Paper ID: 2304.06842

A Dynamic Adverse Selection Multiagent Model with Off-Menu Actions

Tao Zhang, Quanyan Zhu

In dynamic mechanism design literature, one critical aspect has been typically ignored-the agents' periodic participation, which they can adapt and plan strategically. We propose a framework for dynamic principal-multiagent problems, augmenting the classic model by incorporating agents' periodic coupled decisions on participation and regular action selections. The principal faces adverse selection and designs a mechanism comprising a task policy profile (defining evolving agent action menus), a coupling policy profile (affecting agent utilities), and an off-switch function profile (assigning rewards or penalties upon agent withdrawal). Firstly, we introduce payoff-flow conservation-a sufficient condition to ensure dynamic incentive compatibility for regular actions. Secondly, we formulate a unique process, persistence transformation, which integrates task policy's implicit functions, enabling a closed-form off-switch function derivation, hence securing sufficient conditions for agents' coupled decisions' incentive compatibility, aligning with the principal's preferences. Thirdly, we go beyond the traditional envelope theorem by presenting a necessary condition for incentive compatibility, leveraging the coupled optimality of principal-desired actions. This approach helps explicitly formulate both the coupling and off-switch functions. Finally, we establish envelope-like conditions exclusively on the task policies, facilitating the application of the first-order approach.

Submitted: Apr 13, 2023