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Learning to deal with risk: what does reinforcement learning tell us about risk atittudes?

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  • Burgos, Albert

Abstract

People are generally reluctant to accept risk. In particular, people overvaluate outcomes that are considered certain, relative to outcomes which are merely probable. At the same time, people is also more willing to accept bets when payoffs involve losses rather than gains. I consider how far adaptive learning can go on in explaining these phenomena. I report simulations in which adaptive learners of the kind studied in Roth & Erev (1995, 1998) and B6rgers & Sarin (1996, 1997) deal with a problem of iterated choice under risk where alternatives differ by a mean preserving spread. The simulations show that adaptive learning induce (on average) risk averse choices. This learning bias is stronger for gains than for losses. Also, risk averse choices are much more likely when one of the alternatives is a certain prospect. The implications of a learning interpretation of risk taking are examined.

Suggested Citation

  • Burgos, Albert, 1999. "Learning to deal with risk: what does reinforcement learning tell us about risk atittudes?," UC3M Working papers. Economics 6152, Universidad Carlos III de Madrid. Departamento de Economía.
    • Handle: RePEc:cte:werepe:6152
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    1. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
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    3. Oyarzun, Carlos & Sarin, Rajiv, 2012. "Mean and variance responsive learning," Games and Economic Behavior, Elsevier, vol. 75(2), pages 855-866.
    4. Oyarzun, Carlos & Sarin, Rajiv, 2013. "Learning and risk aversion," Journal of Economic Theory, Elsevier, vol. 148(1), pages 196-225.

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    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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