In a controlled laboratory experiment we use one sample of college students and one of mature executives to investigate how positive skew influences risky choices. In reduced-form regressions we find that both students and executives make riskier choices when lotteries display positive skew. We estimate decision models to explore three explanations for skew seeking choices: risk-loving, optimism and likelihood insensitivity. We find no role for love for risk as neither students nor executives have convex utility. Both optimism and likelihood insensitivity (...) play a part in skew seeking choices. Likelihood insensitivity is larger for students than for executives. Executives have more concave utility and are more optimistic than students, but this is found to be largely due to them being older. (shrink)

This paper explores the role of inequity aversion as an explanation for observed behavior in experimental Cournot oligopolies. We show that inequity aversion can change the nature of the strategic interaction: quantities are strategic substitutes for sufficiently asymmetric output levels but strategic complements otherwise. We find that inequity aversion can explain why: some experiments result in higher than Cournot–Nash production levels while others result in lower, collusion often occurs with only two players whereas with three or more players market outcomes (...) are very close to Cournot–Nash, and players often achieve equal profits in asymmetric Cournot oligopoly. (shrink)

We propose a task for eliciting attitudes toward risk that is close to real-world risky decisions which typically involve gains and losses. The task consists of accepting or rejecting gambles that provide a gain with probability p\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document} and a loss with probability 1-p\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1-p$$\end{document}. We employ finite mixture models to uncover heterogeneity in risk preferences and find that behavior is heterogeneous, with one half (...) of the subjects behaving as expected utility maximizers, for the others, reference-dependent models perform better than those where subjects derive utility from final outcomes, models with sign-dependent decision weights perform better than those without, and there is no evidence for loss aversion. The procedure is sufficiently simple so that it can be easily used in field or lab experiments where risk elicitation is not the main experiment. (shrink)

Research in psychology suggests that some individuals are more sensitive to positive than to negative information while others are more sensitive to negative rather than positive information. I take these cognitive positive–negative asymmetries in information processing to a Bayesian decision-theory model and explore its consequences in terms of decisions and payoffs. I show that in monotone decision problems economic agents with more positive-responsive information structures are always better off, ex ante, when they face problems where payoffs are relatively more sensitive (...) to the action chosen when the state of nature is favorable. (shrink)

This paper uses a field survey to investigate the quality of individuals’ beliefs of relative performance in tournaments. We consider two field settings, poker and chess, which differ in the degree to which luck is a factor and also in the information that players have about the ability of the competition. We find that poker players’ forecasts of relative performance are random guesses with an overestimation bias. Chess players also overestimate their relative performance but make informed guesses. We find support (...) for the “unskilled and unaware hypothesis” in chess: high-skilled chess players make better forecasts than low-skilled chess players. Finally, we find that chess players’ forecasts of relative performance are not efficient. (shrink)