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Equilibrium with Internal Transfers

arXiv 2606.20960 ↗First posted 18 Jun 2026cs.GT cs.LG econ.THcited by 0

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Abstract (arXiv)

Nash equilibrium (NE) arises from selfish utility maximization, yet its social welfare can be arbitrarily far from optimal. Moreover, computing an NE is intractable in general. We study augmented game models in which players use budget-balanced internal transfers to improve incentives before play. We first introduce Self-Enforcing Transfer Equilibrium (SETE), where players commit to nonnegative peer-to-peer transfers that are paid only if the recipient does not deviate from a prescribed strategy. For polymatrix games, we show that every stationary point of the social welfare function, in particular any socially optimal strategy profile, can be sustained as a SETE. This induces a Nash equilibrium in the agent normal form of the corresponding augmented game. We further propose a polynomial-time algorithm and a decentralized learning dynamic to compute such product-form equilibria. We then introduce Mediated Self-Enforcing Transfer Equilibrium (M-SETE), where a mediator makes both the payment schedule and the prescribed strategies binding offers. This additional enforcement resolves the agent-normal-form limitation: an M-SETE is a Nash equilibrium of the augmented game itself, not merely of its agent normal form, and any socially optimal strategy profile can be supported as an M-SETE in any finite game while preserving budget balance. Thus, internal transfers improve welfare and computation while preserving independent play on the equilibrium path. When full sequential-game stability is required, binding mediation provides the corresponding implementation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 24 references that are in the map. First posted on arXiv 18 Jun 2026.

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References · 5

  1. 2019Introduction to Online Convex Optimizationcited by 803
  2. 2023Steering No-Regret Learners to a Desired Equilibriumcited by 6
  3. 2023Semi Bandit Dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guaranteescited by 5
  4. 2024Games with Payments between Learning Agentscited by 3
  5. 2026Computing Equilibrium beyond Unilateral Deviationcited by 1

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