In contest models with symmetric valuations, equilibrium payoffs are positiveshares of the value of the prize. In contrast to a bargaining situation, theseshares sum to less than one because a share of the value is lost due to rent-dissipation. We ask: can every such division into payoff shares...

In this paper we unify, simplify, and extend previous work on the evolutionary dynamics of symmetric N-player matrix games with two pure strategies. In such games, gains from switching strategies depend, in general, on how many other individuals in the group play a given strategy. As a...

We prove existence of steady-state equilibrium in a class of matching modelswith search frictions.

We use the theory of abstract convexity to study adverse-selection principal-agent problems and two-sided matching problems, departing from much of the literature by not requiring quasilinear utility. We formulate and characterize a basic underlying implementation duality. We show how this...

Extreme adverse selection arises when private information has unboundedsupport, and market breakdown occurs when no trade is the only equilibriumoutcome. We study extreme adverse selection via the limit behavior of afinancial market as the support of private information converges to an...

This paper presents simple su±cient conditions under which optimal bunches inadverse-selection principal-agent problems can be characterized without using optimal controltheory.

We study markets in which agents first make investments and are then matched into potentially productive partnerships. Equilibrium investments and the equilibrium matching will be efficient if agents can simultaneously negotiate investments and matches, but we focus on markets in which agents...

We embed a two-sided matching market with non-transferable utility, a marriage market, into a random search model. We study steadystate equilibria and characterize the limit of the corresponding equilibrium matchings as exogenous search frictions become small. The central question is whether the...