SMU SOE Seminar (Oct 5, 2017): Mechanism Design with Bounded Depth of Reasoning and Small Modeling Mistakes

We consider mechanism design in contexts in which agents exhibit bounded depth of reasoning (e.g., level-k) instead of rational expectations, and in which the planner may make small modeling mistakes.  While level-0 agents are assumed to be truth tellers, level-k agents best-respond to their belief that other agents have at most k-1 levels of reasoning.  We find that continuous implementation can be performed using simple direct mechanisms, in which agents report only first-order beliefs.  Incentive compatibility is necessary for continuous implementation in this framework, while its strict version alone is sufficient.

Examples illustrate the permissiveness of our findings in contrast to earlier related results, which relied on the assumption of rational expectations.