Underlying behavioral economics and distinguishing it from contemporary (neoclassical) economics is the presumption that the realism of behavioral and institutional assumptions matter substantively to the modeling of the economic agent (Simon 1959, 1978, 1987). In contemporary economic theory (e.g., Friedman 1953), assumptions are of little analytical consequence as long as the model’s predictions are correct. In behavioral economics, the realism of assumptions affects the accuracy of models’ predictions. It also helps distinguish between spurious correlations and actual cause-and-effect relationships between independent and dependant variables. Even when “as if” neoclassical behavioral assumptions yield correct predictions, if alternative, more realistic assumptions also generate accurate predictions, abiding by the former spawns false and therefore unscientific causal results. However, behavioral economics does not dispute the notion of abstraction and simplicity in model building; it questions models built upon unrealistic simplifying assumptions. When theory and evidence conflict, one includes in one’s search for modeling deficiencies misspecified assumptions, unlike in conventional analysis that focuses largely on missing variables and questioning the validity of the evidence itself (Reder 1982).
Unlike behavioral economics, economic theory typically plays a marginal role in economic psychology. The latter focuses upon applying psychological tools to economic questions and generating evidence that might underlie behavioral assumption of economic agency. It also examines the psychological motivation for economic behavior (Lewis, Webley, and Furnham 1995).
Contemporary behavioral economics is often associated with the contributions of Hebert Simon and, more recently, Daniel Kahneman and Amos Tversky (Kahneman and Tversky 1979; Tversky and Kahneman 1981), all of whom are Nobel Prize Laureates. Thus far, George Katona’s earlier pioneering contributions (1951, 1975) revising the psychological assumptions of consumer behavior in economic theory have gone largely unnoticed. Connected with Simon, Kahneman, Tversky, and their colleagues and associates are the presentation of new theories that either supplement or revise neoclassical micro and macro theories (Altman 2006; Camerer, Lowenstein, and Rabin 2003; Cyert and March 1963; Frank 1988; Shiller 1993; Thaler 1992; Williamson 1975).
Simon introduced the concepts of bounded rationality and satisficing, integrating into economic theory the reality of the cognitive limitations of individuals in terms of computational ability and knowledge acquisition that can only be realized at a cost. Rational individuals adopt behavioral procedures designed to limit such costs that differ descriptively and normatively from the conventional economic standard. Satisficing is the rational alternative to optimizing in a world of bounded rationality. In this case, it is possible for the firm’s output and profit and the individual’s utility to be less than they would be in a world with no limits to human cognitive abilities. Neoclassical norms are no longer optimal or descriptively accurate. Simon also emphasized the importance of recognizing the importance of power relationships, conflicts, fairness, altruism, and institutions for modeling economic agency—variables that are given little space in conventional theory. Individuals might be maximizing their own well-being at the expense of others or society at large; utililty might be enhanced at the expense of material wealth (Kahneman, Knetsch, Thaler 1986). Kahneman and Tversky developed prospect theory based on their experiments as an alternative to expected subjective utility theory, where individuals weigh losses more than gains and evaluate their utility in terms of relative positioning. Wealth maximization is not the end game in their descriptive modeling framework. Perspectives developed here have given rise to the revealing ultimatum and dictator game experiments wherein individuals make material sacrifices in the name of fairness (Güth 1995).
Independent of the work of Simon and more contemporary behavioral economists, Harvey Leibenstein (1957, 1966, 1979) developed the concepts of efficiency wages and x-efficiency theory (see Frantz 1997, for details). Based on the evidence, he assumes that effort inputs into the process of production are variable, not fixed at some maximum, as is assumed in conventional theory. Therefore, costs need not be minimized nor output maximized. Effort maximizing remains the ideal for wealth maximization or x-efficiency to be achieved. However, for this to transpire, appropriate market conditions and in-firm incentive environments (often far removed from neoclassical norms) must be developed. The efficiency wage and x-efficiency literature have been extended, for example in Akerlof (2002), Akerlof and Yellen (1986), Altman (1996), Darity and Goldsmith (1996), Stiglitz (1987), and Tomer (1997).
There are roughly two major perspectives within behavioral economics. The one that follows and extends the work of the psychologists Kahneman and Tversky is especially focused on, demonstrating through experiments the extent to which human behavior deviates from neoclassical norms, where the latter are used as the benchmark for economic rationality. By such standards individuals are found to be largely irrational, but such behavior might possibly be corrected through education or government intervention. Irrationality in behavior as the norm is completely inconsistent with conventional theory, and would be regarded as suboptimal in the realm of production and consumption. Such findings are therefore thought to undermine the legitimacy of much contemporary economic modeling. Conventional theory is assumed to provide an adequate description of how individuals behave, and holds that this behavior is also normatively optimal. Many behavioral economists argue that conventional theory fails as an accurate descriptor of even average human behavior, although it might very well be normatively correct. Vernon Smith (2003, 2005) has challenged many of the empirical findings of such behavioral economists, arguing that the incentive environment of many of their experiments are far removed from what is typically found in the real world of economic life. But Smith and his colleagues have also challenged the modeling assumptions (especially their institutional parameters) of contemporary theory, also using experimental data. Smith finds that human behavior is largely economically rational.
Compatible with Smith’s view on behavioral economics is a perspective that builds on the contributions of Simon. Individuals are assumed to be largely rational and intelligent, developing procedures and institutions that best suit their individual needs given the constraints that they face (March 1978; Smith 2003; Todd and Gigerenzer 2003). This approach to human behavior is sometimes referred to as “ecological rationality.” Deviations from neoclassical norms are therefore not regarded as expressions of irrationality yielding suboptimal socioeconomic outcomes. Neoclassical procedures might very well yield suboptimal outcomes, but errors in decision making can be corrected through evolutionary processes such as learning.
Behavioral economics enriches conventional theory by introducing modeling variables and parameters that make for more scientific causal and predictive analysis. It does not reject the importance of incentives and opportunity costs in decision making, but it does question wealth maximization as dominating the decision-making process, economic efficiency as the end product of individual decision making, and the extent to which neoclassical behavioral norms should serve as appropriate benchmarks for economic analysis (Altman 2005). This in turn opens the door wide open for reconstructing economic theory, engaging institutional analysis as a partner in model building and inviting public policy analysis to help better understand the constraints and incentive environments under which economic agents make boundedly rational choices.
SEE ALSO Akerlof, George A.; Behaviorism; Economic Psychology; Economics, Experimental; Economics, Institutional; Economics, Neoclassical; Galbraith, John Kenneth; Maximization; Rationality; Satisficing Behavior; Smith, Vernon L.; Social Psychology; Sociology, Economic; Wages
Akerlof, George A. 2002. Behavioral Macroeconomics and Macroeconomic Behavior. American Economic Review 92: 411–433.
Akerlof, George A., and Janet L. Yellen, eds. 1986. Efficiency Wage Models of the Labor Market. Cambridge, U.K.: Cambridge University Press.
Altman, Morris. 1996. Human Agency and Material Welfare: Revisions in Microeconomics and Their Implications for Public Policy. Boston, Dordrecht, and London: Kluwer Academic.
Altman, Morris. 2005. Reconciling Altruistic, Moralistic, and Ethical Behavior with the Rational Economic Agent and Competitive Markets. Journal of Economic Psychology 26: 732–757.
Altman, Morris, ed. 2006. Handbook of Contemporary Behavioral Economics: Foundations and Developments. Armonk, NY: M. E. Sharpe.
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"Economics, Behavioral." International Encyclopedia of the Social Sciences. . Encyclopedia.com. (July 19, 2018). http://www.encyclopedia.com/social-sciences/applied-and-social-sciences-magazines/economics-behavioral
"Economics, Behavioral." International Encyclopedia of the Social Sciences. . Retrieved July 19, 2018 from Encyclopedia.com: http://www.encyclopedia.com/social-sciences/applied-and-social-sciences-magazines/economics-behavioral
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Contemporary conceptualizations view drug dependence as a "syndrome in which the use of a drug is given a much higher priority than other behaviors that once had higher value" (Jaffe, 1990). Thus, in order to understand drug dependence, the complex interactions among reinforcers must be understood. Consider, for example, the frequently observed case of polydrug abuse. On some occasions, the drugs may be used simultaneously (e.g., cocaine and heroin), whereas on other occasions one drug may be used in lieu of another (e.g., benzodiazepines and opioids). Understanding reinforcer interactions is also vital to developing effective treatment because treatments, be they pharmacological (e.g., methadone, nicotine gum) or nonpharmacological (e.g., AA meetings, alternative behaviors), often try to supplant drug reinforcers with other more acceptable reinforcers. Although understanding reinforcer interactions is important for understanding drug abuse, no method currently exists to quantify or even categorize these different types of reinforcer interactions.
Behavioral economics provides a means to understand the interactions among qualitatively different reinforcers. The value of behavioral economics derives from its unique ability to quantify the effects of qualitatively different reinforcers and their interactions (Bickel et al., 1992; Bickel, DeGrandpre, & Higgins, 1995; Bickel & Vuchinich, 2000; Hursh, 1980, 1991).
Several concepts from behavioral economics are relevant to this important issue. A central concept is the demand law which stipulates that total consumption decreases as price increases, all else being equal (Allison, 1979). Price can be considered anything (e.g., response requirement, monetary price, delay, changes in the amount of the commodity while holding the monetary or work price constant) that decreases availability of a commodity. Indeed, drug self-administration studies that vary price (response requirement) report results consistent with the demand law; that is, drug consumption decreases as the response requirement decreases (Griffiths, Bigelow, & Henningfield, 1980; Young & Herling, 1986). However, behavioral economics does more than simply restate this observation with a different terminology; it adds by quantitatively characterizing the relation between price and consumption via the economic measure of own-price elasticity (Bickel & DeGrandpre, 1996; Hursh & Bauman, 1987; Samuelson & Nordhaus, 1985). Own-price elasticity measures the proportional change in consumption across price conditions. If consumption of a particular reinforcer decreases proportionally to a large extent as price increases, then the consumption is referred to as elastic. If consumption decreases proportionally to a limited extent as price increases, then the consumption is referred to as inelastic. Elastic and inelastic consumption are quantified by elasticity coefficients greater than 1.0 and less than 1.0, respectively (Hursh, 1980). When examined across a broad range of prices, elasticity of demand for many reinforcers is often mixed: inelastic at low prices and elastic at higher prices.
With this method, then, qualitatively different reinforcers can be compared and distinguished in drug-dependent populations. For example, in a recent study money and cigarettes were compared among cigarette-deprived subjects on progressive ratio schedules (Bickel & Madden, 1999). Response requirements were increased across sessions and the same response requirement was imposed separately for both commodities. At the lowest response requirements, money was self-administered to a greater extent than cigarettes (a greater intensity of demand). As response requirement increased, money was shown to be more sensitive to price than cigarettes. The own-price elasticities of money and cigarettes were 2.1 and 0.9, respectively, with money being 2.3-fold more sensitive to price. Such efforts can be meaningfully extended to clinical settings via simulation technology (Petry & Bickel, 1998; Jacobs & Bickel, 1999). Jacobs and Bickel used questionnaires to assess the reported consumption of cigarettes and heroin singly and concurrently across a range of prices ($0.01 to $1,120) in opioid-dependent smokers undergoing treatment for heroin addiction. Across conditions in which cigarettes and heroin were available alone, or concurrently, demand for heroin was less elastic than for cigarettes. For example, heroin consumption was defended to a greater extent across increases in price than cigarettes. Taken together, these studies indicate that individuals with drug dependence value the primary drug of dependence more than other commodities.
Own-price elasticity, like other behavioral effects, is not an inherent "property" of a drug, but is determined by several variables including the context in which it is measured (Hughes, Higgins, & Bickel, 1988). Indeed, the presence of other reinforcers can alter own-price elasticity (Hursh, 1984; Bickel et al., 1995). Within an economic framework, reinforcer interactions lie on a continuum that can be quantified with a measure termed cross-price elasticity (i.e., proportional change in consumption of reinforcer A as a function of the price of reinforcer B) (Hursh & Bauman, 1987). At one end of the continuum, reinforcers are substitutable reinforcers; that is, as the price of one reinforcer increases (e.g., price of attending a movie theater), consumption of a second reinforcer (i.e., the substitute) will increase (e.g., video rentals). At the other end of the continuum, reinforcers can be complementary; that is, as the cost of one reinforcer increases (e.g., soup), the consumption of a second reinforcer (i.e., the complement) also decreases (e.g., soup crackers). Between these two extremes are independent reinforcers; as the cost of one reinforcer increases (e.g., movie attendance), the consumption of a second reinforcer (e.g., soup crackers) will remain unchanged. The quantification of substitutes, compliments, and independent reinforcers is measured by cross-price elasticity values greater than zero, less than zero, and equal to zero, respectively (Hursh & Bauman, 1987).
A review and reanalysis of self-administration studies support this continuum (Bickel et al., 1995). Specifically, the results of sixteen drug self-administration studies that employed concurrently available reinforcers were reanalyzed using the economic measure of cross-price elasticity. In that review, price was defined as responses required per milligram of drug per ingestion (i.e., unit price) (Hursh et al., 1988; Bickel et al., 1990). A wide variety of reinforcers (e.g., cocaine, food, heroin, and cigarettes) across several species (e.g., rats, humans, baboons, and rhesus monkeys) were examined. Overall, the results of reanalysis demonstrated that each of the studies demonstrated one of the three types of interactions specified by economics. Extensions can also be made to clinical settings again by simulations. For example, the Petry and Bickel (1998) study described earlier found that heroin prices affected other drug purchases. The cross-price elasticity measure showed that as the price of heroin rose and heroin purchases decreased, valium and cocaine purchases increased. However, increases in the price of valium did not affect heroin purchases. This suggests an asymmetrical relationship between heroin and valium purchases when the price of one drug increases while the price of the other remains constant.
Collectively, these studies suggest that the availability of a concurrent reinforcer can significantly modulate drug intake. Sometimes, reinforcers interact as substitutes, whereas at other times they function as complements. Introducing a substitute tends to decrease drug consumption, while introducing a complement may increase drug consumption. Unfortunately, few of these studies has prospectively examined these interactions. Additional research that systematically and parametrically examines reinforcer interactions may facilitate a greater understanding of the ways in which the availability of alternative reinforcers increases or decreases drug consumption.
This approach to studying interactions of any reinforcers may have several important implications. First, this research may have implications for understanding behavioral vulnerability. For example, one issue in drug dependence is why only a few people exposed to a drug of abuse go on to become dependent. Perhaps, vulnerable individuals have limited availability to alternative nondrug substitutes and easy access to complements for drug taking (e.g., Carroll, Lac, & Nygaard, 1989). Second, this research may provide a useful way to characterize the various types of polydrug abuse (e.g., Petry & Bickel, 1998). Third, such research may provide an empirical basis for developing treatment strategies; that is, in treatment it may be worthwhile to decrease the response cost of obtaining other substitutable nondrug reinforcers. With regard to complements, this research suggests that drug-abuse treatment may be more successful if consumption of complements is also decreased. For example, Higgins and colleagues (1993) examined the effects of disulfiram (antabuse) therapy among patients abusing cocaine and alcohol. Disulfiram is a medication that is used to deter alcohol use by inducing nausea and vomiting when alcohol is consumed. This study found that supervised disulfiram therapy was associated with significant decreases in alcohol and cocaine use as measured by breath and urine samples. More recently, Carroll and colleagues (1998) examined the effects of three standardized psychotherapy treatments alone and with disulfiram treatment among a large, diverse sample of individuals who abuse cocaine and alcohol. Their results indicated that disulfiram treatment was associated with significantly better retention in treatment and longer duration of abstinence from alcohol and cocaine use. Fourth, behavioral economic analyses of research may predict conditions in which relapse is likely. Relapse may occur when substitutable reinforcer interactions become unavailable and complements become available (Vuchinch & Tucker, 1988, 1996). Thus, the study of reinforcer interactions may increase our understanding of the etiology, maintenance, and treatment of drug dependence as well as relapse.
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Warren K. Bickel
Louis A. Giordano
"Behavioral Economics." Encyclopedia of Drugs, Alcohol, and Addictive Behavior. . Encyclopedia.com. (July 19, 2018). http://www.encyclopedia.com/education/encyclopedias-almanacs-transcripts-and-maps/behavioral-economics
"Behavioral Economics." Encyclopedia of Drugs, Alcohol, and Addictive Behavior. . Retrieved July 19, 2018 from Encyclopedia.com: http://www.encyclopedia.com/education/encyclopedias-almanacs-transcripts-and-maps/behavioral-economics