{"type":"ScholarlyArticle","title":"FaRM: Fair Reward Mechanism for Information Aggregation in Spontaneous Localized Settings","authors":[{"name":"Moin Hussain Moti","slug":"moin-hussain-moti","given":"Moin Hussain","family":"Moti","affiliation":"International Institute of Information Technology Hyderabad , India"},{"name":"Dimitris Chatzopoulos","slug":"dimitris-chatzopoulos","given":"Dimitris","family":"Chatzopoulos","affiliation":"The Hong Kong University of Science and Technology , Hong Kong"},{"name":"Pan Hui","slug":"pan-hui","given":"Pan","family":"Hui","affiliation":"The Hong Kong University of Science and Technology , Hong Kong; University of Helsinki , Finland"},{"name":"Sujit Gujar","slug":"sujit-gujar","given":"Sujit","family":"Gujar","affiliation":"International Institute of Information Technology Hyderabad , India"}],"abstract":"Although peer prediction markets are widely used in crowdsourcing to aggregate information from agents, they often fail to reward the participating agents equitably. Honest agents can be wrongly penalized if randomly paired with dishonest ones. In this work, we introduce selective and cumulative fairness. We characterize a mechanism as fair if it satisfies both notions and present FaRM, a representative mechanism we designed. FaRM is a Nash incentive mechanism that focuses on information aggregation for spontaneous local activities which are accessible to a limited number of agents without assuming any prior knowledge of the event. All the agents in the vicinity observe the same information. FaRM uses (i) a report strength score to remove the risk of random pairing with dishonest reporters, (ii) a consistency score to measure an agent's history of accurate reports and distinguish valuable reports, (iii) a reliability score to estimate the probability of an agent to collude with nearby agents and prevents agents from getting swayed, and (iv) a location robustness score to filter agents who try to participate without being present in the considered setting. Together, report strength, consistency, and reliability represent a fair reward given to agents based on their reports.","doi":"10.24963/ijcai.2019/72","url":"https://ijcai.org/proceedings/2019/72","pdf":"https://ijcai.org/proceedings/2019/0072.pdf","markdown":"https://ijcai.org/proceedings/2019/72.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/72","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":72,"pages":{"first":506,"last":512},"keywords":["Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems","Agent-based and Multi-agent Systems: Trust and Reputation"],"videos":[],"references":[{"position":1,"raw":"[Chatzopoulos et al., 2016] Dimitris Chatzopoulos, Sujit Gujar, Boi Faltings, and Pan Hui. 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