Decision Theory and Human Behavior

People are not logical. They are psychological. Anonymous

People often make mistakes in their maths. This does not mean that we should abandon arithmetic.

Jack Hirshleifer

Decision theory is the analysis of the behavior of an individual facing nonstrategic uncertainty—that is, uncertainty that is due to what we term "Nature" (a stochastic natural event such as a coin flip, seasonal crop loss, personal illness, and the like) or, if other individuals are involved, their behavior is treated as a statistical distribution known to the decision maker. Decision theory depends on probability theory, which was developed in the seventeenth and eighteenth centuries by such notables as Blaise Pascal, Daniel Bernoulli, and Thomas Bayes.

A rational actor is an individual with consistent preferences (§1.1). A rational actor need not be selfish. Indeed, if rationality implied selfishness, the only rational individuals would be sociopaths. Beliefs, called subjective priors in decision theory, logically stand between choices and payoffs. Beliefs are primitive data for the rational actor model. In fact, beliefs are the product of social processes and are shared among individuals. To stress the importance of beliefs in modeling choice, I often describe the rational actor model as the beliefs, preferences and constraints model, or the BPC model. The BPC terminology has the added attraction of avoiding the confusing and value-laden term "rational."

The BPC model requires only preference consistency, which can be defended on basic evolutionary grounds. While there are eminent critics of preference consistency, their claims are valid in only a few narrow areas. Because preference consistency does not presuppose unlimited information-processing capacities and perfect knowledge, even bounded rationality (Simon 1982) is consistent with the BPC model. Because one cannot do behavioral game theory, by which I mean the application of game theory to the experimental study of human behavior, without assuming preference consistency, we must accept this axiom to avoid the analytical weaknesses of the behavioral disciplines that reject the BPC model, including psychology, anthropology, and sociology (see chapter 11).

Behavioral decision theorists have argued that there are important areas in which individuals appear to have inconsistent preferences. Except when individuals do not know their own preferences, this is a conceptual error based on a misspecification of the decision maker's preference function. We show in this chapter that, assuming individuals know their preferences, adding information concerning the current state of the individual to the choice space eliminates preference inconsistency. Moreover, this addition is completely reasonable because preference functions do not make any sense unless we include information about the decision maker's current state. When we are hungry, scared, sleepy, or sexually deprived, our preference ordering adjusts accordingly. The idea that we should have a utility function that does not depend on our current wealth, the current time, or our current strategic circumstances is also not plausible. Traditional decision theory ignores the individual's current state, but this is just an oversight that behavioral decision theory has brought to our attention.

Compelling experiments in behavioral decision theory show that humans violate the principle of expected utility in systematic ways (§1.5.1). Again, it must be stressed that this does not imply that humans violate preference consistency over the appropriate choice space but rather that they have incorrect beliefs deriving from what might be termed "folk probability theory" and make systematic performance errors in important cases (Levy 2008).

To understand why this is so, we begin by noting that, with the exception of hyperbolic discounting when time is involved (§1.2), there are no reported failures of the expected utility theorem in nonhumans, and there are some extremely beautiful examples of its satisfaction (Real 1991) Moreover, territoriality in many species is an indication of loss aversion (Gintis 2007b). The difference between humans and other animals is that the latter are tested in real life, or in elaborate simulations of real life, as in Leslie Real's work with bumblebees (Real 1991), where subject bumblebees are released into elaborate spatial models of flowerbeds. Humans, by contrast, are tested using imperfect analytical models of real-life lotteries. While it is important to know how humans choose in such situations, there is certainly no guarantee they will make the same choices in the real-life situation and in the situation analytically generated to represent it. Evolutionary game theory is based on the observation that individuals are more likely to adopt behaviors that appear to be successful for others. A heuristic that says "adopt risk profiles that appear to have been successful to others" may lead to preference consistency even when individuals are incapable of evaluating analytically presented lotteries in the laboratory. Indeed, a plausible research project in extending the rational actor model would be to replace the assumption of purely subjective prior (Savage 1954) with the assumption that individuals are embedded in a network of mind across which cognition is more or less widely distributed (Gilboa and Schmeidler 2001; Dunbar et al. 2010; Gintis 2010).

In addition to the explanatory success of theories based on the BPC model, supporting evidence from contemporary neuroscience suggests that expected utility maximization is not simply an "as if" story. In fact, the brain's neural circuitry actually makes choices by internally representing the payoffs of various alternatives as neural firing rates and choosing a maximal such rate (Shizgal 1999; Glimcher 2003; Glimcher and Rustichini 2004; Glimcher et al. 2005). Neuroscientists increasingly find that an aggregate decision making process in the brain synthesizes all available information into a single unitary value (Parker and Newsome 1998; Schall and Thompson 1999). Indeed, when animals are tested in a repeated trial setting with variable rewards, dopamine neurons appear to encode the difference between the reward that the animal expected to receive and the reward that the animal actually received on a particular trial (Schultz et al. 1997; Sutton and Barto 2000), an evaluation mechanism that enhances the environmental sensitivity of the animal's decision making system. This...