AI竞价代理可能在电力市场自发形成隐性串通，无需明确合谋。
AI agents in Algorithmic Electricity Markets: On the Emergence of Tacit Collusion
- 用多智能体强化学习模拟电力市场重复博弈，研究自主代理行为。
- 实验显示代理能自发维持高于竞争水平的电价，符合隐性串通特征。
- 适用于关注算法治理与市场公平性的政策制定者与研究人员。
随着电力市场参与者越来越多地采用基于学习的代理进行竞价策略，电力市场正逐渐变得算法化。其他领域算法市场的证据表明，即使在独立学习的情况下也可能出现隐性串通。此外，电力市场通常为寡头结构，参与者数量少且重复互动，结构性上容易滋生非竞争行为。本文探究了这一假设：当参与者行为由自主学习算法控制时，电力市场中可能出现隐性串通。我们把战略竞价建模为具有不完全公共监控的重复博弈，并使用多智能体强化学习来模拟参与者的涌现行为。提出一个多维度评估标准（超越利润对比纳什均衡），以判断行为是否构成隐性串通。实验结果表明，这种风险在电力市场中是真实的：存在案例中，代理确实学会了维持超竞争性结果，支持隐性串通指标，尽管这些代理从未被指示合谋。
原文摘要 · Abstract (English)
As electricity market participants increasingly adopt learning-based agents for their bidding strategies, electricity markets are becoming algorithmic. Evidence from algorithmic markets in other domains shows that tacit collusion can arise purely through independent learning. Moreover, electricity markets are typically oligopolistic and feature repeated interaction among a small number of participants, making them structurally susceptible to non-competitive behavior. In the face of these observations, this paper investigates the hypothesis that tacit collusion may emerge in electricity markets where participants' actions are controlled by autonomous learning-based algorithms. We model strategic bidding as a repeated game with imperfect public monitoring, and model the participants' emergent behavior using multi-agent reinforcement learning. We propose a multi-dimensional set of criteria (going beyond profit comparisons against Nash equilibria) to assess whether the resulting behavior constitutes tacit collusion. Our experimental results showcase that such a danger is realistic for electricity markets: there are cases where agents do learn to sustain supra-competitive outcomes that are supportive of tacit collusion indicators, even though the agents were never instructed to collude.
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