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Artificial Intelligence and Spontaneous Collusion (2202.05946v5)

Published 12 Feb 2022 in econ.TH, cs.AI, and cs.GT

Abstract: We develop a tractable model for studying strategic interactions between learning algorithms. We uncover a mechanism responsible for the emergence of algorithmic collusion. We observe that algorithms periodically coordinate on actions that are more profitable than static Nash equilibria. This novel collusive channel relies on an endogenous statistical linkage in the algorithms' estimates which we call spontaneous coupling. The model's parameters predict whether the statistical linkage will appear, and what market structures facilitate algorithmic collusion. We show that spontaneous coupling can sustain collusion in prices and market shares, complementing experimental findings in the literature. Finally, we apply our results to design algorithmic markets.

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Citations (6)
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Summary

  • The paper demonstrates that spontaneous coupling drives unintended collusion among learning algorithms through reinforcement-based updates.
  • It uses a fluid approximation to convert a stochastic framework into a continuous model for analyzing strategic algorithm interactions.
  • The study finds that uneven learning rates lead to higher cooperation rates, highlighting the need for regulatory shifts in algorithmic markets.

Analysis of Strategic Interactions and Collusion Among Learning Algorithms

Introduction

Recent advancements in AI have led to the widespread deployment of algorithmic solutions across various sectors, particularly in setting prices and defining market shares. This paper develops a model that elucidates the strategic interactions between learning algorithms, unlocking insights into the phenomenon of algorithmic collusion. Unlike traditional forms of collusion that depend on explicit agreements or tacit understanding among human-based entities, algorithmic collusion stems from the intrinsic characteristics of the learning algorithms themselves. This paper coins the term "spontaneous coupling" to describe a novel mechanism through which independent, myopic algorithms synchronize their strategies to achieve collusive outcomes, without any programmed intent to do so.

Model Overview

At the heart of our analysis lies the concept of the reinforcer, a class of learning algorithms characterized by their reinforcement of successful actions through payoff-based updates. This reinforcement process, governed by a policy function, trades off exploration and exploitation to maximize payoff. By employing a fluid approximation technique, we transition from a discrete, stochastic framework to a deterministic continuous model, facilitating the analytical examination of the learning dynamics.

Key Findings

We demonstrate that spontaneous coupling manifests under certain conditions, leading to cooperation rates higher than those predicted by Nash equilibria in games such as the Prisoner's Dilemma. Our model reveals that the phenomenon hinges on the discrepancy in learning rates across different actions within the algorithms. Specifically, when learning rates are uneven, algorithms tend to synchronize their actions, resulting in collusive outcomes characterized by periodic cycles of high cooperation rates. Notably, algorithms with uniform learning rates across actions are immune to spontaneous coupling, underscoring the importance of learning rate configurations in preventing inadvertent collusion.

Implications for Algorithmic Markets

Our findings have profound implications for both the theoretical understanding and practical design of algorithmic markets. The identification of spontaneous coupling as a catalyst for collusion challenges the prevailing regulatory framework, which traditionally focuses on intent and explicit coordination among market participants. Thus, our model underscores the need for a regulatory paradigm shift to address collusion in algorithmic settings. Furthermore, by elucidating the mechanics of spontaneous coupling, this paper lays a foundation for developing strategies to mitigate or prevent collusive outcomes in markets dominated by algorithmic players.

Future Directions

This paper opens new avenues for research into the dynamics of learning algorithms in strategic settings. Future work could explore the robustness of spontaneous coupling across a wider array of game theoretic models and examine the sensitivity of collusive outcomes to variations in algorithm design parameters. Additionally, investigating countermeasures, such as modifications to learning algorithms or market design interventions, to prevent or mitigate algorithmic collusion presents a fruitful direction for subsequent research.

Conclusion

In conclusion, this paper offers a groundbreaking perspective on the dynamics of learning algorithms in competitive environments, unveiling the spontaneous coupling mechanism as a novel driver of algorithmic collusion. By bridging the gap between theoretical models and real-world phenomena, this research not only enhances our understanding of algorithmic behavior in markets but also prompts a reevaluation of regulatory approaches to safeguard competition in the digital economy.

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