Incentive-Compatible Recovery from Manipulated Signals, with Applications to Decentralized Physical Infrastructure
Jason Milionis, Jens Ernstberger, Joseph Bonneau, Scott Duke Kominers, Tim Roughgarden
TL;DR
This work addresses recovering unverifiable information from a manipulative source within multi-observer signal networks, a setting motivated by DePIN challenges in verifying service levels. It introduces the source identifiability condition as a sharp criterion for when truthful reporting can be incentivized, showing impossibility without identifiability and constructing a prior-free BTS-based mechanism that yields a strictly truthful Bayesian Nash equilibrium when identifiability holds, with uniqueness under positive probability of unconditional honesty. The results are applied to location and bandwidth signal networks, yielding a convex-hull geometric condition for location recovery and a quasi-strict equilibrium for bandwidth under bounded observations. Practically, the findings guide incentive design in DePIN systems, highlighting the need to surround potential source locations with observers and to guard against self-dealing through non-mechanism-based governance or trust assumptions.
Abstract
We introduce the first formal model capturing the elicitation of unverifiable information from a party (the "source") with implicit signals derived by other players (the "observers"). Our model is motivated in part by applications in decentralized physical infrastructure networks (a.k.a. "DePIN"), an emerging application domain in which physical services (e.g., sensor information, bandwidth, or energy) are provided at least in part by untrusted and self-interested parties. A key challenge in these signal network applications is verifying the level of service that was actually provided by network participants. We first establish a condition called source identifiability, which we show is necessary for the existence of a mechanism for which truthful signal reporting is a strict equilibrium. For a converse, we build on techniques from peer prediction to show that in every signal network that satisfies the source identifiability condition, there is in fact a strictly truthful mechanism, where truthful signal reporting gives strictly higher total expected payoff than any less informative equilibrium. We furthermore show that this truthful equilibrium is in fact the unique equilibrium of the mechanism if there is positive probability that any one observer is unconditionally honest (e.g., if an observer were run by the network owner). Also, by extending our condition to coalitions, we show that there are generally no collusion-resistant mechanisms in the settings that we consider. We apply our framework and results to two DePIN applications: proving location, and proving bandwidth. In the location-proving setting observers learn (potentially enlarged) Euclidean distances to the source. Here, our condition has an appealing geometric interpretation, implying that the source's location can be truthfully elicited if and only if it is guaranteed to lie inside the convex hull of the observers.