算法电力市场中的AI智能体：关于默契合谋的涌现
AI agents in Algorithmic Electricity Markets: On the Emergence of Tacit Collusion
- Technical University of Denmark（丹麦技术大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文研究发现，在寡头垄断的算法电力市场中，基于多智能体强化学习的自主投标智能体可在未被指示合谋的情况下，通过独立学习涌现出支持默契合谋的超竞争结果。
中文摘要 AI 辅助
随着电力市场参与者越来越多地采用基于学习的智能体制定投标策略，电力市场正逐渐走向算法化。其他领域算法市场的证据表明，默契合谋可纯粹通过独立学习产生。此外，电力市场通常是寡头垄断的，且在少数参与者之间存在重复交互，这使其在结构上易受非竞争行为影响。针对这些观察，本文研究了以下假设：当电力市场参与者的行为由自主学习型算法控制时，可能会涌现出默契合谋。我们将策略投标建模为具有不完美公共监测的重复博弈，并使用多智能体强化学习对参与者的涌现行为进行建模。我们提出了一套多维标准（超出了与纳什均衡的利润比较）来评估所得行为是否构成默契合谋。实验结果表明，这种危险对电力市场而言是现实存在的：存在一些情况，即使智能体从未被指示进行合谋，它们也确实学会了维持支持默契合谋指标的超竞争结果。
英文摘要
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.
