Robustness and Approximation of Discrete-time Mean-field Games under Discounted Cost Criterion

[2310.10828] Robustness and Approximation of Discrete-time Mean-field Games under Discounted Cost Criterion Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Systems and Control arXiv:2310.10828 (eess) [Submitted on 16 Oct 2023] Title: Robustness and Approximation of Discrete-time Mean-field Games under Discounted Cost Criterion Authors: Uğur Aydın , Naci Saldi View a PDF of the paper titled Robustness and Approximation of Discrete-time Mean-field Games under Discounted Cost Criterion, by U\u{g}ur Ayd{\i}n and Naci Saldi View PDF HTML (experimental) Abstract: In this paper, we investigate the robustness of stationary mean-field equilibria in the presence of model uncertainties, specifically focusing on infinite-horizon discounted cost functions. To achieve this, we initially establish convergence conditions for value iteration-based algorithms in mean-field games. Subsequently, utilizing these results, we demonstrate that the mean-field equilibrium obtained through this value iteration algorithm remains robust even in the face of system dynamics misspecifications. We then apply these robustness findings to the finite model approximation problem in mean-field games, showing that if the state space quantization is fine enough, the mean-field equilibrium for the finite model closely approximates the nominal one. Comments: 35 Pages Subjects: Systems and Control (eess.SY) ; Optimization and Control (math.OC) Cite as: arXiv:2310.10828 [eess.SY] (or arXiv:2310.10828v1 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2310.10828 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1287/moor.2023.0316 Focus to learn more DOI(s) linking to related resources Submission history From: Uğur Aydın [ view email ] [v1] Mon, 16 Oct 2023 21:03:41 UTC (37 KB) Full-text links: Access Paper: View a PDF of the paper titled Robustness and Approximation of Discrete-time Mean-field Games under Discounted Cost Criterion, by U\u{g}ur Ayd{\i}n and Naci Saldi View PDF HTML (experimental) TeX Source view license Current browse context: eess.SY < prev | next > new | recent | 2023-10 Change to browse by: cs cs.SY eess math math.OC 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

Related documents

• Mean-field quadratic BSDEs and related mean-field portfolio games of controls#985319
• Evolutionary Analysis of Continuous-time Finite-state Mean Field Games with Discounted Payoffs#1156229
• Ranking Quantilized Mean-Field Games with an Application to Early-Stage Venture Investments#1034659
• Constant regret in general games via higher-order optimism#926951
• Who Buys and Sells Options: The Role and Pricing of Options in an Economy with Background Risk#942553