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Risk Aversion as a Perceptual Bias


  • Mel Win Khaw
  • Ziang Li
  • Michael Woodford


The theory of expected utility maximization (EUM) explains risk aversion as due to diminishing marginal utility of wealth. However, observed choices between risky lotteries are difficult to reconcile with EUM: for example, in the laboratory, subjects' responses on individual trials involve a random element, and cannot be predicted purely from the terms offered; and subjects often appear to be too risk averse with regard to small gambles (while still accepting sufficiently favorable large gambles) to be consistent with any utility-of-wealth function. We propose a unified explanation for both anomalies, similar to the explanation given for related phenomena in the case of perceptual judgments: they result from judgments based on imprecise (and noisy) mental representation of the decision situation. In this model, risk aversion is predicted without any need for a nonlinear utility-of-wealth function, and instead results from a sort of perceptual bias — but one that represents an optimal Bayesian decision, given the limitations of the mental representation of the situation. We propose a specific quantitative model of the mental representation of a simple lottery choice problem, based on other evidence regarding numerical cognition, and test its ability to explain the choice frequencies that we observe in a laboratory experiment.

Suggested Citation

  • Mel Win Khaw & Ziang Li & Michael Woodford, 2017. "Risk Aversion as a Perceptual Bias," NBER Working Papers 23294, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:23294
    Note: EFG ME

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    References listed on IDEAS

    1. Frederick Mosteller & Philip Nogee, 1951. "An Experimental Measurement of Utility," Journal of Political Economy, University of Chicago Press, vol. 59, pages 371-371.
    2. Jakub Steiner & Colin Stewart, 2016. "Perceiving Prospects Properly," American Economic Review, American Economic Association, vol. 106(7), pages 1601-1631, July.
    3. James Cox & Vjollca Sadiraj & Bodo Vogt & Utteeyo Dasgupta, 2013. "Is there a plausible theory for decision under risk? A dual calibration critique," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 54(2), pages 305-333, October.
    4. Friedman, Daniel & Isaac, R. Mark & James, Duncan & Sunder, Shyam, 2014. "Risky Curves: On the Empirical Failure of Expected Utility," Santa Cruz Department of Economics, Working Paper Series qt87v8k86z, Department of Economics, UC Santa Cruz.
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    Cited by:

    1. Xavier Gabaix & David Laibson, 2017. "Myopia and Discounting," NBER Working Papers 23254, National Bureau of Economic Research, Inc.
    2. Xavier Gabaix, 2017. "Behavioral Inattention," NBER Working Papers 24096, National Bureau of Economic Research, Inc.

    More about this item

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D3 - Microeconomics - - Distribution
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D87 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Neuroeconomics

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