{"type":"ScholarlyArticle","title":"Indirect Trust is Simple to Establish","authors":[{"name":"Elham Parhizkar","slug":"elham-parhizkar","given":"Elham","family":"Parhizkar","affiliation":"Department of Computer Science , University of Regina , Canada"},{"name":"Mohammad Hossein Nikravan","slug":"mohammad-hossein-nikravan","given":"Mohammad Hossein","family":"Nikravan","affiliation":"Department of Computer Science , University of Regina , Canada"},{"name":"Sandra Zilles","slug":"sandra-zilles","given":"Sandra","family":"Zilles","affiliation":"Department of Computer Science , University of Regina , Canada"}],"abstract":"In systems with multiple potentially deceptive agents, any single agent may have to assess the trustworthiness of other agents in order to decide with which agents to interact. In this context, indirect trust refers to trust established through third-party advice. Since the advisers themselves may be deceptive or unreliable, agents need a mechanism to assess and properly incorporate advice. We evaluate existing state-of-the-art methods for computing indirect trust in numerous simulations, demonstrating that the best ones tend to be of prohibitively large complexity. We propose a new and easy to implement method for computing indirect trust, based on a simple prediction with expert advice strategy as is often used in online learning. This method either competes with or outperforms all tested systems in the vast majority of the settings we simulated, while scaling substantially better. Our results demonstrate that existing systems for computing indirect trust are overly complex; the problem can be solved much more efficiently than the literature suggests.","doi":"10.24963/ijcai.2019/446","url":"https://ijcai.org/proceedings/2019/446","pdf":"https://ijcai.org/proceedings/2019/0446.pdf","markdown":"https://ijcai.org/proceedings/2019/446.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/446","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":"Machine Learning M-Z","track":"Main track","number":446,"pages":{"first":3216,"last":3222},"keywords":["Agent-based and Multi-agent Systems: Trust and Reputation","Machine Learning: Online Learning"],"videos":[],"references":[{"position":1,"raw":"[Burnett et al., 2010] Chris Burnett, Timothy J. 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