We propose a novel method to find Nash equilibria in games with binary decision variables by including compensation payments and incentive-compatibility constraints from non-cooperative game theory directly into an optimization framework in lieu of using first order conditions of a...

We propose a novel method to find Nash equilibria in games with binary decision variables by including compensation payments and incentive-compatibility constraints from non-cooperative game theory directly into an optimization framework in lieu of using first order conditions of a...

The literature on supermodular optimization and games is surveyed from the perspective of potential users in economics. This methodology provides a new approach for comparative statics based only on critical assumptions, and allows a general analysis of games with strategic complementarities....

We analyze the use of information in a repeated oligopolistic insurance market. To sustain collusion, insurance companies might refrain from changing their pricing schedules even if new information about risks becomes available. We therefore provide an explanation for the existence of "unused...

This paper examines a dynamic game of exploitation of a common pool of some renewable asset by agents that sell the result of their exploitation on an oligopolistic market. A Markov Perfect Nash Equilibrium of the game is used to analyze the effects of a merger of a subset of the agents. We...

This study provides a comprehensive picture of experimental Kreps-Scheinkman markets with capacity choice in the first stage and subsequent price competition at the second. We conduct seven different treatments of such markets, varying the number of firms, the demand rationing scheme, the...

This study is the first to investigate the effect of demand rationing in experimental Bertrand-Edgeworth markets with fixed exogenous capacities. It is found that prices and profits are significantly higher under proportional than under efficient demand rationing. Moreover, the amount of...