Welfare Equilibria: A Solution to Arrogance and Catastrophe in Stackelberg Self-Play

Core Concepts

Welfare Equilibria (WE) provide a generalization of Stackelberg strategies that can recover desirable Nash Equilibria in non-coincidental games, where the Stackelberg strategy profile fails. The Welfare Function Search (WelFuSe) algorithm adaptively chooses an appropriate welfare function to avoid catastrophe in self-play while preserving performance against naive learning opponents.

Abstract

The content discusses the challenges of learning in multi-agent systems, where agents may have misaligned incentives and the environment is non-stationary. Opponent shaping (OS) approaches, which explicitly consider the opponent's incentives and behavior, have been proposed to address these challenges. The authors first show that the Stackelberg strategy profile, in which both players choose Stackelberg strategies, represents a sensible solution concept in many two-player games. They then demonstrate that several existing OS algorithms can be derived as approximations to Stackelberg strategies. However, the authors identify a class of "non-coincidental games" in which the Stackelberg strategy profile is not a Nash Equilibrium (NE). This includes several canonical matrix games, such as the Chicken Game, where the Stackelberg strategy profile leads to catastrophic outcomes in self-play. To address this issue, the authors introduce Welfare Equilibria (WE) as a generalization of Stackelberg strategies. WE allows each player to choose a welfare function, which is then maximized while assuming the opponent plays a best response. The authors show that appropriate choices of welfare functions, such as egalitarian or fairness-based functions, can recover desirable NE solutions in non-coincidental games. Finally, the authors present Welfare Function Search (WelFuSe), a practical algorithm that adaptively chooses the best welfare function from a predefined set, based on experience. WelFuSe is able to preserve performance against naive learning opponents while avoiding catastrophe in self-play, by learning to select non-greedy welfare functions when appropriate.

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Key Insights Distilled From

The Danger Of Arrogance

by Jake Levi,Ch... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2402.01088.pdf

Deeper Inquiries

What are the limitations of the predefined set of welfare functions used in WelFuSe, and how could the algorithm be extended to learn more general welfare functions

The limitations of the predefined set of welfare functions used in WelFuSe lie in their fixed nature, which may not capture the full spectrum of possible welfare considerations in different games. To extend the algorithm to learn more general welfare functions, one approach could be to incorporate a mechanism for adaptive learning of welfare functions. This could involve introducing a parameterized family of welfare functions and using a reinforcement learning framework to update the parameters based on the performance of the agent in different games. By allowing the algorithm to dynamically adjust the welfare function based on the game dynamics and opponent behavior, it can learn to optimize for a wider range of objectives.

How could the Welfare Equilibrium concept be extended to n-player games, and what are the challenges in doing so

Extending the Welfare Equilibrium concept to n-player games presents several challenges. One key challenge is the increased complexity of interactions and strategies in n-player settings, which can make it difficult to define a single welfare function that accurately captures the preferences of all players. Additionally, finding a solution concept that balances the welfare of multiple players in a fair and efficient manner becomes more intricate as the number of players increases. Addressing these challenges may involve developing new theoretical frameworks for defining and computing Welfare Equilibria in n-player games, considering the interplay of individual and collective welfare objectives.

What are the potential real-world applications of Welfare Equilibria and WelFuSe, and how could they be used to improve the safety and robustness of multi-agent systems deployed in the real world

The potential real-world applications of Welfare Equilibria and WelFuSe are vast, particularly in domains where multi-agent systems are deployed, such as autonomous driving, resource allocation, and strategic decision-making. By leveraging Welfare Equilibria, these systems can achieve more cooperative and mutually beneficial outcomes, enhancing overall system performance and efficiency. WelFuSe can be instrumental in improving the safety and robustness of multi-agent systems by enabling agents to adaptively choose welfare functions based on the context and opponent behavior, leading to more stable and desirable outcomes in dynamic environments. This can have significant implications for enhancing the reliability and effectiveness of AI systems in real-world scenarios.