{"type":"ScholarlyArticle","title":"Social Welfare Under Heterogeneous Time Preferences","authors":[{"name":"Sarvin Bahmani","slug":"sarvin-bahmani","given":"Sarvin","family":"Bahmani","affiliation":""},{"name":"Soumyajit Paul","slug":"soumyajit-paul","given":"Soumyajit","family":"Paul","affiliation":""},{"name":"Sven Schewe","slug":"sven-schewe","given":"Sven","family":"Schewe","affiliation":""},{"name":"Shadi Tasdighi Kalat","slug":"shadi-tasdighi-kalat","given":"Shadi","family":"Tasdighi Kalat","affiliation":""},{"name":"Ashutosh Trivedi","slug":"ashutosh-trivedi","given":"Ashutosh","family":"Trivedi","affiliation":""}],"abstract":"In several socioeconomic-critical decision-making settings, such as fair resource allocation, climate policy, or AI alignment, multiple principals interact within a common arena. While it is well established that these principals may have differing preferences, decision-making under heterogeneous time preferences remains relatively unexplored. In particular, principals may weigh future outcomes differently and may derive distinct utilities from the same decisions. Motivated by such scenarios, we introduce the notion of heterogeneous time preferences in MDPs, where multiple principals possess distinct reward functions and apply different discount factors to future rewards. To compute meaningful decisions in such settings, an AI agent must rely on a notion of optimality that accounts for the preferences of all principals. \r\n\r\n\r\nWe adopt a utilitarian notion of social welfare, defined as the sum of utilities accrued to all principals, and study the synthesis of agent strategies that maximise this welfare. Under heterogeneous time preferences, we show that optimal strategies are no longer positional, even when all principals receive identical rewards. Nevertheless, optimal strategies remain structurally simple: they can be realised as pure finite-memory counting strategies, require only polynomial memory in the system size, and can be synthesised in polynomial time. On the other hand, we show that deciding threshold questions for optimal positional strategies is NP-hard, exposing a poor trade-off: insisting on positional simplicity neither makes synthesis tractable nor preserves social welfare.","doi":"10.24963/ijcai.2026/3","url":"https://ijcai.org/proceedings/2026/3","pdf":"https://ijcai.org/proceedings/2026/0003.pdf","markdown":"https://ijcai.org/proceedings/2026/3.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/3","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":3,"pages":{"first":20,"last":27},"keywords":["Agent-based and Multi-agent Systems: Formal verification, validation and synthesis","Planning and Scheduling: Theoretical foundations of planning","Planning and Scheduling: Markov decisions processes"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}