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.

In recent years, descriptive models of risky choice have incorporated features that reflect the importance of particular outcome values in choice. Cumulative prospect theory (CPT) does this by inserting a reference point in the utility function. SP/A (security-potential/aspiration) theory uses aspiration level as a second criterion in the choice process. Experiment 1 compares the ability of the CPT and SP/A models to account for the same within-subjects data set and finds in favor of SP/A. Experiment 2 replicates the main finding of Experiment 1 in a between-subjects design. The final discussion brackets the SP/A result by showing the impact on fit of both decreasing and increasing the number of free

How do human beings make wage comparisons? This paper provides empirical support for an approach suggested by the psychologist Allen Parducci. The paper combines an experimental study with an analysis of data on 16,000 British employees. Satisfaction levels are shown to depend not simply upon relative pay but upon an individual’s ordinal rank within a comparison group (for example, whether the individual is 4th or 34th in the wage hierarchy of the company). Moreover, consistent with Parducci’s theory, quits in a workplace are higher the greater is the positive skewness of the pay distribution. This research was supported by grants 88/S15050 from BBSRC (UK) and grants R000239002 and R000239351 from ESRC (UK). Any opinions in this article are those of the individual authors only; they do not necessarily reflect the views or policies of Watson Wyatt. For helpful suggestions, we are

We develop a positive behavioral portfolio theory and explore its implications for portfolio construction and security design. Portfolios within the behavioral framework resemble layered pyramids. Layers are associated with distinct goals and covariances between layers are overlooked. We explore a simple two-layer portfolio. The downside protection layer is designed to prevent financial disaster. The upside potential layer is designed for a shot at becoming rich. Behavioral portfolio theory has predictions that are distinct from those of meanvariance portfolio theory. In particular, behavioral portfolio theory is consistent with the reluctance to have short and margined positions, an inverse relation between the bond/stock ratio and portfolio riskiness, the existence of the home bias, the use of labels such as “growth ” and “income, ” the preference for securities with floors on returns, and the purchase of lottery tickets. 2 Behavioral Portfolio Theory We develop behavioral portfolio theory as a descriptive theory, an alternative to

This review article attempts to highlight from my personal perspective some of the major developments in the representational theory of measurement during the past 50 years. Emphasis is placed on the ongoing interplay between the development of abstract theory and the attempts to apply it to empirically testable phenomena. The article has four major sections. The first concerns classical representational measurement, which was the successful attempt to formulate the major measurement methods of classical physics: extensive and additive conjoint structures, their distributive interlock in dimensional analysis, and intensive (averaging) structures. The second illustrates a nontrivial behavioral example using both extensive and conjoint measurement plus functional equations to arrive at rank- and sign-dependent utility (also called cumulative prospect) representations for decision making under risk. The third section, contemporary representational measurement, somewhat overlaps the classical one but includes new findings and approaches: representations of nonadditive concatenation and conjoint structures; a general theory of scale types; results for general, finitely unique, homogeneous structures; structures that are homogeneous between singular points; generalized distributive triples; and a generalization of dimensional analysis to include any ratio scalable attribute; and the concept of meaningfulness. The final section concerns applications of the latter ideas to psychophysical scaling and merging functions.] 1996 Academic Press, Inc. 1.

y be factored into the decision more heavily than is price. The execu- tive may choose to ride dow-ntown by taxi and then implement this decision by standing on line and taking a taxi to the hotel. To bring these sorts of decision situations into the laboratory, researchers commonly focused on the goal of obtaining money, which they assume is shared across people. In the prototypical task, subjects are given choice options that differ in probability and amount. The use of gambles enabled researchers to explore decision making under risk. Often, a number of different choices are made in a single experimental session, and the pattern of choices across sets is analyzed. For ample, people might be asked whether they prefer a 45% chance to win $200 or a 50% chance to win $150. Later in the same ses sion, they might be asked whether they prefer a 90% chance to win $200 or a 100% chance to win $150. At issue in studies like these is the consistency of people's choices. The anal- yses would in

Decision theorist Howard Raiffa [1968] introduces useful distinctions among three approaches to the analysis of decisions. Normative analysis is concerned with the rational solution to the decision problem. It defines the ideal that actual decisions should strive to approximate. Descriptive analysis is concerned with

Abstract. Inspired by much empirical evidence of human decisionmaking under risk that does not coincide with expected value maximization, much effort has been invested into the development of descriptive theories of human decision-making involving risk (e.g. Prospect Theory, Regret Theory, SP/A Theory). An open question is how behavior corresponding to these descriptive models could have been learned or arisen evolutionarily, as the described behavior differs from expected value maximization. We believe that the answer to this question lies, at least in part, in the interplay between risk-taking and sequentiality of choice in evolutionary environments. We provide simulation results for evolutionary game environments where sequential decisions are made between risky and safe choices. Our results show there are evolutionary games in which agents that are sometimes risk-prone and sometimes risk-averse can outperform agents that make decisions solely based on the maximization of the local expected values of the outcomes. 1

Abstract. There is much empirical evidence that human decisionmaking under risk does not correspond the decision-theoretic notion of “rational ” decision making, namely to make choices that maximize the expected value. An open question is how such behavior could have arisen evolutionarily. We believe that the answer to this question lies, at least in part, in the interplay between risk-taking and sequentiality of choice in evolutionary environments. We provide analytical and simulation results for evolutionary game environments where sequential decisions are made between risky and safe choices. Our results show there are evolutionary games in which agents with state-dependent risk preferences (i.e., agents that are sometimes riskprone and sometimes risk-averse depending on the outcomes of their previous decisions) can outperform agents that make decisions solely based on the local expected values of the outcomes. 1

There is much empirical evidence that human decision-making under risk does not coincide with expected value maximization, and much effort has been invested into the development of descriptive theories of human decision-making involving risk (e.g. Prospect Theory). An open question is how behavior corresponding to these descriptive models could have been learned or arisen evolutionarily, as the described behavior differs from expected value maximization. We believe that the answer to this question lies, at least in part, in the interplay between risk-taking, sequentiality of choice, and population dynamics in evolutionary environments. In this paper we provide the results of several evolutionary game simulations designed to study the risk behavior of agents in evolutionary environments. These include several evolutionary lottery games where sequential decisions are made between risky and safe choices, and an evolutionary version of the well-known stag hunt game. Our results show how agents that are sometimes risk-prone and sometimes risk-averse can outperform agents that make decisions solely based on the maximization of the local expected values of the outcomes, and how this can facilitate the evolution of cooperation in situations where cooperation entails risk. 1.