arXiv:2606.01159v1 Announce Type: new Abstract: We study two-player zero-sum games (TPZSGs) with bandit feedback under fairness constraints requiring every action to be played with probability at least $\alpha/m$. Existing instance-dependent results target $\textit{pure}$ Nash equilibria, while fairness generically produces $\textit{mixed}$ equilibria, a harder learning target. Our key technical tool is a reparametrization: every fair strategy decomposes as $p = (\alpha/m)\mathbf{1} + (1-\alpha)\widetilde{p}$ with $\widetilde{p} \in \Delta_m$, and substituting into the payoff form yields $p^{\
The increasing prevalence of AI applications across various domains necessitates robust theoretical foundations for multi-agent interactions, especially in competitive settings requiring fairness guarantees.
This research provides fundamental insights into fairness constraints in competitive AI systems, impacting the ethical implementation and stability of automated decision-making in confrontational scenarios.
The explicit incorporation of fairness constraints into the learning mechanisms of zero-sum games shifts the focus from purely optimal strategies to strategies that also ensure equitable participation for all actions.
- · AI ethicists
- · Developers of competitive AI agents
- · Researchers in game theory and multi-agent systems
- · Systems prioritizing raw win-loss rates over fairness
Improved theoretical understanding of fair play in adversarial AI and multi-agent systems.
Development of new algorithms for AI agents that explicitly incorporate fairness criteria in competitive environments.
Enhanced trust and broader adoption of AI agents in sensitive applications where fairness is a critical requirement, even at the cost of maximal performance.
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Read at arXiv cs.LG