---
res:
  bibo_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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Alex
      foaf_name: Mcavoy, Alex
      foaf_surname: Mcavoy
  - foaf_Person:
      foaf_givenName: Udari Madhushani
      foaf_name: Sehwag, Udari Madhushani
      foaf_surname: Sehwag
  - foaf_Person:
      foaf_givenName: Christian
      foaf_name: Hilbe, Christian
      foaf_surname: Hilbe
      foaf_workInfoHomepage: http://www.librecat.org/personId=2FDF8F3C-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-5116-955X
  - foaf_Person:
      foaf_givenName: Krishnendu
      foaf_name: Chatterjee, Krishnendu
      foaf_surname: Chatterjee
      foaf_workInfoHomepage: http://www.librecat.org/personId=2E5DCA20-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-4561-241X
  - foaf_Person:
      foaf_givenName: Wolfram
      foaf_name: Barfuss, Wolfram
      foaf_surname: Barfuss
  - foaf_Person:
      foaf_givenName: Qi
      foaf_name: Su, Qi
      foaf_surname: Su
  - foaf_Person:
      foaf_givenName: Naomi Ehrich
      foaf_name: Leonard, Naomi Ehrich
      foaf_surname: Leonard
  - foaf_Person:
      foaf_givenName: Joshua B.
      foaf_name: Plotkin, Joshua B.
      foaf_surname: Plotkin
  bibo_doi: 10.1073/pnas.2319927121
  bibo_issue: '25'
  bibo_volume: 122
  dct_date: 2025^xs_gYear
  dct_identifier:
  - UT:001522351900001
  dct_isPartOf:
  - http://id.crossref.org/issn/0027-8424
  - http://id.crossref.org/issn/1091-6490
  dct_language: eng
  dct_publisher: National Academy of Sciences@
  dct_title: Unilateral incentive alignment in two-agent stochastic games@
  fabio_hasPubmedId: '40523172'
...