Virtually all current theories of choice under risk or uncertainty are cognitive and consequentialist. They assume that people assess the desirability and likelihood of possible outcomes of choice alternatives and integrate this information through some type of expectation-based calculus to arrive at a decision. The authors propose an alternative theoretical perspective, the risk-as-feelings hypothesis, that highlights the role of affect experienced at the moment of decision making. Drawing on research from clinical, physiological, and other subfields of psychology, they show that emotional reactions to risky situations often diverge from cognitive assessments of those risks. When such divergence occurs, emotional reactions often drive behavior. The risk-as-feelings hypothesis is shown to explain a wide range of phenomena that have resisted interpretation in cognitive-consequentialist terms.

Elicitation is a key task for subjectivist Bayesians. While skeptics hold that it cannot (or perhaps should not) be done, in practice it brings statisticians closer to their clients and subjectmatter-expert colleagues. This paper reviews the state-of-the-art, reflecting the experience of statisticians informed by the fruits of a long line of psychological research into how people represent uncertain information cognitively, and how they respond to questions about that information. In a discussion of the elicitation process, the first issue to address is what it means for an elicitation to be successful, i.e. what criteria should be employed? Our answer is that a successful elicitation faithfully represents the opinion of the person being elicited. It is not necessarily “true ” in some objectivistic sense, and cannot be judged that way. We see elicitation as simply part of the process of statistical modeling. Indeed in a hierarchical model it is ambiguous at which point the likelihood ends and the prior begins. Thus the same kinds of judgment that inform statistical modeling in general also inform elicitation of prior distributions.

Cosmides and Tooby (1996) increased performance using a frequency rather than probability frame on a problem known to elicit base-rate neglect. Analogously, Gigerenzer (1994) claimed that the conjunction fallacy disappears when formulated in terms of frequency rather than the more usual single-event probability. These authors conclude that a module or algorithm of mind exists that is able to compute with frequencies but not probabilities. The studies reported here found that base-rate neglect could also be reduced using a clearly stated single-event probability frame and by using a diagram that clarified the critical nested-set relations of the problem; that the frequency advantage could be eliminated in the conjunction fallacy by separating the critical statements so that their nested relation was opaque; and that the large effect of frequency framing on the two problems studied is not stable. Facilitation via frequency is a result of clarifying the probabilistic interpretation of the...

this article, we observe that the evidence for implicit subadditivity is mixed, and we present several examples of the opposite pattern, implicit superadditivity, observed when unpacking an event decreases its perceived probability. We show that an analysis of the way that people represent category structure helps one to characterize the circumstances that lead to implicit subadditivity, additivity, or implicit superadditivity. In particular, we find that the probability assigned to an unpacked description is proportional to the typicality of instances used to unpack the category, that is, to the degree that they are good representatives of the category being judged

Marketers face a myriad of decisions when developing a Web site for ECommerce. What advice can we supply based upon our current understanding of consumer behavior? We attempt to organize streams of research that address the development of marketplaces for the digital economy. We start by characterizing computer-based decision environments as Marketplaces of the Artificial, arguing that the unbundling of product information from products presents many decisions and opportunities for the design of decision environments. We then review four areas of research, identifying themes in each area. These are: 1) The economics of search, 2) Cognitive cost approaches, 3) Constructive preference approaches, and 4) Phenomenological approaches. We illustrate each approach by discussing examples of research questions and results. While this organization is approximate, it serves to highlight the underlying assumptions and questions addressed by each perspective. 1 Introduction When visiting a World...

What is the nature of moral behavior? According to the study of bounded rationality, it results not from character traits or rational deliberation alone, but from the interplay between mind and environment. In this view, moral behavior is based on pragmatic social heuristics rather than moral rules or maximization principles. These social heuristics are not good or bad per se, but solely in relation to the environments in which they are used. This has methodological implications for the study of morality: Behavior needs to be studied in social groups as well as in isolation, in natural environments as well as in labs. It also has implications for moral policy: Only by accepting the fact that behavior is a function of both mind and environmental structures can realistic prescriptive means of achieving moral goals be developed.

The authors examine how a constructive preferences perspective might change the prevailing view of medical decision making by suggesting that the methods used to measure preferences for medical treatments can change the preferences that are reported. The authors focus on 2 possible techniques that they believe would result in better outcomes. The 1st is the wise selection of default options. Defaults may be best applied when strong clinical evidence suggests a treatment option to be correct for most people but preserving patient choice is appropriate. The 2nd is the use of environments that explicitly facilitate the optimal construction of preferences. This seems most appropriate when choice depends on a patient’s ability to understand and represent probabilities and outcomes. For each technique, the authors describe the background and literature, provide a case study, and discuss applications.

Summary. Tradeoff studies involving human subjective calibration and data updating are often distrusted by decision makers. A review of objectivity and subjectivity in decision making confirms that prospect theory is a good model for actual human decision making. Relationships between tradeoff studies and the elements of experiments in judgment and decision making show that tradeoff studies are susceptible to human cognitive biases. Examples of relevant biases are given. Knowledge of these biases should help give decision makers more confidence in tradeoff studies. 1

Vries, Joel van der Weele, and two anonymous referees for helpful comments on earlier versions of

In most contemporary approaches to decision making, a decision problem is described by a sets of states and set of outcomes, and a rich set of acts, which are functions from states to outcomes over which the decision maker (DM) has preferences. Most interesting decision problems, however, do not come with a state space and an outcome space. Indeed, in complex problems it is often far from clear what the state and outcome spaces would be. We present an alternative foundation for decision making, in which the primitive objects of choice are syntactic programs. A representation theorem is proved in the spirit of standard representation theorems, showing that if the DM’s preference relation on objects of choice satisfies appropriate axioms, then there exist a set S of states, a set O of outcomes, a way of interpreting