Noise and bias in eliciting preferences

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Social Preferences under Risk:Ex-Post Fairness vs. Efficiency * Alexia Gaudeul † July 6, 2015Social lotteries are lotteries that are played along with someone else. The experimental literature indicates that risk attitudes depend on how one's situation in the safe alternative compares to that of a peer. Evaluation of the risky alternative also depends on whether the lottery gives equal payoffs ex-post. Experiments usually present payoffs side-by-side (payoff for me, payoff for the other). This draws attention to inequality in payoffs and thus gives weight to fairness concerns. We consider whether showing own payoff as a share of the total payoff changes risk preferences. Showing total payoffs explicitly draws attention to risk at the level of the pair and may thus moderate dislike for negatively correlated lotteries, as those are less risky at the level of the group. We find that a significant minority of subjects keeps on disliking lotteries that lead to ex-post unequal distributions of payoffs. Subjects also tend to prefer taking a risk rather than obtaining safe but unequal payoff distributions. Beyond reconciling findings from the previous literature, we also discuss differences in sensitivity to the social setting across individuals and the relation between social value orientation in safe and in risky settings.JEL Codes: C91, D63, D81

Section: Paymentmentioning

confidence: 99%

Social Preferences Under Risk: Ex-Post Fairness vs. Efficiency

Gaudeul

2015

“…Theory with a transformed outcome domain 4 . Relative utility is expressed in terms of probability (of winning the main prize or fulfilling aspirations) and does not require any hypothetical "utils" to describe people's behavior.…”

Section: 5mentioning

confidence: 99%

Multi-Outcome Lotteries: Prospect Theory vs. Relative Utility

Kontek

2010

This paper discusses two approaches for the analysis of multi-outcome lotteries. The first uses Cumulative Prospect Theory. The second is the Relative Utility Function, which strongly resembles the utility function hypothesized by Markowitz (1952). It is shown that the relative utility model follows Expected Utility Theory with a transformed outcome domain.An illustrative example demonstrates that not only it is a simpler model, but it also provides more sound predictions regarding certainty equivalents of multi-outcome lotteries. The paper discusses estimation procedures for both models. It is noted that Cumulative Prospect Theory has been derived using two-outcome lotteries only, and it is hard to find any evidence in the literature of its parameters ever having been estimated by using lotteries with more than two outcomes. Least squares (mean) and quantile (including median) regression estimations are presented for the relative utility model. It turns out that the estimations for two-and threeoutcome lotteries are essentially the same. This confirms the correctness of the model and vindicates the homogeneity of responses given by subjects. An additional advantage of the relative utility model is that it allows multi-outcome lotteries, together with the estimation results, to be presented on a single graph. This is not possible using Cumulative Prospect Theory.JEL classification: C13, C21, C51, C91, D03, D81, D87

“…Besides the sample "average" or extreme values may be found in most of 1 The author is grateful to Stefan Traub from the Department of Economics, University of Bremen and Ulrich Schmidt from the Department of Economics, University of Kiel, for making the results of their experiments available. 2 Contact: ul. Chrościckiego 93/105, 02-414 Warsaw, Poland, e-mail: kontek@artal.com.pl, kkontek2000@yahoo.com.…”

Section: 1mentioning

confidence: 99%

Classifying Behaviors in Risky Choices

Kontek

2010

This paper presents a nonparametric approach to classification of data from lottery experiments. Using very basic mathematical tools the paper endeavors to answer the questions:How to determine the "average" subject in a group? How to find a subject presenting the most similar behavior to a given one? How to detect outlier subject(s)? How to classify behaviors by their dissimilarity from the perfectly rational decision maker? How to rank subjects by risk attitudes? How to cluster subjects? This paper demonstrates that the answer to all of these questions may be found non-parametrically, without the use of any specific model. JEL classification: C02, C14, C81, C91, D03, D81