Decision makers are often described as seeking higher expected payoffs and avoiding higher variance in payoffs. We provide some necessary and some sufficient conditions for learning rules, that assume the agent has little prior and feedback information about the environment, to reflect such...

We study how learning shapes behavior towards risk when individuals are not assumed to know, or to have beliefs about, probability distributions. In any period, the behavior change induced by learning is assumed to depend on the action chosen and the payoff obtained. We characterize learning...

The key element of models of contest is the Contest Success Function (CSF) which specifies the winning probabilities of agents. The existing axiomatizations of CSFs assume that contestants can make only one type of investment. This paper generalizes these axiomatizations to the case where each...

The key element of models of contest is the Contest Success Function (CSF) which specifies the winning probabilities of agents. We provide an axiomatization of two parametric families of CSF's. In the first, the winning probability of each agent depends on the investments and a vector of...

The key element of models of contest is the Contest Success Function (CSF) which specifies the winning probabilities of agents. We provide an axiomatization of two parametric families of CSF’s. In the first, the winning probability of each agent depends on the investments and a vector of...

This paper studies a model of memory. The model takes into account that memory capacity is limited and imperfect. We study how agents with such memory limitations, who have very little information about their choice environment, play games. We introduce the notion of a Limited Memory Equilibrium...

In the payoff assessment model of choice ( Sarin and Vahid, 1999), only the assessment of the chosen strategy is updated. We extend that model to allow the agent to also update the assessments of strategies that the agent thinks are similar to the chosen strategy. We use this model to explain...

This paper considers learning rules for environments in which little prior and feedback information is available to the decision maker. Two properties of such learning rules are studied: absolute expediency and monotonicity. Both require that some aspect of the decision maker's performance...