GTMar 17

Steering No-Regret Learners to a Desired Equilibrium

arXiv:2306.0522117.014 citationsh-index: 81
Predicted impact top 11% in GT · last 90 daysOriginality Highly original
AI Analysis

This addresses equilibrium selection and information design in multi-agent systems, providing theoretical insights with practical implications for game theory and AI, though it is incremental in extending prior work on steering and budgets.

The paper tackles the problem of steering no-regret learners in extensive-form games toward a desired equilibrium using payments, showing that steering is possible with sublinear total budgets or constant per-round budgets in observable settings, but impossible with constant budgets in general extensive-form games when only trajectories are observed.

A mediator observes no-regret learners playing an extensive-form game repeatedly across $T$ rounds. The mediator attempts to steer players toward some desirable predetermined equilibrium by giving (nonnegative) payments to players. We call this the steering problem. The steering problem captures problems several problems of interest, among them equilibrium selection and information design (persuasion). If the mediator's budget is unbounded, steering is trivial because the mediator can simply pay the players to play desirable actions. We study two bounds on the mediator's payments: a total budget and a per-round budget. If the mediator's total budget does not grow with $T$, we show that steering is impossible. However, we show that it is enough for the total budget to grow sublinearly with $T$, that is, for the average payment to vanish. When players' full strategies are observed at each round, we show that constant per-round budgets permit steering. In the more challenging setting where only trajectories through the game tree are observable, we show that steering is impossible with constant per-round budgets in general extensive-form games, but possible in normal-form games or if the per-round budget may itself depend on $T$. We also show how our results can be generalized to the case when the equilibrium is being computed online while steering is happening. We supplement our theoretical positive results with experiments highlighting the efficacy of steering in large games.

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