Evolutionary psychologists have suggested that confidence and conservatism promoted aggression in our ancestral past, and that this may have been an adaptive strategy given the prevailing costs and benefits of conflict. However, in modern environments, where the costs and benefits of conflict can be very different owing to the involvement of mass armies, sophisticated technology, and remote leadership, evolved tendencies toward high levels of confidence and conservatism may continue to be a contributory cause of aggression despite leading to greater costs and fewer benefits. The purpose of this paper is to test whether confidence and conservatism are indeed associated with greater levels of aggression—in an explicitly political domain. We present the results of an experiment examining people’s levels of aggression in response to hypothetical international crises (a hostage crisis, a counter-insurgency campaign, and a coup). Levels of aggression (which range from concession to negotiation to military attack) were significantly predicted by subjects’ (1) confidence that their chosen policy would succeed, (2) score on a liberal-conservative scale, (3) political party affiliation, and (4) preference for the use of military force in real-world US policy toward Iraq and Iran. We discuss the possible adaptive and maladaptive implications of confidence.
Empirical testing of competing theories lies at the heart of social science research. We demonstrate that a well-known class of statistical models, called finite mixture models, provides an effective way of rival theory testing. In the proposed framework, each observation is assumed to be generated either from a statistical model implied by one of the competing theories or more generally from a weighted combination of multiple statistical models under consideration. Researchers can then estimate the probability that a specific observation is consistent with each rival theory. By modeling this probability with covariates, one can also explore the conditions under which a particular theory applies.We discuss a principled way to identify a list of observations that are statistically significantly consistent with each theory and propose measures of the overall performance of each competing theory. We illustrate the relative advantages of our method over existing methods through empirical and simulation studies.
Estimating the mechanisms that connect explanatory variables with the explained variable, also known as “mediation analysis,” is central to a variety of social-science fields, especially psychology, and increasingly to fields like epidemiology. Recent work on the statistical methodology behind mediation analysis points to limitations in earlier methods. We implement in Stata computational approaches based on recent developments in the statistical methodology of mediation analysis. In particular, we provide functions for the correct calculation of causal mediation effects using several different types of parametric models, as well as the calculation of sensitivity analyses for violations to the key.
While most existing theoretical and experimental literatures focus on how a high probability of repeated play can lead to more socially efficient outcomes (for instance, using the result that cooperation is possible in a repeated prisoner’s dilemma), this paper focuses on the detrimental effects of repeated play—the ‘‘dark side of the future.’’ I study a resource division model with repeated interaction and changes in bargaining strength. The model predicts a negative relationship between the likelihood of repeated interaction and social efficiency. This is because the longer shadow of the future exacerbates commitment problems created by changes in bargaining strength. I test and find support for the model using incentivized laboratory experiments. Increases in the likelihood of repeated play lead to more socially inefficient outcomes in the laboratory.
Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumptions. Randomizing treatment and intermediate variables is also insufficient. Despite these difficulties, the study of causal mechanisms is too important to abandon. We make three contributions to improve research on causal mechanisms. First, we present a minimum set of assumptions required under standard designs of experimental and observational studies and develop a general algorithm for estimating causal mediation effects. Second, we provide a method for assessing the sensitivity of conclusions to potential violations of a key assumption. Third, we offer alternative research designs for identifying causal mechanisms under weaker assumptions. The proposed approach is illustrated using media framing experiments and incumbency advantage studies.