提出可激励诚实报告不可验证信号的机制,适用于去中心化基础设施网络
Incentive-Compatible Recovery from Manipulated Signals, with Applications to Decentralized Physical Infrastructure
- 基于观察者对源信号的隐含感知,设计激励相容机制
- 只要源点在观测者凸包内,就能实现严格诚实报告的唯一均衡
- 适用于位置证明和带宽证明等去中心化物理网络场景
我们提出了首个形式化模型,用于从具有隐含信号的参与方(源)处获取不可验证信息,这些信号由其他参与者(观察者)推断。该模型源于去中心化物理基础设施网络(DePIN)的应用需求,即由不信任且自利的个体提供传感器数据、带宽或能源等物理服务。这类系统的核心挑战在于验证参与者实际提供的服务质量。我们首先定义了“源可识别性”条件,并证明其是存在严格诚实报告为严格均衡机制的必要条件。进一步地,通过借鉴同伴预测技术,我们证明:所有满足源可识别性的信号网络中,均存在一个严格诚实机制,其中诚实报告带来的期望总收益高于任何信息量更少的均衡。此外,若任一观察者以正概率完全诚实(例如由网络所有者运行),则该诚实均衡是唯一的。我们还扩展条件至联盟情形,发现所考虑设置下通常不存在抗共谋机制。最后,我们将框架应用于两个DePIN场景:位置证明与带宽证明。在位置证明中,观察者获取到源的欧几里得距离(可能被放大)。此时,我们的条件具有直观几何意义:只有当源的位置被保证位于观察者的凸包内部时,才能真实地被诱导出诚实报告。
原文摘要 · Abstract (English)
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.
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