Memoryless Algorithmic Collusion: Sure to Fail, Slow to Fall

[2409.01147] Memoryless Algorithmic Collusion: Sure to Fail, Slow to Fall Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Economics > Theoretical Economics arXiv:2409.01147 (econ) [Submitted on 2 Sep 2024 ( v1 ), last revised 9 Aug 2026 (this version, v2)] Title: Memoryless Algorithmic Collusion: Sure to Fail, Slow to Fall Authors: Zhang Xu , Wei Zhao View a PDF of the paper titled Memoryless Algorithmic Collusion: Sure to Fail, Slow to Fall, by Zhang Xu and Wei Zhao View PDF HTML (experimental) Abstract: This paper shows that, in a class of Bertrand-style competition games, memoryless Q-learning algorithms should adapt to Nash Equilibrium given sufficient explorations in the long-run. This is also verified through accelerating simulations, while the convergence time grows super-exponentially for high discount factors. The resilience of collusive outcomes in the short-run is due to exploration of unprofitable actions, making the system resemble random walk. A structural model is proposed to estimate the convergence time, which not only explains the role of discount factor, but also unveils the non-monotonic relation in learning rate, which is overlooked in the literature. Subjects: Theoretical Economics (econ.TH) ; Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA) Cite as: arXiv:2409.01147 [econ.TH] (or arXiv:2409.01147v2 [econ.TH] for this version) https://doi.org/10.48550/arXiv.2409.01147 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhang Xu [ view email ] [v1] Mon, 2 Sep 2024 10:33:42 UTC (861 KB) [v2] Sun, 9 Aug 2026 03:48:37 UTC (536 KB) Full-text links: Access Paper: View a PDF of the paper titled Memoryless Algorithmic Collusion: Sure to Fail, Slow to Fall, by Zhang Xu and Wei Zhao View PDF HTML (experimental) TeX Source view license Current browse context: econ.TH < prev | next > new | recent | 2024-09 Change to browse by: cs cs.GT cs.MA econ References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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