Overcoming the Price of Anarchy by Steering with Recommendations

2025-02-26

AI Summary

In multi-agent resource competition systems, selfish agent behavior leads to the Price of Anarchy (PoA), quantifying inefficiency in decentralized decision-making. Method: This paper proposes an external recommendation intervention paradigm that dynamically manipulates the observed states of Q-learners in repeated congestion games—including those exhibiting Braess’s paradox—to steer learning toward socially optimal equilibria while preserving individual autonomy. Contribution/Results: We formally define the *learning dynamics manipulation problem* and establish a positive correlation between recommendation space size and system controllability. Integrating Q-learning, game-theoretic modeling, and recommendation policy optimization, our approach robustly reduces PoA across diverse congestion scenarios. Empirical results demonstrate consistent performance improvement as the recommendation space expands, validating scalability and efficacy in mitigating systemic inefficiency without centralized control.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Varied real world systems such as transportation networks, supply chains and energy grids present coordination problems where many agents must learn to share resources. It is well known that the independent and selfish interactions of agents in these systems may lead to inefficiencies, often referred to as the `Price of Anarchy'. Effective interventions that reduce the Price of Anarchy while preserving individual autonomy are of great interest. In this paper we explore recommender systems as one such intervention mechanism. We start with the Braess Paradox, a congestion game model of a routing problem related to traffic on roads, packets on the internet, and electricity on power grids. Following recent literature, we model the interactions of agents as a repeated game between $Q$-learners, a common type of reinforcement learning agents. This work introduces the Learning Dynamic Manipulation Problem, where an external recommender system can strategically trigger behavior by picking the states observed by $Q$-learners during learning. Our computational contribution demonstrates that appropriately chosen recommendations can robustly steer the system towards convergence to the social optimum, even for many players. Our theoretical and empirical results highlight that increases in the recommendation space can increase the steering potential of a recommender system, which should be considered in the design of recommender systems.

Problem

• Reducing inefficiencies in multi-agent systems.
• Steering agents towards social optimum using recommendations.
• Enhancing system performance with strategic state observations.

Innovation

• Recommender systems reduce inefficiencies
• Q-learners model agent interactions
• Strategic recommendations steer social optimum