2.4 Rational Behavior We introduce the concept of rational behavior here, because it is often closely tied to discrete choice analysis. However, it is important to note that nothing in the discrete choice analysis framework requires the assumption of rationality. The common use of the term “rational behavior” is based on the beliefs of an observer about what the outcome of a decision should be. Obviously different observers may have varying beliefs and may assume different objective functions. Thus, as colloquially used, the notion of rationality is not a useful concept in describing individual behavior. In the scientific literature the concept is used to describe the decision process itself. In general, it means a consistent and calculated decision process in which the individual follows his or her own objectives, whatever they may be. It stands in contrast to impulsiveness, in which individuals respond to choice situations in different ways depending on their variable psychological state at the time a decision is made. It also assumes individuals make decisions without error and/or biases that would lead to the individual making a decision not in his or her own interests. The classical concept of perfect rationality assumes an omniscient individual who can gather and store large quantities of information, perform very complex computations, and make consistently optimal decisions based on those computations that are in her best interest. We further define the rationality assumption later when discussing microeconomic consumer theory, and we revisit the rationality assumption at the end of this chapter when discussing broader behavioral theories. 2.8 Beyond Rationality Above we described decision-makers as being rational. That they have full information on all available alternatives and are able to accurately calculate and compare the value of options before choosing to follow the course of action that is best for them. However, human beings have cognitive limitations: our abilities as problem solvers are constrained by our limited information gathering and processing capabilities. Behavioral science researchers have a long history of raising serious questions about the rationality assumption. Their research has often succeeded in pointing out seemingly inconsistent and non-sensible choices. Other behavioral researchers aren’t driven by refuting rationality, but are interested in understanding and influencing behavior, such as in psychology, marketing and the health science and in this quest have developed various theories of the behavioral process that are in many ways far from the economics-based paradigm emphasized above. In this section we provide a brief introduction to this vast literature, starting first with the literature more closely linked with the rationality discussion and following with a discussion of behavioral theories from other domains. Amongst the earliest theories to diverge from rationality was proposed by Simon (1957). He recognized cognitive limitations that would preclude rational behavior and suggested the notion of bounded rationality as a more realistic representation. He postulated that individuals when faced with a complicated choice situation employ heuristics and rules of thumb to reduce the complexity of the original problem, and are subsequently rational within the resulting simplified framework. Thus bounded rationality regards individual decision-makers as “satisficers” - they seek a satisfactory solution rather than an optimal one. Such heuristics and rules of thumb can manifest themselves in several different ways and can result in often surprising departures from perfect rationality. The psychologists Daniel Kahneman and Amos Tversky led early work in this area. Kahneman was awarded the 2002 Nobel Prize in Economics for their work (Kahneman, 2003), six years after the death of Tversky. Their experiments repeatedly demonstrated how the use of heuristics for making probability judgments under uncertainty could result in systematic errors, or cognitive biases. Examples of cognitive biases include loss aversion, which implies that the disutility of giving up an object is greater than the utility associated with acquiring it, or that individuals attach a higher value to a commodity that they already own than to an identical commodity that they do not; anchoring, which refers to situations where individuals disproportionately weigh one specific piece of information, and readjust or reinterpret other information to conform to the anchored information; and framing, which is when the same information when presented differently to the same individual can lead her to surmise a different conclusion. Numerous other cognitive biases have been identified, and their implications for decision-making theory comprise an ongoing field of research. The work of Kahneman and Tversky forms the foundation of the field of behavioral economics, which is a blend of psychology and microeconomics. Behavioral economics has sought to bring greater psychological realism to neoclassical economic models of how individuals make decisions, and has attempted to expand our understanding of what it means to be rational. Kahneman and Tversky (2000) contains some of the seminal papers in the field and Ariely (2010) and Thaler and Sunstein (2009) provide particularly accessible introductions to more recent developments. They discuss issues such as lack of self-control and our all too frequent tendency to indulge in short-term behavior that is at odds with our long-term interests. The need for instant gratification can result in erratic and potentially deleterious actions, such as procrastinating, smoking, eating unhealthily, or not saving enough for retirement. On the positive side of human behavior, individuals often exhibit preferences that deviate from pure self-interest and are often rooted in considerations of fairness and the welfare of others. For example, experiments have repeatedly found that individuals when free to allocate money between themselves and others do not keep the money all to themselves. Evidence also shows that individuals are reciprocal: they are nicer to people who are nice to them, and unduly cruel to people who are cruel to them, above and beyond what they should be if self-interest were their only concern. For example, seeking revenge even when there is no material gain to be had from its attainment would be an instance where reciprocity conflicts with the definition of perfect rationality. Psychologists have long stressed the importance of the cognitive processes on choice behavior (Payne et al., 1992, and Prelec, 1991). Far from the concept of innate, stable preferences that are the basis of traditional discrete choice models, they emphasize the importance of experience and circumstances and a whole host of amorphous concepts, such as context, knowledge, point of view, degree of complexity, familiarity, risk of the choice at hand, and the use of non-utility maximizing decision protocols such as problem-solving, reason-based, and rule-driven processes. Explicit frameworks for modeling the behavioral choice process have been developed in a number of fields. The Theory of Planned Behavior (TPB, Ajzen, 1991) from psychology emphasizes the influences of attitudes, social norms, behavioral control and intention. The TPB framework has been extended in the Model of GoalDirected Behavior (MGB, Perugini and Bagozzi, 2001) to include emotions, habits, and behavioral desire to the framework. Psychometricians, in their quest to understand behavioral constructs and causal relationships, have pioneered the use of psychometric data, for example, answers to direct survey questions regarding attitudes, perceptions, motivations, affect, etc. A general approach to synthesizing models with latent variables and psychometric-type measurement models has been advanced by a number of researchers including Keesling (1972), Joreskog (1973), Wiley (1973), and Bentler (1980). In marketing, the emphasis of the behavioral process is on the dynamics of information acquisition and the influence of marketing triggers in product evaluation. For example, the Consumer Process Model (CDP, Blackwell et al., 2005) is a seven stage model that consists of need recognition, search, pre-purchase evaluation, purchase, consumption, post-consumption evaluation, and divestment. The health sciences have their own model consisting of behavioral stages, but their model is motivated by the desire to modify destructive behaviors such as drug use and poor nutrition. The Transtheoretical Model of Behavior Change (TTM, Prochaska and Velicer, 1997), also known as the stages of change, emphasizes that health behavior change involves a staged process which starts at pre contemplation, and moves through contemplation, preparation, action, maintenance and then either relapse or termination. Also focusing on behavior change, but more from a computer science orientation is the rapidly growing persuasive technology field. The Fogg Behavioral Model (FBM, Fogg, 2009) espouses that behavior change occurs when motivation, ability, and trigger occur at the same time and further elaborates on how to motivate, simplify (ability), and trigger people to achieve the target behavior. And this is all just to name a few of the behavioral frameworks that have been developed. The angles by which one can study and structure human behavior are endless. As implied by the discussion above, there is a large gap between behavioral theory by psychologists and behavioral researchers and discrete choice models. This gap arises from the difference in driving forces behind the two disciplines. While discrete choice modelers aim for operational, quantitative models of choice behavior, the profession would do well to study behavioral research and incorporate, where relevant, these ideas. Indeed, much of the motivation for the advances in discrete choice analysis that are presented in this text, are the result of efforts to improve the behavioral realism of quantitative models of choice. McFadden (1999) provides a summary of behavioral science research from a discrete choice modeler’s view. He argues that “most cognitive anomalies operate through errors in perception that arise from the way information is stored, retrieved, and processed” and that “empirical study of economic behavior would benefit from closer attention to how perceptions are formed and how they influence decision-making.” We wholeheartedly agree. Since a prime objective is to model behavior, it is important to recognize the potential role of other fields such as psychology and behavioral sciences in this discipline. In developing discrete choice models, it is important not to lose sight of the underlying behavioral processes that are driving the behavior. Focusing too much on the statistical formulation confines the development process, whereas advanced discrete choice approaches are immensely capable of capturing complex behavioral dynamics. [[📜Gershman15_comp_rationality]],