{"type":"ScholarlyArticle","title":"Learning in Markets: Greed Leads to Chaos but Following the Price is Right","authors":[{"name":"Yun Kuen Cheung","slug":"yun-kuen-cheung","given":"Yun Kuen","family":"Cheung","affiliation":""},{"name":"Stefanos Leonardos","slug":"stefanos-leonardos","given":"Stefanos","family":"Leonardos","affiliation":""},{"name":"Georgios Piliouras","slug":"georgios-piliouras","given":"Georgios","family":"Piliouras","affiliation":""}],"abstract":"We study learning dynamics in distributed production economies such as blockchain mining, peer-to-peer file sharing and crowdsourcing. These economies can be modelled as multi-product Cournot competitions or all-pay auctions (Tullock contests) when individual firms have market power, or as Fisher markets with quasi-linear utilities when every firm has negligible influence on market outcomes. In the former case, we provide a formal proof that Gradient Ascent (GA) can be Li-Yorke chaotic for a step size as small as Θ(1/n), where n is the number of firms. In stark contrast, for the Fisher market case, we derive a Proportional Response (PR) protocol that converges to market equilibrium. The positive results on the convergence of the PR dynamics are obtained in full generality, in the sense that they hold for Fisher markets with any quasi-linear utility functions. Conversely, the chaos results for the GA dynamics are established even in the simplest possible setting of two firms and one good, and they hold for a wide range of price functions with different demand elasticities. Our findings suggest that by considering multi-agent interactions from a market rather than a game-theoretic perspective, we can formally derive natural learning protocols which are stable and converge to effective outcomes rather than being chaotic.","doi":"10.24963/ijcai.2021/16","url":"https://ijcai.org/proceedings/2021/16","pdf":"https://ijcai.org/proceedings/2021/0016.pdf","markdown":"https://ijcai.org/proceedings/2021/16.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/16","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":16,"pages":{"first":111,"last":117},"keywords":["Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems","Agent-based and Multi-agent Systems: Multi-agent Learning","Agent-based and Multi-agent Systems: Noncooperative Games"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}