Pairwise Exchanges of Freely Replicable Goods with Negative Externalities

2026-03-12

AI Summary

This study addresses the fundamental tension between cooperation and competition in bilateral exchanges of freely replicable goods—such as data—where agents benefit from acquiring new resources yet impose negative externalities when others obtain similar goods. The authors reformulate the core objectives of individual rationality, incentive compatibility, and stability for this setting and demonstrate that traditional Pareto efficiency may fail to capture desirable outcomes. By constructing a multi-round, money-free bilateral exchange mechanism grounded in game theory and mechanism design, they propose a protocol guided by an altruistic central planner that guarantees participants never incur losses and are incentivized to share fully. This protocol is the first to simultaneously satisfy all three desiderata, offering a theoretically sound and practically viable framework for digital resource sharing under negative externalities.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Humans and AI: Learning Human Values and Preferences

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 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.

Problem

• freely replicable goods
• negative externalities
• pairwise exchanges
• incentive compatibility
• individual rationality

Innovation

• pairwise exchange
• freely replicable goods
• negative externalities
• incentive compatibility
• exchange protocol