Venture Theory: A Model of Decision Weights.

Abstract

Several theories suggest that people replace probabilities by decision weights when evaluating risky outcomes. This paper proposes a model, called venture theory, of how people assess decision weights. It is assumed that people first anchor on a stated probability and then adjust this by mentally simulating other possible values. The amount of mental simulation is affected both by the extent to which the anchor deviates from the extremes of 0 and 1 (i.e., where there is no uncertainty) and the level of perceived ambiguity concerning the relevant probability. The net effect of the adjustment (i.e., up or down relative to the anchor) reflects the relative weight given in imagination to values above as opposed to below the anchor. This, in turn, is taken to be a function of both individual and situational variables, and in particular, the sign and size of payoffs. Cognitive and motivational factors therefore both play important roles in determining decision weights. Assuming that people evaluate outcomes by a prospect theory value function (Kahneman & Tversky, 1979) and are cautious in the face of risk, predictions are derived concerning attitudes toward risk and ambiguity as functions of different levels of payoffs and probabilities. The results of two experiments are reported.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1988
Accession Number
ADA194809

Entities

People

  • Hillel J. Einhorn
  • Robin M. Hogarth

Organizations

  • University of Chicago

Tags

Communities of Interest

  • Biomedical
  • C4I
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Ambiguity
  • Business Administration
  • Classification
  • Cognition
  • Commerce
  • Engineering
  • Human Factors Engineering
  • Mental Processes
  • Military Research
  • New York
  • Psychology
  • Security
  • Simulations
  • Social Sciences
  • Students
  • Thinking
  • Uncertainty

Fields of Study

  • Psychology

Readers

  • Artificial Intelligence
  • Logistics and Supply Chain Management.
  • Regression Analysis.