# AI agents in Algorithmic Electricity Markets: On the Emergence of Tacit Collusion

arXiv: https://arxiv.org/abs/2608.26896 · HTML: https://mlsanity.com/paper/2608.26896/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 27 Aug 2026.

## Builds on (most cited first, 1 of 1)

- [Algorithmic Bidding for Virtual Trading in Electricity Markets](https://mlsanity.com/paper/1802.03010/) (2018)

## Built on by (most cited first, 1 of 1)

- [Beyond Supra-Competitive Outcomes: Collusive Behaviour in Deep Reinforcement Learning for Optimal Execution Games](https://mlsanity.com/paper/2610.00619/) (2026)

## Abstract (arXiv)

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.