Decision Making

Decision making is the process of choosing a preferred option or course of action from among a set of alternatives. Decision making permeates all aspects of life, including decisions about what to buy, whom to vote for, or what job to take. Decisions often involve uncertainty about the external world (e.g., What will the weather be like?), as well as conflict regarding one's own preferences (e.g., Should I opt for a higher salary or for more leisure?). The decision-making process often begins at the information-gathering stage and proceeds through likelihood estimation and deliberation, until the final act of choosing.

The study of decision making is an interdisciplinary enterprise involving economics, political science, sociology, psychology, statistics, and philosophy. Decisions are made by individuals and by groups. Important results have been obtained both in the theoretical and experimental study of group decision making. With an eye toward the cognitive sciences, this article focuses on the empirical study of decision making at the individual level. (The focus is on choice behavior; for more on judgment, see JUDGMENT HEURISTICS.)

One can distinguish three approaches to the analysis of decision making: normative, descriptive, and prescriptive. The normative approach assumes a rational decision-maker who has well-defined preferences that obey certain axioms of rational behavior. This conception, known as RATIONAL CHOICE THEORY, is based primarily on a priori considerations rather than on empirical observation. The descriptive approach to decision making is based on empirical observation and on experimental studies of choice behavior. It is concerned primarily with the psychological factors that guide behavior in decision-making situations. Experimental evidence indicates that people's choices are often at odds with the normative assumptions of the rational theory. In light of this, the prescriptive enterprise focuses on methods of improving decision making, bringing it more in line with normative desiderata (see, e.g., von Winterfeld and Edwards 1986).

In some decision contexts, the availability of the chosen option is essentially certain (as when choosing among dishes from a menu, or cars at a dealer's lot). Other decisions are made under UNCERTAINTY: they can be "risky," where the probabilities of the outcomes are known (e.g., gambling or insurance), or they can be "ambiguous," as are most real world decisions, in that their precise likelihood is not known and needs to be judged "subjectively" by the decision maker. When making decisions under uncertainty, a person has to consider both the desirability of the potential outcomes and their probability of occurrence (see PROBABILISTIC REASONING). Indeed, part of the study of decision-making concerns the manner in which these factors are combined.

Presented with a choice between a risky prospect that offers a 50 percent chance to win $200 (and a 50 percent chance to win nothing) and an alternative of receiving $100 for sure, most people prefer the sure gain over the gamble, although the two prospects have the same expected value. (The expected value is the sum of possible outcomes weighted by their probability of occurrence. The expected value of the gamble above is .50 × $200 + .50 × 0 = $100.) Preference for a sure outcome over a risky prospect of equal expected value is called risk aversion; indeed, people tend to be risk averse when choosing between prospects with positive outcomes. The tendency towards risk aversion can be explained by the notion of diminishing sensitivity, first formalized by Daniel Bernoulli (1738), who thought that "the utility resulting from a fixed small increase in wealth will be inversely proportional to the quantity of goods previously possessed." Bernoulli proposed that people have a concave utility function that captures their subjective value for money, and that preferences should be described using expected utility instead of expected value (a function is concave if a line joining two points on the curve lies below it). According to expected utility, the worth of a gamble offering a 50 percent chance to win $200 (and 50 percent chance to win nothing) is .50 × u($200), where u is the person's utility function (u(0) = 0). It follows from a concave function that the subjective value attached to a gain of $100 is more than 50 percent of the value attached to a gain of $200, which entails preference for the sure $100 gain and, hence, risk aversion.

Expected UTILITY THEORY and the assumption of risk aversion play a central role in the standard economic analysis of choice between risky prospects. In fact, a precipitating event for the empirical study of decision making came from economics, with the publication of von Neumann and Morgenstern's (1947) normative treatment of expected utility, in which a few compelling axioms, when satisfied, were then shown to imply that a person's choices can be thought of as favoring the alternative with the highest subjective expected utility. The normative theory was introduced to psychologists in the late 1950s and early 1960s (Edwards 1961; Luce and Raiffa 1957), and has generated extensive research into "behavioral decision theory." Because the normative treatment specifies simple and compelling principles of rational behavior, it has since served as a benchmark against which behavioral studies of decision making are compared. Research over the last four decades has gained important insights into the decision-making process, and has documented systematic ways in which decision-making behavior departs form the normative benchmark (see ECONOMICS AND COGNITIVE SCIENCE).

When asked to choose between a prospect that offers a 50 percent chance to lose $200 (and a 50 percent chance at nothing) and the alternative of losing $100 for sure, most people prefer to take an even chance at losing $200 or nothing over a sure $100 loss. This is because diminishing sensitivity applies to negative as well as to positive outcomes: the impact of an initial $100 loss is greater than that of an additional $100. The worth of a gamble that offers a 50 percent chance to lose $200 is thus greater (i.e., less negative) than that of a sure $100 loss; .50 × u(-$200) > u(-$100). This results in a convex function for losses and a risk-seeking preference for the gamble over a sure loss. Preference for a risky prospect over a sure outcome of equal expected value is called risk seeking. With the exception of prospects that involve very small probabilities, risk aversion is generally observed in choices involving gains, whereas risk seeking tends to hold in choices involving losses.

An S-shaped value function, based on these attitudes toward risk, forms part of prospect theory (Kahneman and TVERSKY 1979), an influential descriptive theory of choice. The value function of prospect theory has three important properties: (1) it is defined on gains and losses rather than total wealth, which captures the fact that people normally treat outcomes as departures from a current reference point, rather than in terms of final assets, as posited by the rational theory of choice; (2) it is steeper for losses than for gains: thus, a loss of $X is more aversive than a gain of $X is attractive. The fact that losses loom larger than corresponding gains is known as loss aversion; and (3) it is concave for gains and convex for losses, which yields the risk attitudes described above: risk aversion in the domain of gains and risk seeking in the domain of losses.

These attitudes seem compelling and unobjectionable, yet their combination can lead to normatively problematic consequences. In one example (Tversky and Kahneman 1986), respondents are asked to assume themselves to be $300 richer and are then asked to choose between a sure gain of $100 or an equal chance to win $200 or nothing. Alternatively, they are asked to assume themselves to be $500 richer, and made to choose between a sure loss of $100 and an equal chance to lose $200 or nothing. In accord with the properties described above, most subjects choosing between gains are risk averse and prefer the certain $100 gain, whereas most subjects choosing between losses are risk seeking, preferring the risky prospect over the sure $100 loss. The two problems, however, are essentially identical: when the initial $300 or $500 payment is added to the respective outcomes, both problems amount to a choice between $400 for sure as opposed to an even chance at $300 or $500. This is known as a framing effect. It occurs when alternative framings of what is essentially the same decision problem give rise to predictably different choices. The way a problem is described -- in terms of gains or losses -- can trigger conflicting risk attitudes; similarly, different methods of eliciting preference -- for example, through choice versus independent evaluation -- can lead people to weigh certain aspects of the options differently. This leads to violations of the normative requirements of "description invariance" and "procedure invariance," according to which logically equivalent representations of a decision problem as well as logically equivalent methods of elicitation should yield the same preferences.

Monetary gambles have traditionally served as metaphors for uncertain awards and as frequent stimuli in decision research. However, much attention has also been devoted to choices between nonmonetary awards, which tend to be multidimensional in nature -- for example, the need to choose between job options that differ in prestige, salary, and rank, or between apartments that differ in size, price, aesthetic attractiveness, and location. People are typically uncertain about how much weight to assign to the different dimensions of options; such assignments are often contingent on relatively immaterial changes in the task, the description, and the nature of the options under consideration (Hsee 1998; Tversky, Sattath, and Slovic 1988).

The study of decision-making incorporates issues of PLANNING, PROBLEM SOLVING, PSYCHOPHYSICS, MEMORY, and SOCIAL COGNITION, among others. Behavioral studies of decision-making have included process-tracing methods, such as verbal protocols (Ericsson and Simon 1984), information-acquisition sequences (Payne, Bettman, and Johnson 1993), and eye-movement data (Russo and Dosher 1983). Decision patterns involving hypothetical problems have been replicated in real settings and with experts, for example, physicians' decisions regarding patient treatment (McNeil et al. 1982; Redelmeier and Shafir 1995), academics' retirement investment decisions (Benartzi and Thaler 1995), and taxi drivers' allocation of working hours (Camerer et al. 1997). Some replications have provided substantial as opposed to minor payoffs (e.g., the equivalent of a month's salary paid to respondents in the Peoples' Republic of China; Kachel-meier and Shehata 1992).

People's choices are influenced by various aspects of the decision-making situation. Among these are the conflict or difficulty that characterize a decision (March 1978; Tversky and Shafir 1992), the regret anticipated in cases where another option would have been better (Bell 1982), the role that reasons play in justifying one choice over another (Shafir, Simonson, and Tversky 1993), the attachment that is felt for options already in one's possession (Kahneman, Knetsch, and Thaler 1990), the influence exerted by costs already suffered (Arkes and Blumer 1985), the effects of temporal separation on future decisions (Loewenstein and Elster 1992), and the occasional inability to predict future or remember past satisfaction (Kahneman 1994). Research in decision-making has uncovered psychological principles that account for empirical findings that are counterintuitive and incompatible with normative analyses. People do not always have well-ordered preferences: instead, they approach decisions as problems that need to be solved, and construct preferences that are heavily influenced by the nature and the context of decision.

References

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Bell, D. E. (1982). Regret in decision making under uncertainty. Operations Research 30:961-981.

Benartzi, S., and R. Thaler. (1995). Myopic loss aversion and the equity premium puzzle. Quarterly Journal of Economics 110(1):73-92.

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Hsee, C. K. (1998). The evaluability hypothesis: Explaining joint-separate evaluation preference reversal and beyond. In D. Kahneman and A. Tversky, Eds. Choices, values and frames. Cambridge: Cambridge University Press.

Kachelmeier, S. J., and M. Shehata. (1992). Examining risk preferences under high monetary incentives: Experimental evidence from the People's Republic of China. American Economic Review 82:1120-1141.

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McNeil, B. J., S. G. Pauker, H. C. Sox, and A. Tversky. (1982). On the elicitation of preferences for alternative therapies. New England Journal of Medicine 306:1259-1262.

Payne, J. W., J. R. Bettman, and E. J. Johnson. (1993). The Adaptive Decision Maker. Cambridge: Cambridge University Press.

Redelmeier, D., and E. Shafir. (1995). Medical decision making in situations that offer multiple alternatives. Journal of the American Medical Association 273(4):302-305.

Russo, J. E., and B. A. Dosher. (1983). Strategies for multiattribute binary choice. Journal of Experimental Psychology: Learning, Memory, and Cognition 9(4):676-696.

Shafir, E., I. Simonson, and A. Tversky. (1993). Reason-based choice. Cognition 49(2):11-36.

Tversky, A., and D. Kahneman. (1986). Rational choice and the framing of decisions. Journal of Business 59(4, pt. 2):251-278.

Tversky, A., S. Sattath, and P. Slovic. (1988). Contingent weighting in judgment and choice. Psychological Review 95(3):371-384.

Tversky, A., and E. Shafir. (1992). Choice under conflict: The dynamics of deferred decision. Psychological Science 3(6):358-361.

von Neumann, J., and O. Morgenstern. (1947). Theory of Games and Economic Behavior. 2nd ed. Princeton: Princeton University Press.

von Winterfeld, D., and W. Edwards. (1986). Decision Analysis and Behavioral Research. Cambridge: Cambridge University Press.

Decision Making

See also

References

Further Readings