Doron Sonsino

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The paper studies a large class of bounded-rationality, probabilistic learning models on strategic-form games. The main assumption is that players ‘‘recognize’’ cyclic patterns in the observed history of play. The main result is convergence with probability one to a fixed pattern of pure strategy Nash equilibria, in a large class of ‘‘simple games’’ in(More)
We analyze a repeated first-price auction in which the types of the players are determined before the first round. It is proved that if every player is using either a belief-based learning scheme with bounded recall or a generalized fictitious play learning scheme, then after sufficiently long time, the players' bids are in equilibrium in the one-shot(More)
We present experimental evidence suggesting that human subjects penalize lotteries for complexity. Our results contradict the assumption that human agents follow the discounted expected utility model in multi-period choice with uncertainty. In particular, we show that the buying price o€ered for an inferior, simple multi-period lottery may sometimes(More)
Assume that two risk neutral agents with asymmetric information simultaneously expect a gain from zero-sum betting. Geanakoplos and Sebenius (1983) (henceforth GS) consider the case where the agents may re-evaluate the profitability of betting successively before the payments are realized. They prove that one of the players must reject the proposed bet(More)
An “unprocessed risk” is collection of simple lotteries with a reduction-rule that describes the actual-payoff to the decision-maker as a function of realized lottery outcomes. Experiments reveal that the willingness to pay for unprocessed risks is consistently biased towards the payoff-level in the unprocessed representation. The “anchoring-to-frame” bias(More)