Rational Choice Theory and Political Behavior Explained

Introduction to Rational Choice Theory

Rational Choice Theory (RCT) is a paradigm for understanding and modeling social and economic behavior within groups or systems. It is sometimes referred to as rational action theory, and is often interpreted as a way to assume behaviors in microeconomic models as "wanting more" of something rather than less — goods, services, overt political control, and so forth (Allingham, 2002). It became even more popular as a usable theory within political science, sociology, and philosophy after Gary Becker used it in describing studies of crime, discrimination, and human capital, and subsequently won the 1992 Nobel Memorial Prize in Economics (Nobel Prize, 1992).

RCT uses the term rational in a rather non-traditional manner — not as meaning thoughtful or clear-headed, but as a way to describe the way an individual acts as if they are balancing costs against benefits to find the most efficacious solution to a problem; in effect, as though a decision serves a personal advantage. Using this theory, any decision made — logical, illogical, sane, or insane — is said to mimic a "rational" benefit process. It is therefore a way of understanding choices in patterns rather than as individual or unique events. For example, there is nothing irrational about preferring dogs to cats as pets consistently, but there is something quite irrational about alternating between preferring dogs to cats and cats to dogs on a regular or even ad hoc basis (Goode, 1997).

RCT is less interested in the origins, nature, or validity of human motivations than in taking a quantitative approach to finding the roots of behavior, preferences, and the reasons choices are made. Its lack of attention to consumer motivations leads some economists to prefer applying it to organizational behavior, where goals are clear and well-defined. In other words, competition in the market encourages organizations to maximize profits so they can survive. Unlike a more utilitarian approach to "maximizing utility," RCT works because it is more focused and consistent in nature (Alchain, 1950).

How RCT Models Individual Decision-Making

Because RCT is so multidimensional in its modeling capabilities, all RCT models assume that individuals will choose the best action according to a stable weighing of the pros and cons facing their choice. The model is not meant to be a complete description of reality; instead, it helps clarify reasoning, assists in formulating falsifiable hypotheses, and supports judging those hypotheses using empirical tests. One famous example comes from Milton Friedman, who noted that if a theory explaining the behavior of the leaves of a tree by attributing rationality to the leaves is proven empirically, then under RCT it constitutes a successful hypothesis (Friedman, 1970).

One might then ask what happens when it is impossible to empirically test or falsify the assumption of rationality. Does this mean the theory is true by definition but not by example? Proponents of RCT argue that empirical tests may be conducted on some results in most cases. However, in order to achieve a more accurate view of human decision-making, RCT must be expanded using other disciplines — psychology and anthropology, for instance — to extend theories of markets by examining how micro (individual) behavior morphs into macro (group) behavior (Kahneman, 2002).

Bounded Rationality and Its Relationship to RCT

Bounded rationality is the idea that in decision-making, the rationality of individuals is inherently limited by the information they have — that is, their cognitive grasp of the facts — and the time they have available to make decisions. It appears to complement RCT: when decision-makers lack the ability to arrive at a fully informed and timely solution, they apply their sense of "choice or self-interest" only by eliminating other available options. This assumes, however, that decision-makers regularly lack the ability and resources to reach the optimal decision, and so must depend on simplified choices. The role of the individual decision-maker thus becomes finding a satisfactory decision rather than an optimal one.

One scholar explained this concept using the metaphor of scissors: one blade represents the limitations of knowing (cognition), the other represents the environment (external stimuli). The optimal combination of the two produces a better decision, one in which resources may be used effectively by relying on pre-existing structure and expected regularity within the environment in question (Gigerenzer et al., 2002).