Ryan Muldoon

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.

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