We define a general class of network formation models, Statistical Exponential Random Graph Models (SERGMs), that nest standard exponential random graph models (ERGMs) as a special case. We provide the first general results on when these models' (including ERGMs) parameters estimated from the...

In this paper we analyze the problem of whether and/or when to replace a leader (agent) when no monetary rewards are available, and it is the leader's competence rather than effort that is being evaluated. The only decisions that the leader takes over time are whether to undertake risky but...

Consider a voting procedure where countries, states, or districts comprising a union each elect representatives who then participate in later votes at the union level on their behalf. The countries, provinces, and states may vary in their populations and composition. If we wish to maximize the...

Constitutional arrangements affect the decisions made by a society. We study how this effect leads to preferences of citizens over constitutions; and ultimately how this has a feedback that determines which constitutions can survive in a given society. Constitutions are stylized here, to consist...

We investigate the role of networks of military alliances in preventing or encouraging wars between groups of countries. A country is vulnerable to attack if there is some fully-allied group of countries that can defeat that country and its (remaining) allies based on a function of their...

We introduce and analyze a model in which agents observe sequences of signals about the state of the world, some of which are ambiguous and open to interpretation. Instead of using Bayes' rule on the whole sequence, our decision makers use Bayes' rule in an iterative way: first to interpret each...

A set of voters consults experts before voting over two alternatives. Experts observe private signals about the values of the alternatives and can reveal their information or conceal it, but cannot lie. We examine how disclosure and voting vary with preference biases, signal precision, and the...

We study learning in a setting where agents receive independent noisy signals about the true value of a variable and then communicate in a network. They naïvely update beliefs by repeatedly taking weighted averages of neighbors' opinions. We show that all opinions in a large society converge to...