We study a setting where a set of agents engage in pairwise exchanges of freely replicable goods (e.g., digital goods such as data), where two agents grant each other a copy of a good they possess in exchange for a good they lack. Such exchanges introduce a fundamental tension: while agents benefit from acquiring additional goods, they incur negative externalities when others do the same. This dynamic typically arises in real-world scenarios where competing entities may benefit from selective collaboration. For example, in a data sharing consortium, pharmaceutical companies might share (copies of) drug discovery data, when the value of accessing a competitor's data outweighs the risk of revealing their own. In our model, an altruistic central planner wishes to design an exchange protocol (without money), to structure such exchanges between agents. The protocol operates over multiple rounds, proposing sets of pairwise exchanges in each round, which agents may accept or reject. We formulate three key desiderata for such a protocol: (i) individual rationality: agents should not be worse off by participating in the protocol; (ii) incentive-compatibility: agents should be incentivized to share as much as possible by accepting all exchange proposals by the planner; (iii) stability: there should be no further mutually beneficial exchanges upon termination. We design an exchange protocol for the planner that satisfies all three desiderata. While the above desiderata are inspired by classical models for exchange, free-replicability and negative externalities necessitate novel and nontrivial reformalizations of these goals. We also argue that achieving Pareto-efficient agent utilities -- often a central goal in exchange models without externalities -- may be ill-suited in this setting.

翻译：本文研究一种场景：一组智能体参与自由可复制商品（例如数据等数字商品）的成对交换，其中两个智能体相互授予各自拥有而对方缺乏的商品副本。此类交换引发了一个根本性矛盾：智能体在获取额外商品时获得收益，但当其他智能体进行同样操作时却会承受负外部性。这种动态通常出现在现实世界竞争实体可能从选择性协作中获益的场景中。例如，在数据共享联盟中，当获取竞争对手数据的价值超过披露自身数据的风险时，制药公司可能会共享药物研发数据的副本。在我们的模型中，一个利他主义的中央规划者希望设计一种（无需货币的）交换协议来构建智能体间的此类交换。该协议在多轮次中运行，每轮提议一组成对交换方案，智能体可选择接受或拒绝。我们为此类协议制定了三个关键目标：（i）个体理性：智能体参与协议不应使其处境恶化；（ii）激励相容性：智能体应有动机通过接受规划者的所有交换提议来最大化共享；（iii）稳定性：协议终止后不应存在其他互惠交换方案。我们为规划者设计了一种满足所有三个目标的交换协议。虽然上述目标受经典交换模型启发，但自由可复制性与负外部性要求对这些目标进行新颖且非平凡的重构。我们还论证了实现帕累托最优的智能体效用——在无外部性的交换模型中常作为核心目标——可能并不适用于本场景。