Fair Data-Exchange Mechanisms

2026-02-11

AI Summary

This study addresses the challenge of incentivizing strategic agents to share data and avoid free-riding in collaborative settings—such as scientific consortia or healthcare partnerships—where monetary transfers are infeasible. The authors propose the first payment-free, fair data exchange contract: each pair of participants mutually exchanges an equal amount of data, defined as the minimum of their respective collected quantities. Leveraging supermodular game theory and lattice-theoretic analysis, the mechanism guarantees the existence of a pure-strategy Nash equilibrium under strategic interactions, with the set of equilibria forming a lattice. Moreover, the maximal equilibrium is globally Pareto optimal and can be computed efficiently in time quadratic in the number of agents. Remarkably, these desirable properties—computational tractability and optimality—persist even when graph-based constraints are imposed on data exchange.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• data exchange
• free-riding
• strategic agents
• fairness
• incentive alignment

Innovation

• fair data exchange
• supermodular game
• Nash equilibrium
• Pareto optimality
• incentive alignment