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.

翻译：我们研究无货币转移的战略性主体间的数据交换，其动机源于研究联盟和医疗合作等领域，在这些领域中支付不可行或受到限制。核心挑战在于获取数据共享收益的同时防止搭便车行为，否则将导致主体在数据收集上投资不足。我们引入一种简单的公平交换契约：对于每一对主体，每个主体接收的数据点数量恰好等于其提供的数据点数量，即两者收集水平的最小值。我们证明，在策略空间的变换下，该契约诱导的博弈具有超模性。这产生了一个清晰的结构：纯纳什均衡存在，它们构成一个格，并且可以在与主体数量平方成正比的时间内计算得到。此外，在自然的执行假设下，最大均衡可实现真实实施，并且是所有策略配置中全局帕累托最优的。在图限制的模型变体中，超模性不再成立，但对该构造的调整仍能产生可高效计算的纯纳什均衡和帕累托最优结果。总体而言，在缺乏支付的情况下，公平交换为数据交换提供了一种易于处理且激励相容的机制。