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Agree to Disagree: Subjective Fairness in Privacy-Restricted Decentralised Conflict Resolution.

Alex Raymond1, Matthew Malencia1,2, Guilherme Paulino-Passos3

  • 1Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.

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This study introduces a novel approach for decentralized conflict resolution in systems with limited information, addressing privacy constraints and subjective unfairness. It demonstrates strategies to mitigate privacy

Keywords:
argumentationdialoguesexplanationsfairnessmulti-agent systems and autonomous agentsprivacy

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Area of Science:

  • Artificial Intelligence
  • Multi-Agent Systems
  • Computer Science

Background:

  • Decentralized systems often involve agents with partial observability, necessitating communication for information sharing.
  • Existing rule-aware conflict resolution methods assume full state knowledge, which is impractical for many distributed applications.
  • Privacy constraints limit information disclosure, potentially leading to subjective unfairness where agents' perceptions of fairness differ from actual outcomes.

Purpose of the Study:

  • To develop a novel interaction approach for privacy-aware, explainable conflict resolution in decentralized systems.
  • To formalize the relationship between privacy and fairness under partial observability.
  • To investigate strategies for mitigating the impact of privacy constraints on fairness in multi-agent environments.

Main Methods:

  • Proposed a privacy-aware explainable conflict resolution architecture enabling agent dialogue through hypotheses and facts.
  • Defined and formalized subjective and objective fairness at local and global scopes, analyzing the impact of partial observability.
  • Studied the proposed architecture and privacy-fairness relationship abstractly using randomized cultures and argumentation strategies.
  • Conducted a case study simulating prioritized rule-aware collision avoidance with limited information disclosure.

Main Results:

  • Empirically demonstrated a trade-off between privacy, objective fairness, and subjective fairness.
  • Showcased that improved argumentation strategies can effectively mitigate the negative effects of privacy constraints on fairness.
  • Validated the findings through abstract analysis and a specific multi-agent simulation case study.

Conclusions:

  • The proposed architecture enables decentralized, fairness-aware conflict resolution under privacy constraints.
  • Formalizing the privacy-fairness relationship provides a framework for understanding and managing trade-offs in distributed systems.
  • Effective strategies can balance privacy needs with the achievement of both objective and subjective fairness in multi-agent systems.