Ryan Muldoon

Existing models of the division of cognitive labor in science assume that scientists have a particular problem they want to solve and can choose between different approaches to solving the problem. In this essay I invert the approach, supposing that scientists have fixed skills and seek problems to solve. This allows for a better explanation of increasing rates of cooperation in science, as well as flows of scientists between fields of inquiry. By increasing the realism of the model, we gain additional insight into the social structure of science and gain the ability to ask new questions about the optimal division of labor.

We all want to change the world for the better. But we face myriad complex questions, which together compose "the problem of social change." Among these is the question of orientation: Should our efforts to achieve social change be systematically oriented toward a long-term ideal? Should we instead focus on making piecemeal improvements without any definite long-term target in sight? Existing debates have revealed a basic trade-off: moving toward an ideal may require us to make short-term normative sacrifices, while steady piecemeal improvements may make it more difficult to realize an ideal. We introduce an analytical framework for structuring thought experiments that can provide traction on these issues, and present two implementations that provide baseline insights and motivate further research. We conclude with suggestions for extending our framework in ways that can yield insights that can guide our choice of orientation. (The appendix is available on Wiens's PhilPeople page under "Teaching Materials")

Norms hold a central role in social philosophy, with Cristina Bicchieri's game‐theoretic approach being one of the most influential accounts. Bicchieri's framework has inspired numerous successful interventions, improving outcomes across diverse communities worldwide. However, certain aspects of Bicchieri's theory remain underdeveloped, limiting its ability to predict the best strategies for norm change. To address this, we present a simple formal model that clarifies the relationship between key components of her theory—schemas and behavioral rules—offering theorists and practitioners new tools for modeling and measuring social and behavioral change.

The Routledge Companion to Social and Political Philosophy, Second Edition is a comprehensive, definitive reference work, providing an up-to-date survey of the field, charting its history and key figures and movements, and addressing enduring questions as well as contemporary research. Features unique to the Companion are: - an extensive coverage of the history of social and political thought, including separate chapters on the development of political thought in the Islamic world, India, and China as well in modern Germany, France, and Britain - a focus on the core concepts and the normative foundations of social and political theory - a section devoted exclusively to distributive justice, the central issue of political philosophy since Rawls' Theory of Justice - extensive coverage of global justice and international issues. The Companion's seventy-four commissioned chapters, by leading scholars from throughout the world, are divided into eight thematic sections: The History of Social and Political Theory; Political Theories and Ideologies; Normative Foundations; The National State and Beyond; Distributive Justice; Political Concepts; Concepts and Methods in Social Philosophy; and Issues in Social and Political Philosophy. Expanded, updated, and revised throughout, this Second Edition includes new chapters on Politics, Philosophy and Economics (PPE); Political Epistemology; Race and Ethnicity; Power; Foucault; and New Diversity Theory.

It is well-known that people will adjust their first-order beliefs based on observations of others. We explore how such adjustments interact with second-order beliefs regarding universalism and relativism in a population. Across a range of simulations, we show that populations where individuals have a tendency toward universalism converge more quickly in coordination problems, and generate higher total payoffs, than do populations where individuals have a tendency toward relativism. Thus, in contexts where coordination is important, belief in universalism is advantageous. However, we also show, across a range of simulations, that universalism will enshrine inequalities and eliminate diversity, and in these cases it seems that relativism has its own advantages.

Liberal states traditionally rely on a reasonably narrow set of tools for engaging in social regulation. All of these tools are meant to change individual behaviors. Laws come with implicit force, financial regulations come with monetary carrots and sticks, and information provision informs people of the things that policymakers think they should know. Each of these is meant to guide individual choice making by changing how people evaluate their available options. Because this set of tools is meant to change individual behaviors, it is worth considering what implicit assumptions this set of tools makes about the individuals whose behaviors policymakers are looking to change. Behavioral and social science has not only found some limitations on existing policy tools but has also pointed in the direction of some new tools as well. This is because the model of human choice and behavior is now richer. Nudges and norms provide an expanded toolset for policymakers but raise interesting questions about the legitimacy of the state taking advantage of reductions in agency to achieve policy ends. I argue that this framing misconstrues the situation and suggest that the appropriate guide to any options in the policy tool kit is one of nurturing rather than inhibiting citizen autonomy. Sometimes, this will recommend the use of norms or nudges in place of the standard policy tools.

A core set of assumptions in economic modeling is that rational agents, who have a defined preference set, assess their options and determine which best satisfies their preferences. The rational actor model supposes that the world provides us with a menu of options, and we simply choose what’s best for us. Agents are independent of one another, and they can rationally assess which of their options they wish to pursue. This gives special authority to the choices that people make, since they are understood to be the outcomes of the agent’s considered judgments. However, we have come to see that the independence assumption does not always hold in the way that we may have initially thought. Social norms can govern our choices even when we disagree with them. Here we can begin to see how the standard model of choice and agency begins to weaken: no longer are my choices wholly mine, but instead there is a subset of choices that are governed by the broader culture that I live in. Social norms constrain my behavior with informal coercion — my desire to remain a community member in good standing requires me to behave in accordance with the community’s social norms. What I wish to challenge more substantively is the claim that the menu of choices agents “see” is in fact the objective set of options that is transparently provided by the world. Instead, I argue that the options that people perceive and the evidence they use to make choices are mediated by perspectives. Perspectives can importantly interact with social norms to make some norms more resilient to change, and others harder to adopt. This further shapes both our descriptive and normative understanding of agency. Our choices are not over all of the objectively available options, but over the options that we can see. The evidence we marshal to support our choices is not the full set of evidence, but the evidence that we recognize as salient. This is not to deny that individuals have agency, but rather we need a more nuanced understanding of the nature of this agency.

The article makes two substantive contributions to the existing literature on the ethics of international assistance and global justice. First, it builds what we take to be a widely held set of propositions about international assistance into a consistent view, and articulates a strong case against its desirability. Second, it sketches a more attractive alternative. To do so the article uses Sen’s idea of agent-oriented development as a starting point while at the same time providing a generalization of Sen’s account and a justificatory framework that does not rely on the capability approach.

This paper examines a series of Schelling-like models of residential segregation, in which agents prefer to be in the minority. We demon- strate that as long as agents care about the characteristics of their wider community, they tend to end up in a segregated state. We then investigate the process that causes this, and conclude that the result hinges on the similarity of informational states amongst agents of the same type. This is quite dierent from Schelling-like behavior, and sug- gests (in his terms) that segregation is an instance of macro behavior which can arise from a wide variety of micro motives.

In this work, we present a mathematical model for the emergence of descriptive norms, where the individual decision problem is formalized with the standard Bayesian belief revision machinery. Previous work on the emergence of descriptive norms has relied on heuristic modeling. In this paper we show that with a Bayesian model we can provide a more general picture of the emergence of norms, which helps to motivate the assumptions made in heuristic models. In our model, the priors formalize the belief that a certain behavior is a regularity. The evidence is provided by other group members’ behavior and the likelihood by their reliability. We implement the model in a series of computer simulations and examine the group-level outcomes. We claim that domain-general belief revision helps explain why we look for regularities in social life in the first place. We argue that it is the disposition to look for regularities and react to them that generates descriptive norms. In our search for rules, we create them

As scientists begin to study increasingly complex questions, many have turned to computer simulation to assist in their inquiry. This methodology has been challenged by both analytic modelers and experimentalists. A primary objection of analytic modelers is that simulations are simply too complicated to perform model verification. From the experimentalist perspective it is that there is no means to demonstrate the reality of simulation. The aim of this paper is to consider objections from both of these perspectives, and to argue that a proper understanding and application of robustness analysis is able to resolve them. ‡The author would like to thank Cristina Bicchieri, Michelle Foa, Paul Humphreys and Michael Weisberg for their helpful comments and suggestions. †To contact the author, please write to: Department of Philosophy, University of Pennsylvania, 433 Logan Hall, 249 S. 36th Street, Philadelphia, PA, 19104-6304; e-mail: [email protected].

In epistemology and the philosophy of science, there has been an increasing interest in the social aspects of belief acquisition. In particular, there has been a focus on the division of cognitive labor in science. This essay explores several different models of the division of cognitive labor, with particular focus on Kitcher, Strevens, Weisberg and Muldoon, and Zollman. The essay then shows how many of the benefits of the division of cognitive labor flow from leveraging agent diversity. The essay concludes by examining the benefits and burdens of diversity, particularly in the evaluative diversity that can be found in interdisicplinary science.

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

This paper explores the space of possibilities for public justification in morally diverse communities. Moral diversity is far more consequential than is typically appreciated, and as a result, we need to think more carefully about how our standard tools function in such environments. I argue that because of this diversity, public justification can be divorced from any claim of determinateness. Instead, we should focus our attention on procedures—in particular, what Rawls called cases of pure procedural justice. I use a modified form of the procedure “I cut, you choose” to demonstrate how perspectival diversity can make what looks like a simple procedure quite complex in practice. I use this to reframe disputes between classical liberal and contemporary liberal approaches to questions of public morality, arguing that classically liberal procedures, such as a reliance on the harm principle, can generate rather illiberal-looking outcomes when used in a morally diverse community. A seemingly less-principled approach, which simply balances burdens, appears to generate outcomes that look closer to what we would expect from classical liberalism. However, since both approaches are based on pure procedures that we can justify without reference to outcomes, it remains indeterminate which we ought to choose.

The philosophical tradition of liberal political thought has come to see tolerance as a crucial element of a liberal political order. However, while much has been made of the value of toleration, little work has been done on individual-level motivations for tolerant behavior. In this article, we seek to develop an account of the rational motivations for toleration and of where the limits of toleration lie. We first present a very simple model of rational motivations for toleration. Key to this model is an application of David Ricardo’s model of trade to thinking about toleration. This model supports the claim that we always have reasons to be as tolerant as possible. We then explore why we do not always see tolerant attitudes in the actual world, and point to some potential preconditions for toleration that the initial model does not capture. Subsequently, we examine a more detailed model that allows us to investigate more carefully the conditions under which tolerant behavior can be rewarded. We conclude by arguing that a consideration of self-interested motivations for toleration is essential to the success of a robust theory of toleration for a diverse society, but that even this approach has its limitations