AGiR: Mitigating Gift Over-Reliance in Mixed-Motive Games

Computer Science > Multiagent Systems

[Submitted on 2 Nov 2026 (v1), last revised 9 Nov 2026 (this version, v2)]

Title:AGiR: Mitigating Gift Over-Reliance in Mixed-Motive Games

View PDF HTML (experimental)Abstract:In this paper, we consider mixed-motive games (MMGs) which model various real-world multi-agent scenarios where self-interested agents must both cooperate and compete. We identify an issue with existing gifting methods, where agents become overly dependent on incoming gifts and fail to learn reward-yielding behaviors, a phenomenon we call gift over-reliance. To address this, we propose a refusal-augmented gifting framework that balances cooperation and competition in MMGs. Our approach introduces an adaptive refusal mechanism that allows agents to selectively reject a portion of received gifts from their policy-update signals, thereby mitigating gift over-reliance and preventing the agents from being exploited by other agents. This mechanism is gifting-method-agnostic, serving as a plug-in for existing schemes while enabling decentralized learning. We demonstrate that across diverse MMG environments and gifting schemes, our mechanism consistently reduces gift over-reliance and prevents lock-in to sacrificing policies, improving $\alpha$-fairness as a result, without necessitating major changes to existing training pipelines.

Submission history

From: Woohyeon Byeon [view email][v1] Mon, 2 Nov 2026 10:13:21 UTC (4,335 KB)

[v2] Mon, 9 Nov 2026 20:19:11 UTC (4,335 KB)

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