Abstract
Mainstream explanations to gambling specify conditions under which human agents are locally risk loving. Such theories, however, fail to explain the typically observed prize distribution of a few large prizes and a large number of medium ones—hence the medium prizes paradox. In the current study we show that adaptive learning models recently proposed in the literature offer a solution. Simulations of such models predict that multiple medium prizes will slow down the decrease (over time) in agents' inclination to gamble. We run a laboratory experiment that supports this explanation and shows that the positive effect of medium prizes on the inclination to gamble increases with time.
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Haruvy, E., Erev, I. & Sonsino, D. The Medium Prizes Paradox: Evidence From a Simulated Casino. Journal of Risk and Uncertainty 22, 251–261 (2001). https://doi.org/10.1023/A:1011183001837
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DOI: https://doi.org/10.1023/A:1011183001837