{"type":"ScholarlyArticle","title":"Forgiving Debt in Financial Network Games","authors":[{"name":"Panagiotis Kanellopoulos","slug":"panagiotis-kanellopoulos","given":"Panagiotis","family":"Kanellopoulos","affiliation":"University of Essex , UK"},{"name":"Maria Kyropoulou","slug":"maria-kyropoulou","given":"Maria","family":"Kyropoulou","affiliation":"University of Essex , UK"},{"name":"Hao Zhou","slug":"hao-zhou","given":"Hao","family":"Zhou","affiliation":"University of Essex , UK"}],"abstract":"We consider financial networks, where nodes correspond to banks and directed labeled edges correspond to debt contracts between banks. Maximizing systemic liquidity, i.e., the total money flow, is a natural objective of any financial authority. In particular, the financial authority may offer bailout money to some bank(s) or forgive the debts of others in order  to maximize liquidity, and we examine efficient ways to achieve this. We study the computational hardness of finding the optimal debt-removal and budget-constrained optimal bailout policy, respectively, and we investigate the approximation ratio provided by the greedy bailout policy compared to the optimal one. \r\n \r\nWe also study financial systems from a game-theoretic standpoint. We observe that the removal of some incoming debt might be in the best interest of a bank. Assuming that a bank's well-being (i.e., utility) is aligned with the incoming payments they receive from the network, we define and analyze a game among banks who want to maximize their utility by strategically giving up some incoming payments. In addition, we extend the previous game by considering bailout payments. After formally defining the above games, we prove results about the existence and quality of pure Nash equilibria, as well as the computational complexity of finding such equilibria.","doi":"10.24963/ijcai.2022/48","url":"https://ijcai.org/proceedings/2022/48","pdf":"https://ijcai.org/proceedings/2022/0048.pdf","markdown":"https://ijcai.org/proceedings/2022/48.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/48","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":48,"pages":{"first":335,"last":341},"keywords":["Agent-based and Multi-agent Systems: Noncooperative Games","Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems","Agent-based and Multi-agent Systems: Algorithmic Game Theory","Multidisciplinary Topics and Applications: Finance"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987048","length_sec":78,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985191","length_sec":581,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bertschinger et al., 2020] Nils Bertschinger, Martin Hoefer, and Daniel Schmand. 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