Conjoint experiments are popular, but there is a paucity of research on respondents’ underlying decision-making processes. We leverage eye-tracking methodology and a series of conjoint experiments, administered to university students and local community members, to examine how respondents process information in conjoint surveys. There are two main findings. First, attribute importance measures inferred from the stated choice data are correlated with attribute importance measures based on eye movement. This validation test supports the interpretation of common conjoint metrics, such as average marginal component effects (AMCEs), as measures of attribute importance. Second, when we experimentally increase the number of attributes and profiles in the conjoint table, respondents view a larger absolute number of cells but a smaller fraction of the total cells displayed. Moving from two to three profiles, respondents search more within-profile, rather than within-attribute, to build summary evaluations. However, respondents’ stated choices remain fairly stable regardless of the number of attributes and profiles in the conjoint table. Together, these patterns speak to the robustness of conjoint experiments and are consistent with a bounded rationality mechanism. Respondents adapt to complexity by selectively incorporating relevant new information to focus on important attributes, while ignoring less relevant information to reduce cognitive processing costs.

Voter choice is one of the most important problems in political science. The most common models assume that voting is a rational choice based on policy positions (e.g., key issues) and non-policy information (e.g., social identity, personality). Though such models explain macroscopic features of elections, they also reveal important anomalies that have been resistant to explanation. We argue for a new approach that builds upon recent research in cognitive science and neuroscience; specifically, we contend that policy positions and social identities do not combine in merely an additive manner, but compete to determine voter preferences. This model not only explains several key anomalies in voter choice, but also suggests new directions for research in both political science and cognitive science.

While affective polarization has been shown to have serious social consequences, there is little evidence regarding its effects on political attitudes and behavior such as policy preferences, voting, or political information accrual. This paper provides evidence that affective polarization impacts misinformation belief, arguing that citizens with higher levels of affective polarization are more likely to believe in-party-congruent misinformation and less likely to believe out-party-congruent misinformation. The argument is supported by data from the ANES 2020 Social Media Study and the ANES 2020 Time Series Study, which speaks to the generalizability of the relationship. Additionally, a survey experiment provides evidence that the relationship is causal. The results hold among Democrats and Republicans and are independent of the effects of partisan strength and ideological extremity. Furthermore, the relationship between affective polarization and misinformation belief is exacerbated by political sophistication rather than tempered by it, implying that education will not solve the issue. The results speak to the need for work on reducing affective polarization.

Since its establishment in 1963, the Correlates of War (COW) project has sought to build cumulative knowledge about international conflict through the application of the scientific method to the study of militarized interstate behavior. Early analyses from the COW project found substantial variation in the causal model of war across the nineteenth and twentieth centuries, but COW scholars later sought to develop a general model of war that avoided post hoc historical periodization. We use out of sample cross validation to evaluate the plausibility of assuming temporal homogeneity for statistical models of international conflict that span the nineteenth and twentieth centuries. Our results suggest that the causal model of war changes substantially across historical eras. In particular, great care should be taken in generalizing Cold War findings to other historical eras. Our findings demonstrate the importance of exploring temporal variation in the causal model of war.

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