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dc.contributor.authorNowostawski, Mariuszen_NZ
dc.contributor.authorFoukia, Noriaen_NZ
dc.date.available2011-04-07T03:05:36Z
dc.date.copyright2007-08en_NZ
dc.identifier.citationNowostawski, M., & Foukia, N. (2007). Social collaboration, stochastic strategies and information referrals (Information Science Discussion Papers Series No. 2007/05). University of Otago. Retrieved from http://hdl.handle.net/10523/920en
dc.identifier.urihttp://hdl.handle.net/10523/920
dc.description.abstractReferrals are used in multi-agent systems, network agents and peer-to-peer systems for the purpose of global or local information spreading to facilitate trust relationships and reciprocal interactions. Based on referral local interactions can be altered with a purpose to maximise the utility function of each of the participants, which in many cases requires mutual co-operation of participants. The referral system is often based on the global detailed or statistical behaviour of the overall society. Traditionally, referrals are collected by referring agents and the information is provided upon request to individuals. In this article, we provide a simple taxonomy of referral systems and on that basis we discuss three distinct ways information can be collected and aggregated. We analyse the effects of global vs. local information spreading, in terms of individual and global performance of a population based on the maximisation of a utility function of each of the agents. Our studies show that under certain conditions such as large number of non uniformly acting autonomous agents the spread of global information is undesirable. Collecting and providing local information only yields better overall results. In some experimental setups however, it might be necessary for global information to be available otherwise global stable optimal behaviour cannot be achieved. We analyse both of these extreme cases based on simple game-theoretic setup. We analyse and relate our results in the context of e-mail relying and spam filtering.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleSocial collaboration, stochastic strategies and information referralsen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages9en_NZ
otago.date.accession2007-09-21en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints725en_NZ
otago.school.eprintsSoftware Engineering & Collaborative Modelling Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
dc.description.references[1] B. Banerjee, R. Mukherjee, and S. Sen. Learning mutual trust. Working Notes of AGENTS-00 Workshop on Deception, Fraud and Trust in Agent Societies, pages 9–14, 2000. [2] Kaushik Basu. The traveler’s dilemma: Paradoxes of rationality in game theory. American Economic Review, 84(2):391–395, May 1994. [3] Rosaria Conte and Cristiano Castelfranchi. Social order in multiagent systems, chapter Chapter 2: Are incentives good enough to achieve (info)social order?, pages 45–63. Multiagent systems, artificial societies, and simulated organisations. Kluwer Academic Publishers, 2001. Editor: Rosaria Conte and Chrysanthos Dellarocas. [4] Noria Foukia, Li Zhou, and Clifford Neuman. Multilateral decisions for collaborative defense against unsolicited bulk email. In 4th Confernce on trust managment, Itrust2006, Pisa, Italy, 2006. [5] Eric Rasmusen. Games and Information: An Introduction to Game Theory. Blackwell Publishers, 2001. [6] S. Sen and N. Sajja. Robustness of reputation-based trust: boolean case. Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1, pages 288–293, 2002.en_NZ
otago.relation.number2007/05en_NZ
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