A Soft Inducement Framework for Incentive-Aided Steering of No-Regret Players

2025-08-29

AI Summary

This paper investigates how to guide no-regret learning players to converge to an arbitrary target action profile in two-player normal-form games with a mediator. We identify fundamental limitations of relying solely on information design or sublinear payment incentives. To overcome these, we propose a novel Stackelberg-style mechanism: pre-committing to an information structure followed by per-round constant-payment incentives. We theoretically establish that this mechanism breaks the prior limitations, deriving a tight constant lower bound on the required payment to achieve arbitrary target guidance; moreover, we prove it improves the convergence rate by a constant factor with high probability. Experiments validate the framework’s effectiveness and robustness. Our core contribution is the first formal demonstration of the necessity of synergistic information and incentive design, establishing a provably convergent, payment-efficient guidance paradigm for mediated game-theoretic learning.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In this work, we investigate a steering problem in a mediator-augmented two-player normal-form game, where the mediator aims to guide players toward a specific action profile through information and incentive design. We first characterize the games for which successful steering is possible. Moreover, we establish that steering players to any desired action profile is not always achievable with information design alone, nor when accompanied with sublinear payment schemes. Consequently, we derive a lower bound on the constant payments required per round to achieve this goal. To address these limitations incurred with information design, we introduce an augmented approach that involves a one-shot information design phase before the start of the repeated game, transforming the prior interaction into a Stackelberg game. Finally, we theoretically demonstrate that this approach improves the convergence rate of players' action profiles to the target point by a constant factor with high probability, and support it with empirical results.

Problem

• Steering players to desired action profiles using incentives
• Overcoming limitations of information design alone
• Improving convergence rates with augmented incentive framework

Innovation

• Mediator-augmented two-player game steering
• One-shot information design phase transformation
• Constant payment bound with convergence improvement