Payment Schemes from Limited Information with Applications in Distributed Computing

TL;DR

This paper introduces a probabilistic payment mechanism to incentivize behavior in finite games with limited information, with applications in distributed computing like secure multiparty computation.

Contribution

It characterizes when payment schemes can implement arbitrary utilities under limited observation and analyzes the computational complexity of designing optimal schemes.

Findings

Optimal payment schemes are P-complete for perfect information games.

Payments must be linear in the security level, establishing a lower bound.

Applications to distributed computing problems match the theoretical lower bounds asymptotically.

Abstract

We propose a generic mechanism for incentivizing behavior in an arbitrary finite game using payments. Doing so is trivial if the mechanism is allowed to observe all actions taken in the game, as this allows it to simply punish those agents who deviate from the intended strategy. Instead, we consider an abstraction where the mechanism probabilistically infers information about what happened in the game. We show that payment schemes can be used to implement any set of utilities if and only if the mechanism can essentially infer completely what happened. We show that finding an optimal payment scheme for games of perfect information is \textsf{P}-complete, and conjecture it to be \textsf{PPAD}-hard for games of imperfect information. We prove a lower bound on the size of the payments, showing that the payments must be linear in the intended level of security. We demonstrate the applicability of our model to concrete problems in distributed computing, namely decentralized commerce and secure multiparty computation, for which the payments match the lower bound asymptotically.