We develop a model that fleshes out, extends, and modifies existing models of referencedependent preferences and loss aversion while accomodating most of the evidence motivating these models. Our approach makes reference-dependent theory more broadly applicable by avoiding some of the ways that prevailing models—if applied literally and without ancillary assumptions—make variously weak and incorrect predictions. Our model combines the reference-dependent gain-loss utility with standard economic “consumption utility ” and clarifies the relationship between the two. Most importantly, we posit that a person’s reference point is her recent expectations about outcomes (rather than the status quo), and assume that behavior accords to a personal equilibrium: The person maximizes utility given her rational expectations about outcomes, where these expectations depend on her own anticipated behavior. We apply our theory to consumer behavior, and emphasize that a consumer’s willingness to pay for a good is endogenously determined by the market distribution of prices and how she expects to respond to these prices. Because a buyer’s willingness to buy depends on whether she anticipates buying the good, for a range of market prices there are multiple personal equilibria. This multiplicity disappears when the consumer is sufficiently uncertain about the price she will face. Because paying more than she anticipated induces a sense of loss in the buyer, the lower the prices at which she expects to buy the lower will be her willingness to pay. In some situations, a known stochastic decrease in prices can even lower the quantity demanded.

People often choose intuitive rather than equally valid nonintuitive alternatives. The authors suggest that these intuitive biases arise because intuitions often spring to mind with subjective ease, and the subjective ease leads people to hold their intuitions with high confidence. An investigation of predictions against point spreads found that people predicted intuitive options (favorites) more often than equally valid (or even more valid) nonintuitive alternatives (underdogs). Critically, though, this effect was largely determined by people’s confidence in their intuitions (intuitive confidence). Across naturalistic, expert, and laboratory samples (Studies 1–3), against personally determined point spreads (Studies 4–11), and even when intuitive confidence was manipulated by altering irrelevant aspects of the decision context (e.g., font; Studies 12 and 13), the authors found that decreasing intuitive confidence reduced or eliminated intuitive biases. These findings indicate that intuitive biases are not inevitable but rather predictably determined by contextual variables that affect intuitive confidence.

Emotions influence our decisions. They do so in just about every walk of our lives, whether we are aware or unaware of it and whether we acknowledge it or not. In particular, I will argue that emotions may explain inconsistencies in human behavior and forms of behavior that some have deemed irrational, though such behavior may seem more sensible after a discussion of the functions that emotions serve—or may have once served in our evolutionary past. People do have the capacity to override emotional responses. This capacity relies in large measure on the most recently evolved parts of our brains that support forms of behavior that are more recognizably rational. Neuroscientists are beginning to make headway in identifying the neural mechanisms involved in both emotional responses and higher cognitive processes. This effort offers the promise of a deeper understanding of how and why emotions impact decision making, how this may contribute to behavior that appears to deviate from optimality, and how and when we are able to overcome such emotional responses. In this article I will review a series of human brain imaging studies of decisionmaking behavior. I will argue that the human brain is best understood as a confederation of mechanisms that usually act together, but at times may come into competition with one another, favoring different evaluations of similar circumstances. Modern brain imaging methods allow us to measure this competition and relate it to behavioral outcomes. I will describe examples of these processes, drawn from different domains of psychological function. These examples provide evidence that competition among different brain mechanisms can provide insight into the seemingly inconsistent or irrational responses that are of such interest to behavioral scientists. In particular, I will entertain the idea that a broad range of

A central theme in recent research on attitudes is the distinction between deliberate, “explicit” attitudes and automatic, “implicit” attitudes. The present article provides an integrative review of the available evidence on implicit and explicit attitude change that is guided by a distinction between associative and propositional processes. Whereas associative processes are characterized by mere activation independent of subjective truth or falsity, propositional reasoning is concerned with the validation of evaluations and beliefs. The proposed Associative-Propositional Evaluation Model (APE Model) makes specific assumptions about the mutual interplay of the two processes, implying several mechanisms that lead to symmetric or asymmetric changes in implicit and explicit attitudes. The model integrates a broad range of empirical evidence and implies several new predictions for implicit and explicit attitude change.

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The authors used growth mixture models to study religious development during adulthood (ages 27–80) in a sample of individuals who were identified during childhood as intellectually gifted. The authors identified 3 discrete trajectories of religious development: (a) 40 % of participants belonged to a trajectory class characterized by increases in religiousness until midlife and declines in later adulthood; (b) 41 % of participants belonged to a trajectory class characterized by very low religiousness in early adulthood and age-related decline; and (c) 19 % of participants belonged to a trajectory class characterized by high religiousness in early adulthood and age-related increases. Gender, strength of religious upbringing, number of children, marrying, and agreeableness predicted membership in the trajectory classes. Results were largely consistent with the rational choice theory of religious involvement.

Tradeoff studies are broadly recognized and mandated as the method for simultaneously considering multiple alternatives with many criteria, and as such are recommended in the Capability Maturity Model Integration (CMMI) Decision Analysis and Resolution (DAR) process. Tradeoff studies, which involve human numerical judgment, calibration, and data updating, are often approached with under confidence by analysts and are often distrusted by decision makers. The decision-making fields of Judgment and Decision Making, Cognitive Science and Experimental Economics have built up a large body of research on human biases and errors in considering numerical and criteria-based choices. Relationships between experiments in these fields and the elements of tradeoff studies show that tradeoff studies are susceptible to human mental mistakes: This paper indicates ways to eliminate the presence, or ameliorate the effects of mental mistakes on tradeoff studies. © 2007 Wiley Periodicals, Inc. Syst Eng 10: 222–240, 2007 Key words: tradeoff studies; trade studies; cognitive biases; decision analysis; problem statement; evaluation criteria; weights of importance; alternative solutions; evaluation data;

Negotiation is a complex emotional decision-making process aiming to reach an agreement to exchange goods or services. Although a daily activity, few people are effective negotiators. Existing support systems make a significant improvement if the negotiation space is well-understood, because computers can better cope with the computational complexity. However, the negotiation space can only be properly developed if the human parties jointly explore their interests. The inherent semantic problem and the emotional issues involved make that negotiation cannot be handled by artificial intelligence alone, and a human-machine collaborative system is required. This interest paper presents research goals, ideas, challenges and an approach towards creating the next generation of negotiation support systems.

in biomedical research provides a distorted view of the reality of scientific data that are generated in the laboratory and clinic. This system can be studied by applying principles from the field of economics. The “winners curse”, a more general statement of publication bias, suggests that the small proportion of results chosen for publication are unrepresentative of scientists repeated samplings of the real world. The self-correcting mechanism in science is retarded by the extreme imbalance between the abundance of supply (the output of basic science laboratories and clinical investigations) and the increasingly limited venues for publication (journals with sufficiently high

One of the most important requirements for accurate causal inference is that there be no confounds; the cause being evaluated must occur independently of all other causes. When this requirement is not met, causal inferences are likely to be incorrect. The current study asks participants to judge how informative various situations are with respect to drawing causal inferences. Contrary to normative principles, participants appear to believe that many confounded situations are just as informative as unconfounded situations.