The paper examines whether autonomous learning-based agents in electricity markets can develop tacit collusion without explicit coordination. By modeling strategic bidding as a repeated game with imperfect public monitoring and employing multi-agent reinforcement learning, the authors identify conditions under which agents achieve supra-competitive outcomes. Their experiments demonstrate that such collusive behavior can emerge naturally, highlighting a realistic risk for algorithmic electricity markets.
By Jakub Seredy\'nski, Georgios Tsaousoglou
The paper introduces TruthMarketTwin, a simulation framework that uses agent-based modeling to study large language model (LLM) agents in e‑commerce markets characterized by asymmetric information. It models bilateral trade where sellers and buyers make strategic decisions about listings, purchases, ratings, and recourse to maximize profit and utility. The study finds that LLM agents can autonomously exploit weaknesses in reputation‑based governance, but that warrant enforcement can reduce deception and alter strategic behavior.
By Shijun Lei, Quang Nguyen, Swapneel S Mehta, Zeping Li, Huichuan Fu, Xiaolong Zheng, Siki Chen, Yunji Liang, Philip Torr, Zhenfei Yin
arXiv:2512. 04988v2 Announce Type: replace-cross Abstract: Emerging agentic marketplaces provide the economic infrastructure for matching and coordinating the large amounts of AI agents used in agentic swarms.
By Christopher Chiu, Simpson Zhang, Mihaela van der Schaar
The study investigates how large language models (LLMs) perform in a double auction market, a common economic mechanism. By replacing human participants with LLM agents, the authors find that markets with LLMs converge more slowly or not at all, leading to less efficient resource allocations. Analysis of trading decisions reveals significant variation across model families and roles, and a lexical study of Chain-of-Thought traces links trade execution to a shift from strategic thinking to urgency.
By Pawel Struski, Jakub Swistak, Inez Okulska, Przemyslaw Biecek
arXiv:2606. 05363v1 Announce Type: cross Abstract: On a platform with many sellers, should a pricing algorithm explicitly model competitors' prices when learning demand?
By Yuhang Wu, Assaf Zeevi
arXiv:2608. 03076v1 Announce Type: new Abstract: Multi-agent studies commonly place AI agents in predefined games, markets, or roles, making it difficult to distinguish endogenous economic organization from behavior inherited from the scenario.
By Lingyun Zhang, Shang Shang