🤖 AI Summary
This study addresses the challenge of achieving incentive compatibility in environments with dispersed information, where traditional direct mechanisms are precluded by the Hurwicz impossibility theorem. The authors propose a class of non-revelation-equivalent mechanisms that circumvent this limitation by constructing a framework of parallel, unlinkable strategic interactions, thereby enabling indirect inference of agents’ preferences. This approach demonstrates that incentive compatibility can be attained without requiring centralized information or direct preference disclosure, thus overcoming a fundamental constraint in classical mechanism design. By establishing the feasibility of such mechanisms, the work expands the theoretical boundaries of mechanism design and offers a novel pathway for designing incentives in distributed information settings.
📝 Abstract
Achieving incentive compatibility under informational decentralization is impossible within the class of direct and revelation-equivalent mechanisms typically studied in economics and computer science. We show that these impossibility results are conditional by identifying a narrow class of non-revelation-equivalent mechanisms that sustain enforcement by inferring preferences indirectly through parallel, uncorrelatable games.