competitive pressures from others such that they disappear or cease to operate if they continually make bad decisions while their competitors make better decisions. A systematic error in judgment may be considered as an added cost to production or a competitive disadvantage. Because of this, in the context of a competitive industry, many have argued that behavioral economics has no place because firms that fail to maximize profit will be driven from the marketplace by smarter firms. Moreover, many behaviors that are rational under the utility-maximization model are not admissible under profit maximization. The models allow for differences in taste but not for differences in the measurement of profit. Thus those whose preferences get in the way of profit maximization may also be pushed out of the market by competitive pressures. This places a heavier burden on behavioral economists to prove the existence of behavior that is inconsistent with profit maximization by firms in cases where they believe profit is not truly the only motive.

Importantly, analysis using a rational model limits the scope of benevolent policy. By assuming that people have made the best choice possible, only policies that deal in interpersonal effects of economic behavior may improve an individual’s well-being. Thus, a person who decides to smoke cigarettes, or, in a more extreme case, jump off of a bridge, cannot be made better off by a government that wishes to stop them. The rational model assumes these people knowingly chose the outcome that would make them the best off they could be. However, a secondhand smoker, who unwillingly inhales the smoke of nearby smokers, may be made better off if a policymaker limits the ability of others to smoke. In general, the rational model cannot suggest ways to abridge the choice of an individual to make that individual better off. In this sense, the rational model is not therapeutic.

Bounded Rationality and Model Types

Although many of the important concepts in behavioral economics precedes him by many years, the current incarnation of behavioral economics owes much to the work of Herbert Simon. Simon first described the notion of bounded rationality. Specifically, this is the notion that whereas people might have a desire to find the optimal decision, they have limits on their cognitive abilities, limits on access to information, and perhaps limits on other necessary resources for making decisions. Because of these limitations, rather than optimize, people seek to simplify their decision problem by narrowing the set of possible choices, by narrowing the characteristics of outcomes that they might consider, or by simplifying the relationships between choices and outcomes. Thus, instead of optimizing by making the best overall choice, a boundedly rational person instead optimizes using some simplified decision framework. Naturally, this simplified decision framework depends directly on the particular decision resources available to that person. Hence, the proximity of the individual decision to the rational optimum depends not only on the structure of the problem and the information available but also on the characteristics of the person making the decision. Hence, education, experience, emotion, time pressure, stress, or the need to make multiple decisions at once might play directly into the accuracy of the individual decision maker. The decision mechanism may be termed a heuristic, or a simple general rule that may

be used to approximate the solution to the utility or profit-maximization problem. The heuristic most likely results in a close approximation of the true optimum under most circumstances. It is this ability to approximate the optimal choice that makes it useful to the decision maker. However, there may be some circumstances under which the differences are substantial and observable.

Economists have taken several approaches to modeling boundedly rational behavior. Two primary approaches are of particular importance. The first approach is a behavioral model. A behavioral model seeks to simply describe observed behavior. In some cases, it augments a rational model of behavior with some function or appendage that describes the observed deviations from rational decision making. One advantage of such a model is that it is based in empirical observations, and it is thus extremely accurate in the context in which observations were made. Additionally, behavioral models can be used to describe any type of behavior, because they are not based on any particular assumptions about the underlying motivations of the individual. For this same reason, however, behavioral models might not be the best tool for many jobs. Because the model is observation based, it is only as accurate as the observations taken. Thus, if we changed the decision context substantially, the model might no longer be appropriate. For example, we might repeatedly observe someone with two food objects placed in front of him: an apple on the left and a lemon on the right. Suppose each time we observe a choice, the person chooses the apple. One behavioral model might suggest the person always chooses the object on the left. If we then used this model to predict what would happen if a lemon were placed on the left and an apple on the right, we would be disappointed if the individual were actually choosing the object that delivered a preferred taste.

The downfall of the behavioral model is that it does not tell us why, only what. Thus, we cannot generalize the behavioral model to various contexts and decisions. To do this, we would need to understand the actual decision mechanism underlying the decision. Additionally, because the behavioral model does not yield the individual motivation for decisions, it provides an inappropriate instrument for trying to help someone to make better decisions. By simply describing the types of behavior observed, a behavioral model does not provide any rationale for how someone may be made better off. Thus again, our model might describe what behaviors or conditions are associated with deciding to smoke. However, this alone does not tell us if the person would be better off if the choice to smoke were removed by some policymaker. Alternatively, a behavioral model may be very appropriate for making predictions in highly similar contexts. For example, a firm marketing a product may derive and estimate a behavioral model of consumer purchases for the product. So long as the underlying decision problems of the consumers remain the same, the behavioral model may be very accurate and appropriate for their particular marketing efforts.

An alternative approach is to attempt to model the motivation for the decision mechanism. We call this a procedurally rational model. A person is procedurally rational if his or her decision is the result of logical deliberation. This deliberation might include misperceptions or other constraints, but the process by which the decision is arrived at itself is reasoned. Thus, a procedural rational model attempts to provide a reasoned decision mechanism that might not always arrive at the correct choice owing to misperceptions, limits on cognitive ability, or other constraints on decision resources. Given the decision motivations are properly modeled, a procedurally rational model may

be highly predictive of general behavior over vastly different contexts. Additionally, given the model of motivations is accurate, the model naturally suggests a set of normative behaviors. For example, if a particular demographic of people typically begin smoking because they overestimate the benefits of belonging to the particular social group that smokes, a policy limiting the availability of cigarettes to this demographic might be justified.

Special Relationship between Behavioral

Economics and Experimental Economics

Many of the most prominent behavioral economics concepts have their roots in experimental economics research. Although this book focuses more exclusively on behavioral economics theory, it is useful to note why a particular theory would have such a close relationship to a particular methodology. This has much to do with the assumption of rationality in general economics.

Throughout the history of economics, the vast majority of empirical research has employed secondary data sets to explore the relationships implied by theory. A secondary data set records the transactions that have occurred in the past and potentially some data on the demographic and economic qualities of the persons making the decisions. These transactions occur naturally without any opportunity for the researcher to manipulate conditions or parameters that could affect the decisions. To use such data to understand the underlying relationships, one must use a mathematical model to interpret the behavior. Thus, to estimate the way consumption will change with a change in price, you must have a theory that allows you to derive a demand curve that may then be estimated. Referring to the two-good consumption problem we explored earlier in the chapter, the demand curve in this case tells us that the quantity of good 1 demanded depends negatively on the price of good 1, positively on the price of good 2, and in some unspecified way on income. Our ability to test our model is somewhat limited by the particular data on hand. Often between any pair of observations all three of these variables will change depending on other conditions that face decision makers and that the researcher might not directly care about (e.g., supply conditions), leaving no clear prediction of the direction of change in demand. Nonetheless, we can use our models of supply and demand to interpret estimates of the relationships between the prices and income derived from these secondary data.

Alternatively, economics experiments offer the researcher tremendous control to alter the variables that independently influence decisions. Typically, an experiment brings a large number of participants into a laboratory where they make decisions that will be rewarded monetarily or substantially, with the reward structure designed by the researcher. This reward system is changed between various treatment groups in order to test some underlying theory of behavior. Thus, a researcher could run experiments on a random sample of participants and determine if they were willing to purchase good 1 with p1 = $1, p2 = $1 after having endowed the participants with $10. A second treatment could increase p1 while holding the budget and the price of good 2 constant. If consumption of good 1 increased, we would have a rejection

of our rational model. Alternatively, if consumption decreased we would fail to reject the rational model. Failing to reject does not mean that the rational model (or any other model we fail to reject) is the true underlying model. Rather, we only find that the behavior in our particular experiment is consistent with this assumed model. Other experiments using other variable values may be found to reject the model. This ability to discern a causal link between some decision variable and observed behavior is called internal validity.

Although there are circumstances that allow us to test rational models using secondary data, these are certainly rare and more difficult than when using an experimental approach. The experimental approach allows us the direct control to set up choices where an obvious violation of rational choice is possible. Real-world observations have so many variables—many of them unobservable—that such clear violations are usually not discernable. For this reason, behavioral economics is closely associated with experimental economics. On the other hand, we must recognize that an economics experiment cannot control all important decision parameters. For example, in this experiment we suggest endowing each participant with $10. However, some participants may be wealthier than others and thus have a different sensitivity to price changes. It would be difficult to control wealth except perhaps by targeting a particular wealth cohort. This is difficult in practice. Alternatively, preferences might differ among participants in a way that influences the outcome of our result. For example, if good 1 were pork, and some subjects rejected pork on religious grounds, we might find no relationship to price. However, this is not a rejection of our model. Rather, the participants simply have extremely low utility for pork.

It would be difficult to take behavioral relationships estimated in an experiment and directly apply them to policy in a market. Suppose for example, we find some set of conditions under which the rational model fails in the laboratory. Before this anomaly could be of use to a policy maker, we would need to know first how likely it would be that these conditions would ever occur in a natural market. It could be that the necessary conditions are extremely rare or even impossible in a natural market setting. Second, we would need to know whether the magnitude of the effect was sufficiently important in a broader context. For example, we might find a violation of the law of demand for some range of prices. But if the violation is a relatively small effect or over a very small range of prices, it might not be possible for a producer to determine if increasing price in this range truly increases sales.

Empirical estimates from field data are usually much more readily generalized to other conditions so long as the underlying model being estimated is correct. This ability to apply an estimated relationship more broadly is called external validity. Internal validity is necessary in order to find evidence of a behavioral economics result, but we desire external validity before we can begin to apply the model in a forecasting, managerial, marketing, or policymaking exercise. Without having both pieces of the puzzle, we cannot move ahead with confidence in our results. Thus, although behavioral economics has been associated closely with experimental economics, there have been strong movements both to extend experiments to more natural settings and to use secondary data to estimate the behavioral relationships implied by behavioral economics theory.