Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

From anomalies to forecasts : toward a descriptive model of decisions under risk, under ambiguity, and from experience

Tools
- Tools
+ Tools

Erev, Ido, Ert, Eyal, Plonsky, Ori, Cohen, Doron and Cohen, Oded (2017) From anomalies to forecasts : toward a descriptive model of decisions under risk, under ambiguity, and from experience. Psychological Review, 124 (4). pp. 369-409. doi:10.1037/rev0000062

[img]
Preview
PDF
WRAP-from-anomalies-to-forecasts-Erev-2017.pdf - Accepted Version - Requires a PDF viewer.

Download (1977Kb) | Preview
Official URL: http://dx.doi.org/10.1037/rev0000062

Request Changes to record.

Abstract

Experimental studies of choice behavior document distinct, and sometimes contradictory, deviations from maximization. For example, people tend to overweight rare events in 1-shot decisions under risk, and to exhibit the opposite bias when they rely on past experience. The common explanations of these results assume that the contradicting anomalies reflect situation-specific processes that involve the weighting of subjective values and the use of simple heuristics. The current article analyzes 14 choice anomalies that have been described by different models, including the Allais, St. Petersburg, and Ellsberg paradoxes, and the reflection effect. Next, it uses a choice prediction competition methodology to clarify the interaction between the different anomalies. It focuses on decisions under risk (known payoff distributions) and under ambiguity (unknown probabilities), with and without feedback concerning the outcomes of past choices. The results demonstrate that it is not necessary to assume situation-specific processes. The distinct anomalies can be captured by assuming high sensitivity to the expected return and 4 additional tendencies: pessimism, bias toward equal weighting, sensitivity to payoff sign, and an effort to minimize the probability of immediate regret. Importantly, feedback increases sensitivity to probability of regret. Simple abstractions of these assumptions, variants of the model Best Estimate and Sampling Tools (BEAST), allow surprisingly accurate ex ante predictions of behavior. Unlike the popular models, BEAST does not assume subjective weighting functions or cognitive shortcuts. Rather, it assumes the use of sampling tools and reliance on small samples, in addition to the estimation of the expected values.

Item Type: Journal Article
Subjects: H Social Sciences > HB Economic Theory
Divisions: Faculty of Social Sciences > Warwick Business School > Behavioural Science
Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH): Risk -- Mathematical models, Uncertainty -- Mathematical models, Decision making -- Mathematical models
Journal or Publication Title: Psychological Review
Publisher: American Psychological Association
ISSN: 0033-295X
Official Date: July 2017
Dates:
DateEvent
July 2017Published
1 July 2017Available
9 March 2017Updated
1 April 2017Accepted
Volume: 124
Number: 4
Page Range: pp. 369-409
DOI: 10.1037/rev0000062
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: Israel Science Foundation (ISF)
Grant number: Grant no. 1821/12, Grant no. 1739/14 (ISF)

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us