Properties of Referral Networks: Emergence of Authority and Trust

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Title: Properties of Referral Networks: Emergence of Authority and Trust
Author: Yolum, Pinar
Advisors: Munindar P. Singh, Committee Chair
Abstract: Developing, maintaining, and disseminating trust in open environments is crucial. We develop a decentralized approach to trust in the context of service location. Service providers and consumers are modeled as autonomous agents participating in a multiagent system that functions as a referral network. When a service is requested, an agent may provide the requested service or give a referral to another agent. The agents can judge the quality of service obtained. Importantly the agents can adaptively select their neighbors, decide with whom to interact, and choose how to give referrals. The agents' actions lead to the evolution of the referral network. We study the emergent properties of referral networks, especially those dealing with their quality, efficiency, and structure. We first show how the exchange of referrals affect locating service providers, then identify undesirable network structures and show under which conditions these network structures emerge. A referral corresponds to a customized link generated on demand by one agent for another. Referrals thus yield a basis for studying the processes underlying trust and authority, especially as they affect the structure of the evolving social network of agents. Whereas existing work takes an after-the-fact look at Web structure, we can study the emergence of structure as it relates to the policies of the members. Further, we propose a graph-based representation of services that can be applied in conjunction with our referrals-based approach. This representation captures natural relationships among service domains and provides a simple means to accommodate the accrual of trust placed in a given party. Using this representation we study how number of interactions and ease at which agents try new providers affect locating trustworthy providers. The properties uncovered through this dissertation can serve as guidelines to develop robust referral systems that are both efficient and effective.
Date: 2003-06-24
Degree: PhD
Discipline: Computer Science
URI: http://www.lib.ncsu.edu/resolver/1840.16/5277


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