article published yes Alex Mcavoy author Udari Madhushani Sehwag author Christian Hilbe author 2FDF8F3C-F248-11E8-B48F-1D18A9856A870000-0001-5116-955X Krishnendu Chatterjee author 2E5DCA20-F248-11E8-B48F-1D18A9856A870000-0002-4561-241X Wolfram Barfuss author Qi Su author Naomi Ehrich Leonard author Joshua B. Plotkin author KrCh department Formal Methods for Stochastic Models: Algorithms and Applications project 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. https://research-explorer.ista.ac.at/download/19965/19972/2025_PNAS_McAvoy.pdf application/pdfno https://creativecommons.org/licenses/by-nc-nd/4.0/ National Academy of Sciences2025 eng 0027-8424 1091-6490 40523172 00152235190000110.1073/pnas.2319927121 12225 Mcavoy, Alex, Udari Madhushani Sehwag, Christian Hilbe, Krishnendu Chatterjee, Wolfram Barfuss, Qi Su, Naomi Ehrich Leonard, and Joshua B. Plotkin. “Unilateral Incentive Alignment in Two-Agent Stochastic Games.” <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences, 2025. <a href="https://doi.org/10.1073/pnas.2319927121">https://doi.org/10.1073/pnas.2319927121</a>. Mcavoy A, Sehwag UM, Hilbe C, et al. Unilateral incentive alignment in two-agent stochastic games. <i>Proceedings of the National Academy of Sciences</i>. 2025;122(25). doi:<a href="https://doi.org/10.1073/pnas.2319927121">10.1073/pnas.2319927121</a> Mcavoy, Alex, et al. “Unilateral Incentive Alignment in Two-Agent Stochastic Games.” <i>Proceedings of the National Academy of Sciences</i>, vol. 122, no. 25, e2319927121, National Academy of Sciences, 2025, doi:<a href="https://doi.org/10.1073/pnas.2319927121">10.1073/pnas.2319927121</a>. Mcavoy, A., Sehwag, U. M., Hilbe, C., Chatterjee, K., Barfuss, W., Su, Q., … Plotkin, J. B. (2025). Unilateral incentive alignment in two-agent stochastic games. <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences. <a href="https://doi.org/10.1073/pnas.2319927121">https://doi.org/10.1073/pnas.2319927121</a> A. Mcavoy <i>et al.</i>, “Unilateral incentive alignment in two-agent stochastic games,” <i>Proceedings of the National Academy of Sciences</i>, vol. 122, no. 25. National Academy of Sciences, 2025. Mcavoy A, Sehwag UM, Hilbe C, Chatterjee K, Barfuss W, Su Q, Leonard NE, Plotkin JB. 2025. Unilateral incentive alignment in two-agent stochastic games. Proceedings of the National Academy of Sciences. 122(25), e2319927121. A. Mcavoy, U.M. Sehwag, C. Hilbe, K. Chatterjee, W. Barfuss, Q. Su, N.E. Leonard, J.B. Plotkin, Proceedings of the National Academy of Sciences 122 (2025). 199652025-07-06T22:01:23Z2025-09-30T13:47:14Z