Implementation with Uncertain Evidence

Soumen Banerjee and Yi-Chun Chen

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Abstract: We study a full implementation problem with a state unknown to the designer but known to agents, where agents have uncertain evidence privately drawn from state-dependent distributions. This setting allows ``perfect deceptions,'' where agents' reports can mimic the evidence distribution of a false state which cannot also be directly refuted by evidence, making differentiation impossible for any mechanism. This yields our main result: a necessary and sufficient condition, No Perfect Deceptions (NPD), for implementation in (mixed-strategy) Bayesian Nash equilibria. The solution uses ``competing'' scoring rules to elicit ex ante evidence distributions while canceling out in equilibrium, a reflection device that converts low-probability refutations into direct incentives for full evidence submission without penalties that scale with inverse refutation probabilities, and an endogenous ``test allocation'' using bets which separate states based on the evidence probabilities. The constructed mechanism is finite and uses off-equilibrium transfers with maximum magnitude of the order of the outcome. We also allow limited but nontrivial uncertainty in the state dimension. A generalized NPD condition characterizes exact implementation under nonexclusive information and yields exact outcomes with vanishing expected transfers under information smallness.

Date: 2022-09, Revised 2026-10
New Economics Papers: this item is included in nep-des, nep-gth and nep-mic
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Citations: View citations in EconPapers (2)

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