Fair Data-Exchange Mechanisms

TL;DR

Fair Data-Exchange Mechanisms studies data exchange among strategic agents without monetary transfers and introduces a reciprocal, min-based contract $D_{ij}(x_i,x_j)=\min\{x_i,x_j\}$. By transforming to total-access data $T=\Phi(X)$, the authors prove the game is supermodular with positive spillovers, guaranteeing a lattice of pure Nash equilibria and a computable maximal equilibrium that is truthful under enforcement and globally Pareto-optimal; they also provide graph-restricted and discrete extensions. A constructive $O(n^2)$ algorithm computes the maximal equilibrium, which serves as the basis for a direct mechanism that elicits private data-benefit and cost information and outputs a truthful, Pareto-optimal outcome under enforcement. The work connects mechanism design for data acquisition without payments to data-exchange economies and federated learning, offering a tractable benchmark for incentive-aligned data sharing in restricted-transfer environments and outlining fruitful directions for extensions to multi-type data, overlaps, and verifiability.

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.

Figures (1)

• Figure :

Theorems & Definitions (24)

• definition 1: Supermodularity
• definition 2: Positive spillovers
• theorem 1: Supermodularity in total-access coordinates
• theorem 2
• definition 3: $K$-data level
• lemma 1: Upward move is harmful when lower agents are fixed
• lemma 2: Downward move is weakly harmful when low agents are fixed
• theorem 3: Correctness of Algorithm \ref{['alg:max']}
• theorem 4: Extremality of maximal equilibrium
• theorem 5: Truthfulness under enforcement