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A growing body of research uses computational models to study political decision making and behavior such as voter turnout, vote choice, party competition, social networks, and cooperation in social dilemmas. Advances in the computational modeling of political decision making are closely related to the idea of bounded rationality. In effect, models of full rationality can usually be analyzed by hand, but models of bounded rationality are complex and require computer-assisted analysis. Most computational models used in the literature are agent based, that is, they specify how decisions are made by autonomous, interacting computational objects called “agents.” However, an important distinction can be made between two classes of models based on the approaches they take: behavioral and information processing. Behavioral models specify relatively simple behavioral rules to relax the standard rationality assumption and investigate the system-level consequences of these rules in conjunction with deductive, game-theoretic analysis. In contrast, information-processing models specify the underlying information processes of decision making—the way political actors receive, store, retrieve, and use information to make judgment and choice—within the structural constraints on human cognition, and examine whether and how these processes produce the observed behavior in question at the individual or aggregate level. Compared to behavioral models, information-processing computational models are relatively rare, new to political scientists, and underexplored. However, focusing on the underlying mental processes of decision making that must occur within the structural constraints on human cognition, they have the potential to provide a more general, psychologically realistic account for political decision making and behavior.

Article

Expected utility theory is widely used to formally model decisions in situations where outcomes are uncertain. As uncertainty is arguably commonplace in political decisions, being able to take that uncertainty into account is of great importance when building useful models and interpreting empirical results. Expected utility theory has provided possible explanations for a host of phenomena, from the failure of the median voter theorem to the making of vague campaign promises and the delegation of policymaking. A good expected utility model may provide alternative explanations for empirical phenomena and can structure reasoning about the effect of political actors’ goals, circumstances, and beliefs on their behavior. For example, expected utility theory shows that whether the median voter theorem can be expected to hold or not depends on candidates’ goals (office, policy, or vote seeking), and the nature of their uncertainty about voters. In this way expected utility theory can help empirical researchers derive hypotheses and guide them towards the data required to exclude alternative explanations. Expected utility has been especially successful in spatial voting models, but the range of topics to which it can be applied is far broader. Applications to pivotal voting or politicians’ redistribution decisions show this wider value. However, there is also a range of promising topics that have received ample attention from empirical researchers, but that have so far been largely ignored by theorists applying expected utility theory. Although expected utility theory has its limitations, more modern theories that build on the expected utility framework, such as prospect theory, can help overcome these limitations. Notably these extensions rely on the same modeling techniques as expected utility theory and can similarly elucidate the mechanisms that may explain empirical phenomena. This structured way of thinking about behavior under uncertainty is the main benefit provided by both expected utility theory and its extensions.

Article

Political systems involve citizens, voters, politicians, parties, legislatures, and governments. These political actors interact with each other and dynamically alter their strategies according to the results of their interactions. A major challenge in political science is to understand the dynamic interactions between political actors and extrapolate from the process of individual political decision making to collective outcomes. Agent-based modeling (ABM) offers a means to comprehend and theorize the nonlinear, recursive, and interactive political process. It views political systems as complex, self-organizing, self-reproducing, and adaptive systems consisting of large numbers of heterogeneous agents that follow a set of rules governing their interactions. It allows the specification of agent properties and rules governing agent interactions in a simulation to observe how micro-level processes generate macro-level phenomena. It forces researchers to make assumptions surrounding a theory explicit, facilitates the discovery of extensions and boundary conditions of the modeled theory through what-if computational experiments, and helps researchers understand dynamic processes in the real-world. ABM models have been built to address critical questions in political decision making, including why voter turnouts remain high, how party coalitions form, how voters’ knowledge and emotion affect election outcomes, and how political attitudes change through a campaign. These models illustrate the use of ABM in explicating assumptions and rules of theoretical frameworks, simulating repeated execution of these rules, and revealing emergent patterns and their boundary conditions. While ABM has limitations in external validity and robustness, it provides political scientists a bottom-up approach to study a complex system by clearly defining the behavior of various actors and generate theoretical insights on political phenomena.

Article

The “sunk costs fallacy” is a popular import into political science from organizational psychology and behavioral economics. The fallacy is classically defined as a situation in which decision-makers escalate commitment to an apparently failing project in order to “recoup” the costs they have already sunk into it. The phenomenon is often framed as a good example of how real decision-making departs from the assumption of forward-looking rationality which underpins traditional approaches to understanding politics. Researchers have proposed a number of different psychological drivers for the fallacy, such as cognitive dissonance reduction, and there is experimental and observational evidence that it accurately characterizes decision-making in certain contexts. However, there is significant skepticism about the fallacy in many social sciences, with critics arguing that there are better forward-looking rational explanations for decisions apparently driven by a desire to recoup sunk costs – among them reputational concerns, option values and agency problems. Critics have also noted that in practical situations sunk costs are informative both about decision makers’ intrinsic valuation for the issue and the prospects for success, making it hard to discern a separate role for sunk costs empirically. To address these concerns, empirical researchers have employed a number of strategies, especially leveraging natural experiments in certain non-political decision making contexts such as sports or business, in order to isolate the effects of sunk costs per se from other considerations. In doing so, they have found mixed support for the fallacy. Research has also shown that the prevalence of the sunk costs fallacy may be moderated by a number of factors, including the locus of decision-making, framing, and national context. These provide the basis for suggestions for future research.

Article

Law enforcement negotiation is one of the only times when a law enforcement officer interacts with an offender during the commission of a crime and, as such, can influence the outcome of the situation in favor of law enforcement. All other interactions between offenders take place after the commission of the crime or during undercover operations when the law enforcement officer is hiding his or her identity. Law enforcement crisis negotiation (LECN) provides techniques, tactics, and procedures for seamlessly dealing with difficult, dangerous, and disordered persons to obtain voluntary compliance through the application of verbal influence-based skill sets. LECN is a method by which to deal with perceived threats to a subject’s emotional, psychological, or physical well-being during intense conflict or crisis situations. Understanding critical incidents and the mindset of a subject is critical to determining the proper communication strategies and tactics. At the heart of the process is understanding and assessing instrumental and expressive behavior in order to apply tactical negotiation or crisis intervention. A key skill set to being effective in negotiating with difficult, dangerous, and disordered persons is to build credibility through the application of the Behavioral Influence Stairway Model (BISM) in the effective application of active listening skills, empathy, rapport-trust, and influence to persuade behavioral change on the part of the subject.