Mitigating Retaliatory Algorithmic Collusion in Repeated Games

Problem

The paper addresses a gap in existing mitigation approaches for algorithmic collusion specifically in the context of general repeated games. The authors highlight the inadequacy of current methods to effectively prevent collusion among agents, particularly in scenarios modeled by repeated interactions.

Method

The authors propose a novel framework named CURB (Collusion Unwinding via Reward shaping and Belief injection). The core technical contributions include:

• Connection Formalization: The framework formalizes the connection between empirical observations from Q-learning collusion and Simple Penal Codes (SPCs).
• Detection Mechanism: It employs a detection mechanism based on the total variation distance between action distributions derived from cooperation and defection histories.
• Penalty Mechanism: CURB introduces a penalty that is applied to the total variation distance signal during the Q-learning process, effectively discouraging collusive behavior.
• Guarantee: The framework guarantees that any fixed point of an SPC can be converted into a trivial one, thereby preventing the emergence of collusive equilibria among agents.

Results

The results demonstrate that CURB significantly reduces collusion among Q-learning agents in both Bertrand and Cournot Competition Repeated Games. Furthermore, the framework is shown to generalize effectively to deep Q-network agents operating within the context of Bertrand competition. The available text does not report quantitative results.

Limitations

The authors do not report any limitations in their work, and no obvious limitations are identified in the provided text.

Why it matters

The implications of this work are substantial for the design of multi-agent systems, particularly in economic and competitive environments where collusion can undermine fairness and efficiency. By providing a robust mechanism to mitigate collusion, CURB opens avenues for further research into stable and competitive agent interactions in repeated games.