A general technique for computing evolutionarily stable strategies based on errors in decision-making

Realistic models of contests between animals will often involve a series of state-dependent decisions by the contestants. Computation of evolutionarily stable strategies for such state-dependent dynamic games are usually based on damped iterations of the best response map. Typically this map is discontinuous so that iterations may not converge and even if they do converge it may not be clear if the limiting strategy is a Nash equilibrium. We present a general computational technique based on errors in decision making that removes these computational difficulties. We show that the computational technique works for a simple example (the Hawk-Dove game) where an analytic solution is known, and prove general results about the technique for more complex games. It is also argued that there is biological justification for inclusion of the types of errors we have introduced.