We derive continuous approximations of stochastic evolutionary dynamics in games. Depending on how we construct the continuous limit, we obtain a continuous approxi-mation that is either an ordinary differential equation (ODE) or a stochastic differential equation (SDE). Our SDE approximation...

This article considers a simple model of reinforcement learning. All behavior change derives from the reinforcing or deterring effect of instantaneous payoff experiences. Payoff experiences are reinforcing or deterring depending on whether the paxoff exceeds an aspiration level or falls short 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...

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...

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...