Ryan Muldoon

Epistemic Landscapes and the Division of Cognitive Labor

Because contemporary scientific research is conducted by groups of scientists, understanding scientific progress requires understanding this division of cognitive labor. We present a novel agent‐based model of scientific research in which scientists divide their labor to explore an unknown epistemic landscape. Scientists aim to find the most epistemically significant research approaches. We consider three different search strategies that scientists can adopt for exploring the landscape. In the first, scientists work alone and do not let the discoveries of the community influence their actions. This is compared with two social research strategies: Followers are biased toward what others have already discovered, and we find that pure populations of these scientists do less well than scientists acting independently. However, pure populations of mavericks, who try to avoid research approaches that have already been taken, vastly outperform the other strategies. Finally, we show that, in mixed populations, mavericks stimulate followers to greater levels of epistemic production, making polymorphic populations of mavericks and followers ideal in many research domains.

Trustworthiness is a Social Norm, but Trusting is Not

Previous literature has demonstrated the important role that trust plays in developing and maintaining well-functioning societies. However, if we are to learn how to increase levels of trust in society, we must first understand why people choose to trust others. One potential answer to this is that people view trust as normative: there is a social norm for trusting that imposes punishment for noncompliance. To test this, we report data from a survey with salient rewards to elicit people’s attitudes regarding the punishment of distrusting behavior in a trust game. Our results show that people do not behave as though trust is a norm. Our participants expected that most people would not punish untrusting investors, regardless of whether the potential trustee was a stranger or a friend. In contrast, our participants behaved as though being trustworthy is a norm. Most participants believed that most people would punish someone who failed to reciprocate a stranger’s or a friend’s trust. We conclude that, while we were able to reproduce previous results establishing that there is a norm of reciprocity, we found no evidence for a corresponding norm of trust, even among friends.

Robustness and idealization in models of cognitive labor

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.

Introduction, SI of Synthese “The collective dimension of science”

Scientists are not isolated agents: they collaborate in laboratories, research networks and large-scale international projects. Apart from direct collaboration, scientists interact with each other in various ways: they follow entrenched research programs, trust their peers, embed their work into an existing paradigm, exchange concepts, methods and results, compete for grants or prestige, etc. The collective dimension of science has been discussed by philosophers of science in various ways, but until recently, the use of formal methods has been restricted to some particular areas, such as the treatment of the division of scientific labor, the study of reward schemes or the effects of network structures on the production of scientific knowledge. Given the great promise of these methods for modeling and understanding of the dynamics of scientific research, this blind spot struck us as surprising. At the same time, social aspects of the production and diffusion of knowledge have been

The conditions of tolerance

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.

On the Emergence of Descriptive Norms

A descriptive norm is a behavioral rule that individuals follow when their empirical expectations of others following the same rule are met. We aim to provide an account of the emergence of descriptive norms by first looking at a simple case, that of the standing ovation. We examine the structure of a standing ovation, and show it can be generalized to describe the emergence of a wide range of descriptive norms.

Segregation That No One Seeks

This article examines a series of Schelling-like models of residential segregation, in which agents prefer to be in the minority. We demonstrate 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 among agents of the same type. This is quite different from Schelling-like behavior and suggests (in his terms) that segregation is an instance of macrobehavior that can arise from a wide variety of micromotives.

Expanding the Justificatory Framework of Mill's Experiments in Living

In On Liberty , Mill introduced the concept of ‘experiments in living’. I will provide an account of what Mill saw to be the basic problem he was addressing – the extensive pressure to fit in with the crowd, and how this bred mediocrity. I connect this to worries about public reason models of justification. I argue that a generalized version of Mills argument offers us a better path to political justification stemming from experimentation. Rather than grounding political justification on shared political reasons, we justify our political culture on our ability to reject consensus views and try alternatives.

Why are there descriptive norms? Because we looked for them

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.