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Auteurs principaux: Bahmani, Sarvin, Paul, Soumyajit, Schewe, Sven, Kalat, Shadi Tasdighi, Trivedi, Ashutosh
Format: Preprint
Publié: 2026
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Accès en ligne:https://arxiv.org/abs/2605.12251
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author Bahmani, Sarvin
Paul, Soumyajit
Schewe, Sven
Kalat, Shadi Tasdighi
Trivedi, Ashutosh
author_facet Bahmani, Sarvin
Paul, Soumyajit
Schewe, Sven
Kalat, Shadi Tasdighi
Trivedi, Ashutosh
contents 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. We 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 realized as pure finite-memory counting strategies, require only polynomial memory in the system size, and can be synthesized 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.
format Preprint
id arxiv_https___arxiv_org_abs_2605_12251
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Social Welfare under Heterogeneous Time Preferences
Bahmani, Sarvin
Paul, Soumyajit
Schewe, Sven
Kalat, Shadi Tasdighi
Trivedi, Ashutosh
Computer Science and Game Theory
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. We 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 realized as pure finite-memory counting strategies, require only polynomial memory in the system size, and can be synthesized 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.
title Social Welfare under Heterogeneous Time Preferences
topic Computer Science and Game Theory
url https://arxiv.org/abs/2605.12251