{"type":"ScholarlyArticle","title":"Fair Distribution of Delivery Orders","authors":[{"name":"Hadi Hosseini","slug":"hadi-hosseini","given":"Hadi","family":"Hosseini","affiliation":"Pennsylvania State University"},{"name":"Shivika Narang","slug":"shivika-narang","given":"Shivika","family":"Narang","affiliation":"University of New South Wales"},{"name":"Tomasz Wąs","slug":"tomasz-was","given":"Tomasz","family":"Wąs","affiliation":"University of Oxford"}],"abstract":"We initiate the study of fair distribution of delivery tasks among a set of agents wherein delivery jobs are placed along the vertices of a graph. Our goal is to fairly distribute delivery costs (modeled as a submodular function) among a fixed set of agents while satisfying some desirable notions of economic efficiency. We adopt well-established fairness concepts—such as envy-freeness up to one item (EF1) and minimax share (MMS)—to our setting and show that fairness is often incompatible with the efficiency notion of social optimality. Yet, we characterize instances that admit fair and socially optimal solutions by exploiting graph structures. We further show that achieving fairness along with Pareto optimality is computationally intractable. Nonetheless, we design an XP algorithm (parameterized by the number of agents) for finding MMS and Pareto optimal solutions on every tree instance, and show that the same algorithm can be modified to find efficient solutions along with EF1, when such solutions exist. We complement these results by theoretically and experimentally analyzing the price of fairness.","doi":"10.24963/ijcai.2024/313","url":"https://ijcai.org/proceedings/2024/313","pdf":"https://ijcai.org/proceedings/2024/0313.pdf","markdown":"https://ijcai.org/proceedings/2024/313.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/313","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Game Theory and Economic Paradigms","track":"Main Track","number":313,"pages":{"first":2825,"last":2833},"keywords":["Game Theory and Economic Paradigms: GTEP: Computational social choice","Game Theory and Economic Paradigms: GTEP: Fair division"],"videos":[],"references":[{"position":1,"raw":"[Aziz et al., 2022] Haris Aziz, Ioannis Caragiannis, Ayumi Igarashi, and Toby Walsh. 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