ArticleProceedings of the National Academy of Sciences of the United States of America2025
Unilateral incentive alignment in two-agent stochastic games.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- On the optimal integration of intelligent agents into network systems to steer cooperation.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Payoff equivalence and complete strategy spaces of direct reciprocity.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- From simultaneous to leader-follower play in direct reciprocity.PNAS nexus · 2026Article
- Unilateral incentive alignment in two-agent stochastic games.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Collective artificial intelligence and evolutionary dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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8 authors.
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Abstract
Multiagent learning is challenging when agents face mixed-motivation interactions, where conflicts of interest arise as agents independently try to optimize their respective outcomes. Recent advancements in evolutionary game theory have identified a class of "zero-determinant" strategies, which confer an agent with significant unilateral control over outcomes in repeated games. Building on these insights, we present a comprehensive generalization of zero-determinant strategies to stochastic games, encompassing dynamic environments. We propose an algorithm that allows an agent to discover strategies enforcing predetermined linear (or approximately linear) payoff relationships. Of particular interest is the relationship in which both payoffs are equal, which serves as a proxy for fairness in symmetric games. We demonstrate that an agent can discover strategies enforcing such relationships through experience alone, without coordinating with an opponent. In finding and using such a strategy, an agent ("enforcer") can incentivize optimal and equitable outcomes, circumventing potential exploitation. In particular, from the opponent's viewpoint, the enforcer transforms a mixed-motivation problem into a cooperative problem, paving the way for more collaboration and fairness in multiagent systems.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.