{"type":"ScholarlyArticle","title":"Altruism Design in Networked Public Goods Games","authors":[{"name":"Sixie Yu","slug":"sixie-yu","given":"Sixie","family":"Yu","affiliation":""},{"name":"David Kempe","slug":"david-kempe","given":"David","family":"Kempe","affiliation":""},{"name":"Yevgeniy Vorobeychik","slug":"yevgeniy-vorobeychik","given":"Yevgeniy","family":"Vorobeychik","affiliation":""}],"abstract":"Many collective decision-making settings feature a strategic tension\r\nbetween agents acting out of individual self-interest and promoting a common good.\r\nThese include wearing face masks during a pandemic, voting, and vaccination.\r\nNetworked public goods games \r\ncapture this tension, with networks encoding strategic interdependence among agents.\r\nConventional models of public goods games posit solely individual self-interest as a motivation, even though altruistic\r\nmotivations have long been known to play a significant role in agents' decisions.\r\nWe introduce a novel extension of public goods games to account for\r\naltruistic motivations by adding a term in the utility function that\r\nincorporates the perceived benefits an agent obtains from the welfare\r\nof others, mediated by an altruism graph.\r\nMost importantly, we view altruism not as immutable, but rather as a lever for promoting the common good.\r\nOur central algorithmic question then revolves around the\r\ncomputational complexity of modifying the altruism network to achieve desired public goods game investment profiles.\r\nWe first show that the problem can be solved using linear programming\r\nwhen a principal can fractionally modify the altruism network.\r\nWhile the problem becomes in general intractable if the principal's\r\nactions are all-or-nothing, we exhibit several tractable special cases.","doi":"10.24963/ijcai.2021/69","url":"https://ijcai.org/proceedings/2021/69","pdf":"https://ijcai.org/proceedings/2021/0069.pdf","markdown":"https://ijcai.org/proceedings/2021/69.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/69","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":69,"pages":{"first":493,"last":499},"keywords":["Agent-based and Multi-agent Systems: Algorithmic Game Theory","Agent-based and Multi-agent Systems: Noncooperative Games"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}