Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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. Copyright 2001 by Kluwer Academic Publishers