This paper presents simple su±cient conditions under which optimal bunches in adverse-selection principal-agent problems can be characterized without using optimal control theory.

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 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 unbounded support, and market breakdown occurs when no trade is the only equilibrium outcome. We study extreme adverse selection via the limit behavior of a financial market as the support of private information converges to an...

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...

Relatedness and synergy affect the selection pressure on cooperation and altruism. Although early work investigated the effect of these factors independently of each other, recent efforts have been aimed at exploring their interplay. Here, we contribute to this ongoing synthesis in two distinct...

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

In contest models with symmetric valuations, equilibrium payoffs are positive shares of the value of the prize. In contrast to a bargaining situation, these shares 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...