behavior. This supplements recent work by philosophers on the evolution of guilt with a more complete picture of the relevant selection pressures.

This chapter shows why social categories provide solutions to certain coordination problems. It begins with a description of what social categories are and how they function in human groups. The chapter moves on to describe what social categories look like in game theoretic models. As becomes clear, a group with categories will often be able to achieve a level of efficiency unavailable to a group without them. There is a catch, though. The efficiency gained by adding social categories to a group often comes at the expense of egalitarianism. Groups that use social categories to solve coordination problems often display inequity in that joint action with unequal roles leads to unequal benefits for those involved.

The chapter starts with an introduction of the primary paradigm used in this half of the book—the bargaining game. It uses this model to show why in groups with social categories fairness in bargaining is not the expected outcome of cultural evolution. Instead, social categories act as a symmetry breaker that stabilizes inequitable bargaining conventions. The chapter then turns to the role power plays in the evolution of bargaining. Powerful groups often gain an advantage with respect to the emergence of conventions of resource division. This can lead to compounding processes that profoundly disadvantage some social groups. These models make especially clear how irrelevant markers like race and gender can come to be more important in determining resource division than relevant factors, such as individual status.

This chapter first presents an explicitly evolutionary model of the emergence of coordination in modern households. The chapter shows why certain conditions might favor market labor for one gender and home labor for the other. The goal is to provide a proof of concept for the usefulness of evolutionary models in this domain, as opposed to traditional game theoretic models. The chapter also argues that once these patterns have emerged, they should be relatively stable in the face of changing social conditions. Using these patterns of coordination as a starting point, the chapter then shows why emerging patterns of household bargaining, i.e., over who does more total work, and has more total leisure time, should favor whichever gender tends to be employed in market work.

The chapter opens with a brief discussion of gender and gendered division of labor. A general discussion follows of what coordination problems are and the models used to represent them–coordination games—are introduced. It is argued that not all coordination games are equal. While some can be solved by conventions and norms that are identical for everyone in a society, others, those that require people to take different, complementary actions, pose a special problem. Coordinating behavior in these sorts of games requires extra information to break symmetry between those who are interacting. Gendered division of labor is just such a scenario.

Imagine you are in a group of ten people. In a minute, you will all be randomly paired with a partner. At the count of three, without a chance to talk or communicate in any way, you must dance the tango. If you both step forward, you’ll collide. If you both step back, you’ll look stupid. If one of you steps forward, and the other back, you’ll do the dance successfully. This is an example of a coordination problem—a situation where actors have similar interests but nonetheless face difficulties in coordinating their action. Presumably neither you nor your partner really cares which one of you steps forward and which back, at least not as much as you care about executing complementary actions. In other words, what you really care about is coordination....

The goal of this chapter is to use the framework developed thus far in the book to address the question: how do discriminatory conventions and norms influence patterns of interaction? In particular, the models presented will highlight causal processes that can lead to homophily, or disproportionate in-group interaction, as a result of discrimination. The chapter uses academic communities as a case study throughout. Actors are modeled on interactive networks, where they prefer to link with partners who yield higher payoffs. The suggestion is that when women get less academic credit, they learn to avoid collaborating with men as a result. However, when certain groups have advantages that lead to more significant production of credit, this trend can reverse. In these cases, a disadvantaged group may be willing to share credit inequitably to have access to a collaborator who generates more credit.

In this chapter, I briefly summarize the main messages of the book. Gender can emerge to solve coordination problems. This sets the stage for the emergence of inequitable roles. Social categories, combined with cultural learning, lead to persistently inequitable norms and conventions. And understanding this process can help us intervene on it.

This chapter looks at evolutionary models capable of representing the emergence of conventions between actors in different social groups. From a starting point of uncoordinated behavior, groups move toward states where everyone follows unified patterns in a way that tends to lead to successful outcomes. This cultural evolution is driven by social learning. Once groups have arrived at these coordinated behaviors they tend to remain there, so that the patterns of behavior persist over time. As the chapter shows, the addition of social categories radically changes the cultural evolutionary process. In particular, groups with categories reach the inequitable, but efficient, outcomes described in the last chapter.

This chapter looks at a cluster of topics centered on the question: what can be done about inequitable conventions of resource division between social categories? First, the chapter outlines which social preconditions that must be in place in order for inequitable type-based conventions to arise at all. These include the recognition of social categories, and conditioning of behavior based on these categories. There is then a discussion of the possibility of eliminating these preconditions. The chapter moves on to consider how one might go about moving from inequitable to equitable norms for division of resources. Ultimately, the picture is one in which social justice is an endless battle. The forces of cultural evolution pull populations toward inequity, and combating these forces requires constant vigilance.

When cultural groups evolve, or adapt, at different rates, or more generally show asymmetric levels of reactivity towards each other, we can observe a cultural Red King effect. This occurs when a minority group ends up disadvantaged simply by virtue of their size. When a minority group interacts with a majority group, minority types meet out-group members much more often than majority types do by dint of the size differences between the groups. As a result the minority group will learn to interact with the majority more quickly, often leading to a bargaining disadvantage. As this chapter shows, the resulting effect is analogous to one that can occur between coevolving biological species. The chapter explores this effect and where it might matter to real-world bargaining. In addition, the chapter looks at the possibility of a cultural Red King in intersectional populations.

The central aim of this book is to explore the ways in which social categories—especially gender, but also categories like race and religion—interact with and contribute to social solutions to problems of coordination and resource division. In particular, this book uses formal frameworks—game theory and evolutionary game theory—to explore the cultural evolution of conventions that piggyback on seemingly irrelevant factors like gender and race. As I argue, these frameworks elucidate a variety of topics. In particular, these frameworks help show how inequity can emerge from simple processes of cultural change. In groups with gender and racial categories, the process of learning conventions of coordination and resource division is such that under a wide array of situations some groups will tend to get more and others less. One theme that runs throughout the book is that surprisingly minimal conditions are needed to robustly produce phenomena related to inequity that we usually think of as psychologically complex. It takes very little to generate a situation in which social categories (like gender) are almost guaranteed to emerge. The preconditions under which models move toward outcomes that look like discrimination are, again, very minimal. Once inequity emerges in these models, it takes very little for it to persist indefinitely. Thus, we need to think of inequity as part of an ever-evolving process. It is not something we can expect to fix and be done with. Along these lines, the picture I present is ultimately one where those concerned with social justice must remain vigilant against the dynamic forces that push toward inequity.