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Behavioral decision theory is a descriptive psychological theory of human judgment, decision making, and behavior that can be applied to political science. Behavioral decision theory is closely related to behavioral economics and behavioral finance. Behavioral economics is an attempt to understand actual human economic behavior, and behavioral finance studies human behavior in financial markets. Research on people’s decision making represents an important part of these fields, in which various aspects overlap with the scope of behavioral decision theory. Behavioral decision theory focuses on the decision-making phenomena that are broadly divisible into those under certainty, those under risk, and others under uncertainty that includes ambiguity and ignorance. What are the theoretical frameworks that could be used to explain the decision-making phenomenon? Although numerous theories related to decision making have been developed, they are, in essence, often broadly divided into two types: normative theory and descriptive theory. The former is intended to support rational decision making. The latter describes how people actually make decisions. Both normative and descriptive theories reflect the nature of actual human decision making to a degree. Even descriptive theory seeks a certain level of rationality in actual human decision making. Consequently, the two are mutually indistinguishable. Nonetheless, a major example of normative theory is regarded as the system of utility theory that is widely used in economics. A salient example of descriptive theory is behavioral decision theory. Utility theory has numerous variations, such as linear and nonlinear utility theories. Most theories have established axioms and mathematically developed principles. In contrast, behavioral decision theory covers a considerably wide range of variations of theoretical expressions, including theories that have been developed mathematically (such as prospect theory) and those expressed only with natural language (such as multiattribute decision-making process models). Behavioral decision theory has integrated the implications of the normative theory, descriptive theory, and prescriptive theory that help people to make better decisions.

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.

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.