Emotions shape strategic conflict dynamics. However, the precise way in which strategic and emotional concerns interact to affect international cooperation and contention are not well understood. We propose a model of intergroup conflict under incomplete information in which agents are sensitive to psychological motivations in the form of anger. Agents become angry in response to worse-than-expected outcomes due to actions of other players. Aggression may be motivated by anger or by beliefs about preferences of members of the other group. Increasing one group’s sensitivity to anger makes that group more aggressive but reduces learning about preferences, which makes the other group less aggressive in response to bad outcomes. Thus, anger has competing effects on the likelihood of conflict. The results have important implications for understanding the complex role of anger in international relations and, more generally, the interplay between psychological and material aims in both fomenting and ameliorating conflict.

Does motivated reasoning harm democratic accountability? Substantial evidence from political behavior research indicates that voters have “directional motives” beyond accuracy, which is often taken as evidence that they are ill equipped to hold politicians accountable. We develop a model of electoral accountability with voters as motivated reasoners. Directional motives have two effects: (1) divergence—voters with different preferences hold different beliefs, and (2) desensitization—the relationship between incumbent performance and voter beliefs is weakened. While motivated reasoning does harm accountability, this is generally driven by desensitized voters rather than polarized partisans with politically motivated divergent beliefs. We also analyze the relationship between government performance and vote shares, showing that while motivated reasoning always weakens this relationship, we cannot infer that accountability is also harmed. Finally, we show that our model can be mapped to standard models in which voters are fully Bayesian but have different preferences or information.

We analyze the design of an international climate agreement. In particular, we consider two goals of such an agreement: overcoming free-rider problems and adjusting for differences in mitigation costs between countries. Previous work suggests that it is difficult to achieve both of these goals at once under asymmetric information because countries free ride by exaggerating their abatement costs. We argue that independent information collection (investigations) by an international organization can alleviate this problem. In fact, though the best implementable climate agreement without investigations fails to adjust for individual differences even with significant enforcement power, a mechanism with investigations allows adjustment and can enable implementation of the socially optimal agreement. Furthermore, when the organization has significant enforcement power, the optimal agreement is achievable even with minimal investigative resources (and vice versa). The results suggest that discussions about institutions for climate cooperation should focus on information collection as well as enforcement.

Lobbying is a potential source of corruption but is also a valuable source of information for policy-makers. We analyze a game-theoretic model that shows how the threat of corruption affects the incentives of noncorrupt politicians to enlist the help of lobbyists to make more informed decisions. Politicians face a dilemma because voters cannot always tell whether a politician allows access to lobbyists to solicit corruption or to seek information. Thus, a noncorrupt politician may deny access to lobbyists to signal that she is noncorrupt even though doing so impedes her ability to make good policy. This signaling may decrease the welfare of the voters depending on the value of the lost policy information relative to the value of screening out corrupt politicians.

This article presents a new model for scoring alternatives from “contest” outcomes. The model is a generalization of the method of paired comparison to accommodate comparisons between arbitrarily sized sets of alternatives in which outcomes are any division of a fixed prize. Our approach is also applicable to contests between varying quantities of alternatives. We prove that under a reasonable condition on the comparability of alternatives, there exists a unique collection of scores that produces accurate estimates of the overall performance of each alternative and satisfies a well-known axiom regarding choice probabilities. We apply the method to several problems in which varying choice sets and continuous outcomes may create problems for standard scoring methods. These problems include measuring centrality in network data and the scoring of political candidates via a “feeling thermometer.” In the latter case, we also use the method to uncover and solve a potential difficulty with common methods of rescaling thermometer data to account for issues of interpersonal comparability.

The science of human rights requires valid comparisons of repression levels across time and space. Though extensive data collection efforts have made such comparisons possible in principle, statistical measures based on simple additive scales made them rare in practice. This article uses a dynamic measurement model that contrasts with current approaches by (1) accounting for the fact that human rights indicators vary in the level of information they provide about the latent level of repression, (2) allowing realistic descriptions of measurement uncertainty in the form of credible intervals and (3) providing a theoretical motivation for modeling temporal dependence in human rights levels. It presents several techniques, which demonstrate that the dynamic ordinal item-response theory model outperforms its static counterpart.