Several years ago, we wrote a paper that developed agent-based models designed to investigate the impacts of confirmation bias on group learning (Gabriel & O’Connor, 2024). In the current chapter we present a version of this model in detail.
Conventions have been universally defined, within philosophy, as emerging in social groups. The dominant tradition, starting with David Lewis, identifies conventions as arbitrary patterns of behavior that solve coordination problems within a group. This note is intended to argue that single individuals can, and very often do, develop conventions of the sort philosophers are interested in. These conventions are arbitrary patterns of behavior that facilitate coordination between past and future selves.
Failures of retraction are common in science. Why do they occur? And what determines whether a retraction is successful? We use data from citation records and Altmetrics to test proposed answers to these questions. LaCroix et al. (2021) employ network models to argue the social spread of information helps explain failures of retraction. One prediction is that widely known results, surprisingly, should be easier to retract, since their retraction is more relevant. Our results support this conclusion. We find highly cited papers show more significant reductions in citation after retraction and garner more attention to their retractions as they occur.
The values in science literature considers deep questions around value laden aspects of inquiry like: Where and when can values of different sorts legitimately influence science? What sorts of values, and related ethical practices, best promote scientific inquiry? How do individual beliefs and biases impact science? Earlier work in this area focused mostly on how values interact with individual practice. But increasingly there has been a move towards thinking about how the impacts of values can reverberate through scientific communities, and how the dynamics of social groups may lead to unexpected outcomes when values shape science. At the same time, social epistemologists and others have been using network models to think about how social network structures and group features shape knowledge generation. These models are often useful in thinking about the emergent, group-level effects of values on scientific progress. This paper will briefly survey this network literature and connect it with the literature on values in science.
Philosophers and economists are both interested in signaling games and information transmission, but they have taken very different approaches to these issues. Philosophers have mostly focused on common-interest Lewis signaling games, and especially on how simple learners can develop languages in these models, responding to skeptical positions on the emergence of conventional meaning. Economists focused more on strategic analysis of games with partial conflict of interest, like the Crawford-Sobel signaling game. There has been relatively little work on how simple learners might evolve to communicate in this latter context, though. Here we investigate learning in Crawford-Sobel games. While simple reinforcement often finds optimal equilibria in Lewis games, it does not tend to do so in Crawford-Sobel games. One might think this pushes against Lewis's response to skeptics about conventional meaning, given that Crawford-Sobel games are natural representations of many arenas of human communication. But we find that two other simple learning rules---act-based probe and adjust and threshold-sensitive reinforcement--- each do well in Crawford-Sobel games. This illustrates the robustness of simple learners in developing signaling conventions. It also shows the importance of forms of learning that track relative playoffs to forging successful conventions in complex, real-world situations.
What is the connection between capitalism and racial hierarchy? In line with the tradition known as ‘the theory of racial capitalism’ we show that the latter can functionally support the former. As a social construction, race has just those features which allow it to facilitate the sort of stable, inequitable distributions of resources that tend to emerge in capitalist systems. We support this claim using techniques from evolutionary game theory and cultural evolutionary theory, and end by discussing the normative political consequences of this relationship.
Cultural evolutionary models of bargaining can elucidate issues related to fairness and justice, and especially how fair and unfair conventions and norms might arise in human societies. One line of this research shows how the presence of social categories in such models creates inequitable equilibria that are not possible in models without social categories. This is taken to help explain why in human groups with social categories, inequity is the rule rather than the exception. But in previous models, it is typically assumed that these categories are rigid – in the sense that they cannot be altered, and easily observable – in the sense that all agents can identify each others’ category membership. In reality, social categories are not always so tidy. We introduce evolutionary models where the tags connected with social categories can be flexible, variable, or difficult to observe, i.e. where these tags can carry different amounts of information about group membership. We show how alterations to these tags can undermine the stability of unfair conventions. We argue that these results can inform projects intended to ameliorate inequity, especially projects that seek to alter the properties of tags by promoting experimentation, imitation, and play with identity markers.
There are myriad techniques industry actors use to shape the public understanding of science. While a naive view might assume these techniques typically involve fraud or outright deception, the truth is more nuanced. This paper analyzes industrial distraction, a common technique where industry actors fund and share research that is accurate, often high-quality, but nonetheless misleading on important matters of fact. This involves reshaping causal understanding of phenomena with distracting information. Using case studies and causal models, we illustrate how this impacts belief and decision making even for rational learners, informing science policy and debates about misleading content.
Confirmation bias has been widely studied for its role in failures of reasoning. Individuals exhibiting confirmation bias fail to engage with information that contradicts their current beliefs, and, as a result, can fail to abandon inaccurate beliefs. But although most investigations of confirmation bias focus on individual learning, human knowledge is typically developed within a social structure. We use network models to show that moderate confirmation bias often improves group learning. However, a downside is that a stronger form of confirmation bias can hurt the knowledge-producing capacity of the community.
The study of intellectual humility (IH), which is gaining increasing interest among cognitive scientists, has been dominated by a focus on individuals. We propose that IH operates at the collective level as the tendency of a collective’s members to attend to each other’s intellectual limitations and the limitations of their collective cognitive efforts. Given people’s propensity to better recognize others’ limitations than their own, IH may be more readily achievable in collectives than individuals. We describe the socio-cognitive dynamics that can interfere with collective IH and offer the solution of building intellectually humbling environments that create a culture of IH that can outlast the given membership of a collective. We conclude with promising research directions.
We review several topics of philosophical interest connected to misleading online content. First we consider proposed definitions of different types of misleading content. Then we consider the epistemology of misinformation, focusing on approaches from virtue epistemology and social epistemology. Finally we discuss how misinformation is related to belief polarization, and argue that models of rational polarization present special challenges for conceptualizing fake news and misinformation.
Abstract not yet available.
Many theorists have employed game theory to model the emergence of stable social norms, or natural “social contracts”. One branch of this literature uses bargaining games to show why many societies have norms and rules for fairness. In cultural evolutionary models, fair bargaining emerges endogenously because it is an efficient way to divide resources (Young, 1993a; Skyrms, 1996; Alexander, 2007). In response, a number of authors have argued that these models miss an important element of real human societies– divisions into groups or social categories. Once such groups are added to cultural evolutionary models, fairness is no longer the expected outcome. Instead “discriminatory norms” often emerge where one group systematically gets more when dividing resources (Axtell et al., 2001; O’Connor, 2019). These results may help explain why categorical inequity is the rule across human societies (Mills, 1997; Pateman, 1988). If one wishes to understand the naturalistic emergence of social contracts, one must account for the presence of categorical divisions, and unfairness, as well as for norms of fairness. This paper overviews this body of work, and pulls out lessons for social contract theory.
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?
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.
In this chapter, we offer a brief review of formal models in which science is treated as a cultural evolutionary system. The field of cultural evolution has a rich theoretical foundation. Cultural evolutionary models help elucidate how patterns of cultural behavior emerge via appeal to mechanisms of cultural transmission and social learning. Here we 1highlight learning and transmission processes particular to scientific communities. These include scientists choosing methods for hypothesis testing, competing for jobs and resources, learning from other experts, attempting to accrue credit for their work, and assigning credit for work done. Throughout, we connect our discussion to empirical research on the workings of science. Because this is a very recently developed area of cultural evolutionary science, though, there is relatively little new empirical work derived from these models. We hope they may inspire both future empirical investigations and further theoretical work on the cultural evolution of science.
Scientific curation, where scientific evidence is selected and shared, is essential to public belief formation about science. Yet common curation practices can distort the body of evidence the public sees. Focusing on science journalism, we employ computational models to investigate how such distortions influence public belief. We consider these effects for agents with and without confirmation bias. We find that standard journalistic practices can lead to significant distortions in public belief; that preexisting errors in public belief can drive further distortions in reporting; that practices that appear relatively unobjectionable can produce serious epistemic harm; and that, in some cases, common curation practices related to fairness and extreme reporting can lead to polarization.
Why do bad methods persist in some academic disciplines, even when they have been widely rejected in others? What factors allow good methodological advances to spread across disciplines? In this paper, we investigate some key features determining the success and failure of methodological spread between the sciences. We introduce a formal model that considers factors like methodological competence and reviewer bias toward one’s own methods. We show how these self-preferential biases can protect poor methodology within scientific communities, and lack of reviewer competence can contribute to failures to adopt better methods. We then use a second model to argue that input from outside disciplines can help break down barriers to methodological improvement. In doing so, we illustrate an underappreciated benefit of interdisciplinarity.
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 are not 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.
Philosophers of science and social scientists have argued that diverse perspectives, methods, and background assumptions are critical to the progress of science. One way to achieve such diversity is to ensure that a scientific community is made up of individuals from diverse personal backgrounds. In many scientific disciplines, though, minority groups are underrepresented. In some cases minority members further segregate into sub-fields, thus decreasing the effective diversity of research collaborations. In this paper, we employ agent-based, game theoretic models to investigate various types of initiatives aimed at improving the diversity of collaborative groups. This formal framework provides a platform to discuss the potential efficacy of these various proposals. As we point out, though, such proposals may have unintended negative consequences.
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? Here 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.
Effective political decision making requires actors to have accurate beliefs about the domain in which they are acting. But false beliefs about matters of fact are widespread. Such false beliefs are often explained by appeal to individual epistemic factors, such as personal reasoning biases. But these individual factors are only part of the story, as most of what we know, or believe, we have learned directly from other people. Recently, philosophers have begun to use formal methods, including mathematical models and computer simulations, to explore various aspects of social epistemology. This chapter will review the recent literature in the formal social epistemology of false belief. We introduce background work in social epistemology, explain how researchers use models to inform the science of false belief, and discuss what these models tell us about how politically or economically motivated actors shape public belief by exploiting social factors.
Standard accounts of convention include notions of arbitrariness. But many have conceived of conventionality as an all-or-nothing affair. In this paper, I develop a framework for thinking of conventions as admitting of degrees of arbitrariness. In doing so, I introduce an information-theoretic measure intended to capture the degree to which a solution to a certain social problem could have been otherwise. As the paper argues, this framework can help to improve explanation aimed at the cultural evolution of social traits. Good evolutionary explanations recognise that most functional traits are also conventional, at least to some degree, and vice versa.
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.
Abstract not yet available.
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.
This white paper came out of an exploratory workshop held on November 15-16, 2019 at the Institute for Pure and Applied Mathematics at UCLA. Represented at the workshop were members of the mathematics, machine learning, cryptography, philosophy, social science, legal, and policy communities. Discussion at the workshop focused on the impact of deep fakery and how to respond to it. The opinions expressed in this white paper represent those of the individuals involved, and not of their organizations or of the Institute for Pure and Applied Mathematics. “Deep fake” technology represents a substantial advance on earlier technologies of image, audio, and video manipulation like photoshopping. It emerged from the recent deep learning revolution, especially the development of generative adversarial networks. It enables the efficient, computer-assisted production of highly believable audio and video in which real people appear to be saying things they never said and doing things they never did.
Recognizing the need for multifaceted solutions to the issue of the legitimacy and acceptance of fair election results in the United States, Richard L. Hasen, Chancellor’s Professor of Law and Political Science at UC Irvine, convened both a conference and an ad hoc committee made up of a diverse group of leading scholars and leaders to tackle this issue from an interdisciplinary perspective. After public meetings and further online deliberations, this Committee makes the following fourteen recommendations for immediate change that should be implemented to increase voter confidence in the fairness and legitimacy of the 2020 elections. These recommendations listed below call for specific action from: journalists and editors deciding on headlines, what, and how to cover the election up to and including the election night itself; tech companies in the fray; legislators from federal to state to local levels; and nonprofits, citizens, and social media influencers:
When used as an information source, social media have been found to present a health risk that is partly due to their role as disseminators of health-related conspiracies, with non-English language speakers being at greater risk of exposure to misinformation during the crisis. It is likely that these technologies will have a long-lasting impact beyond COVID-19. Yet despite the immediacy of the crisis, the authors invite the reader to take a longer perspective on technology and democracy to get a deeper understanding of the interrelated nuances. In dark times, we seek to bring light to the importance of understanding the influence of online technologies on political behaviour and decision-making.
Abstract not yet available.
Communication can arise when the interests of speaker and listener diverge if the cost of signaling is high enough that it aligns their interests. But what happens when the cost of signaling is not sufficient to align their interests? Using methods from experimental economics, we test whether theoretical predictions of a partially informative system of communication are borne out. As our results indicate, partial communication can occur even when interests do not coincide.
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.
This version was updated in 2023 to correct a small error.
Abstract not yet available.
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?
Social epistemologists have argued that high risk, high reward science has an important role to play in scientific communities. Recently, though, it has also been argued that various scientific fields seem to be trending towards conservatism—the increasing production of what Kuhn (1962) might have called ‘normal science’. This paper will explore a possible explanation for this sort of trend: that the process by which scientific research groups form, grow, and dissolve might be inherently hostile to such science. In particular, I employ a paradigm developed by Smaldino and McElreath (2016) that treats a scientific community as a population undergoing selection. As will become clear, perhaps counter-intuitively this sort of process in some ways promotes high risk, high reward science. But, as I will point out, risky science is, in general, the sort of thing that is hard to repeat. While more conservative scientists will be able to train students capable of continuing their successful projects, and so create thriving lineages, successful risky science may not be the sort of thing one can easily pass on. In such cases, the structure of scientific communities selects against high risk, high rewards projects. More generally, this project makes clear that there are at least two processes to consider in thinking about how incentives shape scientific communities—the process by which individual scientists make choices about their careers and research, and the selective process governing the formation of new research groups.
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.
Recently, game theory and evolutionary game theory – mathematical frameworks from economics and biology designed to model and explain interactive behavior – have proved fruitful tools for philosophers in areas such as ethics, philosophy of language, social epistemology, and political philosophy. This methodological osmosis is part of a trend where philosophers have blurred disciplinary lines to import the best epistemic tools available. In this vein, experimental philosophers have drawn on practices from the social sciences, and especially from psychology, to expand philosophy's grasp on issues from morality to consciousness. We argue that the recent prevalence of formal work on human interaction in philosophy opens the door for new methods in experimental philosophy. In particular, we discuss methods from experimental economics, focusing on a small literature we have been developing investigating signaling and communication in humans. We describe results from a novel experiment showing how environmental structure can shape signaling behavior.
The study of social justice asks: what sorts of social arrangements are equitable ones? But also: how do we derive the inequitable arrangements we often observe in human societies? In particular, in spite of explicitly stated equity norms, categorical inequity tends to be the rule rather than the exception. The cultural Red King hypothesis predicts that differentials in group size may lead to inequitable outcomes for minority groups even in the absence of explicit or implicit bias. We test this prediction in an experimental context where subjects divided into groups engage in repeated play of a bargaining game. We ran 14 trials involving a total of 112 participants. The results of the experiments are statistically significant and suggestive: individuals in minority groups in these experiments end up receiving fewer resources than those in majority groups. Combined with previous theoretical findings, these results give some reason to think that the cultural Red King may occur in real human groups.
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.
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.
Abstract not yet available.
Contemporary societies are often “polarized”, in the sense that sub-groups within these societies hold stably opposing beliefs, even when there is a fact of the matter. Extant models of polarization do not capture the idea that some beliefs are true and others false. Here we present a model, based on the network epistemology framework of Bala and Goyal (1998), in which polarization emerges even though agents gather evidence about their beliefs, and true belief yields a pay-off advantage. As we discuss, these results are especially relevant to polarization in scientific communities, for these reasons. The key mechanism that generates polarization involves treating evidence generated by other agents as uncertain when their beliefs are relatively different from one’s own.
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.
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.
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.
This version was updated in 2019 to correct a small error.
Bruner (2017) 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.
Collaboration is increasingly popular across academia. Collaborative work raises certain ethical questions, however. How will the fruits of collaboration be divided? How will academics divide collaborative labor? In this paper, we consider the following question in particular. Are there ways in which these divisions systematically disadvantage certain groups? We use evolutionary game theoretic models to address this question. First, we discuss results from O’Connor and Bruner (2015) showing that underrepresented groups in academia can be disadvantaged in collaboration and bargaining by dint of their small numbers. Second, we present novel results exploring how the hierarchical structure of academia can lead to bargaining disadvantage. We investigate models where one actor has a higher baseline of academic success, less to lose if collaboration goes south, or greater rewards for non-collaborative work. We show that in these situations, the less powerful partner can be disadvantaged in bargaining over collaboration.
Abstract not yet available.
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 will not be evolutionarily successful (Harley [1981]; Maynard-Smith [1982]). This claim, however, stands at odds with the fact that learning generalization—a behaviour that cannot lead to ESSes when modelled in games—is observed throughout the animal kingdom (Mednick and Freedman [1960]). In this article, 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 sub-optimal behaviour—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.
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.
As I will point out, there are three main sets of results in evolutionary game theory that shed light on the evolution of guilt. First, work on altruism in the prisoner’s dilemma game indicates that in environments where actors engage in reciprocation and punishment, guilt can provide individual benefits by promoting altruism. Second, work on the stag hunt shows that when actors are in groups of relatively cooperative partners, and especially when they are engaged in repeated interactions with neighbors, guilt can promote fitness by leading to cooperation. And lastly, results on costly apology show that, perhaps unintuitively, paying costs can allow actors to successfully apologize and to reap the cooperative benefits of doing so. These findings do not fully explain the evolution of guilt, but they do clarify the conditions under which it provides evolutionary benefits.
Abstract not yet available.
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.
In a recent article, Carlos Santana shows that in common interest signaling games when signals are costly and when receivers can observe contextual environmental cues, ambiguous signaling strategies outperform precise ones and can, as a result, evolve. I show that if one assumes a realistic structure on the state space of a common interest signaling game, ambiguous strategies can be explained without appeal to contextual cues. I conclude by arguing that there are multiple types of cases of payoff-beneficial ambiguity, some of which are better explained by Santana’s models and some of which are better explained by models presented here.
In this paper we use an experimental approach to investigate how linguistic conventions can emerge in a society without explicit agreement. As a starting point we consider the signaling game introduced by Lewis (1969). We find that in experimental settings, small groups can quickly develop conventions of signal meaning in these games. We also investigate versions of the game where the theoretical literature indicates that meaning will be less likely to arise—when there are more than two states for actors to transfer meaning about and when some states are more likely than others. In these cases, we find that actors are less likely to arrive at strategies where signals have clear conventional meaning. We conclude with a proposal for extending the use of the methodology of experimental economics in experimental philosophy.
Vague predicates, those that exhibit borderline cases, pose a persistent problem for philosophers and logicians. Although they are ubiquitous in natural language, when used in a logical context, vague predicates lead to contradiction. This paper will address a question that is intimately related to this problem. Given their inherent imprecision, why do vague predicates arise in the first place? I discuss a variation of the signaling game where the state space is treated as contiguous, i.e., endowed with a metric that captures a similarity relation over states. This added structure is manifested in payoffs that reward approximate coordination between sender and receiver as well as perfect coordination. I evolve these games using a variation of Herrnstein reinforcement learning that better reflects the generalizing learning strategies real-world actors use in situations where states of the world are similar. In these simulations, signaling can develop very quickly, and the signals are vague in much the way ordinary language predicates are vague—they each exclusively apply to certain items, but for some transition period both signals apply to varying degrees. Moreover, I show that under certain parameter values, in particular when state spaces are large and time is limited, learning generalization of this sort yields strategies with higher payoffs than standard Herrnstein reinforcement learning. These models may then help explain why the phenomenon of vagueness arises in natural language: the learning strategies that allow actors to quickly and effectively develop signaling conventions in contiguous state spaces make it unavoidable.
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.
A catalyst meeting on sexual selection studies was held in July 2013 at the facilities of the National Evolutionary Synthesis Center (NESCent) in Durham, NC. This article by a subcommittee of the participants foregrounds some of the topics discussed at the meeting. Topics mentioned here include the relevance of heritability estimates to assessing the presence of sexual selection, whether sexual selection is distinct from natural selection, and the utility of distinguishing sexual selection from fecundity selection. A possible definition of sexual selection is offered based on a distinction between sexual selection as a frequency-dependent process and fecundity selection as a density-dependent process. Another topic highlighted is a deep disagreement among participants in the reality of good-genes, sexy-sons, and run-away processes. Finally, the status of conflict in political-economic theory is contrasted with the status accorded to conflict in evolutionary behavioral theory, and the professional responsibility of sexual-selection workers to consider the ethical dimension of their research is underscored.
In this paper we critically examine and seek to extend Philip Kitcher’s Ethical Project to weave together a distinctive naturalistic conception of how ethics came to occupy the place it does in our lives and how the existing ethical project should be revised and extended into the future. Although we endorse his insight that ethical progress is better conceived of as the improvement of an existing state than an incremental approach towards a fixed endpoint, we nonetheless go on to argue that the metaethical apparatus Kitcher constructs around this creative metaethical proposal simply cannot do the work that he demands of it. The prospect of fundamental conflict between different functions of the ethical project requires Kitcher to appeal to a particular normative stance in order to judge specific changes in the ethical project to be genuinely progressive, and we argue that the virtues of continuity and coherence to which he appeals can only specify rather than justify the normative stance he favors. We conclude by suggesting an alternative approach for ethical naturalists that seems to us ultimately more promising than Kitcher’s own.
Abstract not yet available.