Fair Data-Exchange Mechanisms

TL;DR

This paper introduces a fair-exchange mechanism for data sharing among strategic agents without monetary incentives, ensuring truthful, efficient, and Pareto-optimal outcomes through a supermodular game structure and computationally tractable equilibria.

Contribution

It proposes a novel fair-exchange contract that guarantees balanced data exchange, analyzes its game-theoretic properties, and demonstrates efficient computation of optimal equilibria in both unrestricted and graph-restricted settings.

Findings

Pure Nash equilibria exist and form a lattice.

Maximal equilibrium is truthful and Pareto-optimal.

Efficient algorithms compute equilibria in quadratic time.

Abstract

We study data exchange among strategic agents without monetary transfers, motivated by domains such as research consortia and healthcare collaborations where payments are infeasible or restricted. The central challenge is to reap the benefits of data-sharing while preventing free-riding that would otherwise lead agents to under invest in data collection. We introduce a simple fair-exchange contract in which, for every pair of agents, each agent receives exactly as many data points as it provides, equal to the minimum of their two collection levels. We show that the game induced by this contract is supermodular under a transformation of the strategy space. This results in a clean structure: pure Nash equilibria exist, they form a lattice, and can be computed in time quadratic in the number of agents. In addition, the maximal equilibrium is truthfully implementable under natural enforcement assumptions and is globally Pareto-optimal across all strategy profiles. In a graph-restricted variant of the model supermodularity fails, but an adaptation of the construction still yields efficiently computable pure Nash equilibria and Pareto-optimal outcomes. Overall, fair exchange provides a tractable and incentive-aligned mechanism for data exchange in the absence of payments.