We study a two-stage sequential search model with two agents who compete for one job. The agents arrive sequentially, each one in a different stage. The agents' abilities are private information and they are derived from heterogeneous distribution functions. In each stage the designer chooses an...

We study two-stage all-pay auctions with two identical prizes. In each stage, players compete for one prize. Each player may win either one or two prizes. We analyze the equilibrium strategies where players' marginal values for the prizes are either declining or inclining.

We study a sequential two-stage all-pay auction with two identical prizes. In each stage, the players compete for one prize and each player may win either one or two prizes. The designer may impose a cap on the players' bids in each of the stages. We analyze the equilibrium in this sequential...

We study optimal contest design in situations where the designer can reward high performance agents with positive prizes and punish low performance agents with negative prizes. We link the optimal prize structure to the curvature of distribution of abilities in the population. In particular, we...

We study all-pay auctions with multiple prizes. The players have the same value for all the certain prizes except for one uncertain prize for which each player has a private value. We characterize the equilibrium strategy and show that if the number of prizes is smaller than the number of...

We study round-robin tournaments with three players whose values of winning are common knowledge. In every stage a pair-wise match is modelled as an all-pay auction. The player who wins in two matches wins the tournament. We characterize the sub-game perfect equilibrium for symmetric (all...

We study two-player common-value all-pay auctions (contests) with asymmetric information under the assumption that one of the players has an information advantage over his opponent. We characterize the unique equilibrium in these contests, and examine the role of information in determining the...