🤖 AI Summary
This paper studies the design of information verification mechanisms: how to probabilistically test agents’ reports to balance allocation efficiency and principal surplus. We propose a novel paradigm embedding statistical hypothesis testing into mechanism design, constructing a commitment mechanism with randomized verification—where each report type undergoes a binary test (pass/fail), and outcomes directly inform allocation and payment decisions. Innovatively, we reformulate the virtual value function and, under quasilinear preferences, derive the first closed-form solution for the optimal verification mechanism. Theoretically, we prove that higher verification accuracy strictly improves both allocation efficiency and the principal’s share of surplus, and that these two objectives are positively correlated. Our results establish a theoretically rigorous yet practically implementable foundation for credible information elicitation.
📝 Abstract
We introduce a model of probabilistic verification into the standard mechanism design setting. The principal uses a statistical test to verify the truthfulness of type reports. Testing generates a binary outcome---pass or fail---that depends stochastically on the agent's true type and report. The principal commits to a mechanism that assigns a test to each message and then a decision based on the test outcome. Under quasilinear preferences, we solve for the optimal mechanism by introducing a new expression for the virtual value. When verification is more accurate, the optimal allocation is more efficient and a greater share of the surplus goes to the principal.