Schaffer (1988) proposed a concept of evolutionary stability for finite-population models that has interesting implications in economic models of evolutionary learning, since it is related to perfectly competitive equilibrium. The present paper explores the relation of this concept to Nash...

Since the seminal work by Rothschild and Stiglitz on competitive insurance markets under adverse selection the problem of non-existence of equilibrium has puzzled many economists. In this paper we approach this problem from an evo- lutionary point of view. In a dynamic model insurance companies...

We interpret the Open Method of Coordination (OMC), recently adopted by the EU as a mode of governance in the area of social policy and other ¯elds, as an imitative learning dynamics of the type considered in evolutionary game theory. The best-practise feature and the iterative design of the...

In a (generalized symmetric aggregative game, payoffs depend only on individual strategy and an aggregate of all strategies. Players behaving as if they were negligible would optimize taking the aggregate as given. We provide evolutionary and dynamic foundations for such behavior when the game...

This paper models asset markets as a game where assets pay according to an arbitrary payoff matrix,investors decide on fractions of wealth to allocate to each asset,and prices result from market clearing. The only pure-strategy Nash equilibrium is to split wealth proportionally to the...

We study the properties of learning rules based on imitation in the context of n-player games played among agents within the same population. We find that there are no (nontrivial) rules that increase (average) expected payoffs at each possible state, and for any possible game. The results...

Fiscal federalism is often hailed as an innovation procedure: successful policy experiments in one jurisdiction will, via imitation, spread through the entire system, leading to overall better policy performance. We show that such hopes set in laboratory federalism may be ill-founded. For a...