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Computational Models of Trust and Reputation: Agents, Evolutionary Games, and Social Networks Computational Models of Trust and Reputation: Agents, Evolutionary Games, and Social Networks by Lik Mui B.S., M.Eng., Electrical Engineering and Computer Science Massachusetts Institute of Technology, 1995 M. Phil., Management Studies, Department of Social Studies University of Oxford, 1997 Submitted to the Department of Electrical Engineering and Computer Science in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Electrical Engineering and Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY December 20, 2002 Copyright 2002 Massachusetts Institute of Technology. All rights reserved. Signature of Author: ____________________________________________________________ Department of Electrical Engineering and Computer Science December 20, 2002 Certified by: ___________________________________________________________________ Peter Szolovits Professor of Electrical Engineering and Computer Science Thesis Supervisor Accepted by: __________________________________________________________________ Arthur C. Smith Professor of Electrical Engineering and Computer Science Chairman, Department Committee on Graduate Students Computational Models of Trust and Reputation: Agents, Evolutionary Games, and Social Networks by Lik Mui Submitted to the Department of Electrical Engineering and Computer Science on December 20, 2002 in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Electrical Engineering and Computer Science Abstract Many recent studies of trust and reputation are made in the context of commercial reputation or rating systems for online communities. Most of these systems have been constructed without a formal rating model or much regard for our sociological understanding of these concepts. We first provide a critical overview of the state of research on trust and reputation. We then propose a formal quantitative model for the rating process. Based on this model, we formulate two personalized rating schemes and demonstrate their effectiveness at inferring trust experimentally using a simulated dataset and a real world movie-rating dataset. Our experiments show that the popular global rating scheme widely used in commercial electronic communities is inferior to our personalized rating schemes when sufficient ratings among members are available. The level of sufficiency is then discussed. In comparison with other models of reputation, we quantitatively show that our framework provides significantly better estimations of reputation. “Better” is discussed with respect to a rating process and specific games as defined in this work. Secondly, we propose a mathematical framework for modeling trust and reputation that is rooted in findings from the social sciences. In particular, our framework makes explicit the importance of social information (i.e., indirect channels of inference) in aiding members of a social network choose whom they want to partner with or to avoid. Rating systems that make use of such indirect channels of inference are necessarily personalized in nature, catering to the individual context of the rater. Finally, we have extended our trust and reputation framework toward addressing a fundamental problem for social science and biology: evolution of cooperation. We show that by providing an indirect inference mechanism for the propagation of trust and reputation, cooperation among selfish agents can be explained for a set of game theoretic simulations. For these simulations in particular, our proposal is shown to have provided more cooperative agent communities than existing schemes are able to. Thesis Supervisor: Peter Szolovits Title: Professor of Electrical Engineering and Computer Science 2 BRIEF CONTENT Abstract ........................................................................................................ 2 Acknowledgements …………………………………………………………….. 10 Chapter 1 Introduction ......................................................................... 13 Chapter 2 Notions of Reputation ………………………………………… 20 Chapter 3 Online Rating Systems ……………………………………….. 31 Chapter 4 Rating Experiments …………………………………………… 46 Chapter 5 A Computational Model of Trust and Reputation ……….. 71 Chapter 6 Reputation Experiments …………………………………….. 85 Chapter 7 Evolution of Cooperation ……………………………………. 92 Chapter 8 Evolution of Cooperation by Social Information ………… 104 Chapter 9 Conclusion and Future Work ……………………………….. 118 Appendix A Preference-based Rating Propagation ……………………. 123 Appendix B Bayesian Rating Propagation ………………………………. 125 Appendix C Cooperation, Irrationality, and Economics ……………….. 126 Bibliography ……………………………………………………………………… 131 3 CONTENT ABSTRACT ………………………………………………………………………. 2 BRIEF CONTENT ……………………………………………………………….. 3 CONTENT ………………………………………………………………………… 4 ACKNOWLEDGEMENT ………………………………………………………… 10 CHAPTER 1 INTRODUCTION 1.1 Trust and Reputation …………………………………………………… 13 1.1.1 Formal Trust Producing Mechanisms ……………………………….. 14 1.1.2 Informal Trust Producing Mechanisms ………………………………. 14 1.2 Trust and Reputation in Virtual Communities ……………………… 15 1.3 Contributions …………………………………………………………….. 17 1.4 Relevance to Computer Science ……………………………………… 18 1.5 Roadmap ………………………………………………………………….. 18 CHAPTER 2 NOTIONS OF REPUTATION 2.1 Introduction ………………………………………………………………. 20 2.2 Background ………………………………………………………………. 21 2.2.1 Reputation Reporting System ………………………………………… 21 2.2.2 Economics ……………………………………………………………… 22 4 2.2.3 Scientometrics …………………………………………………………. 22 2.2.4 Computer Science …………………………………………………….. 23 2.2.5 Evolutionary Biology …………………………………………………… 24 2.2.6 Anthropology …………………………………………………………… 24 2.2.7 Sociology ……………………………………………………………….. 25 2.3 Reputation Typology ……………………………………………………. 25 2.3.1 Contextualization ………………………………………………………. 25 2.3.2 Personalization …………………………………………………………. 26 2.3.3 Individual and Group Reputation …………………………………….. 27 2.3.4 Direct and Indirect (individual) Reputation …………………………… 27 2.3.5 Direct Reputation ………………………………………………………. 28 2.3.5.1 Observed Reputation …………………………………………… 28 2.3.5.2 Encounter-derived Reputation ………………………………….. 28 2.3.6 Indirect Reputations …………………………………………………… 29 2.3.6.1 Prior-derived reputation ………………………………………… 29 2.3.6.2 Group-derived Reputation ……………………………………… 29 2.3.6.3 Propagated Reputation ………………………………………...... 30 2.4 Discussions ………………………………………………………………. 30 CHAPTER 3 ONLINE RATING SYSTEMS 3.1 Rating Systems: Trust and Reputation Inference ………………… 31 3.2 Rating Systems: Background ………………………………………… 32 3.3 Formalizing the Rating Process ……………………………………… 34 3.3.1 The Rating Model ……………………………………………………… 34 3.3.1.1 Uniform Context Environment …………………………………………. 35 3.3.1.2 Multiple Contexts Environment ………………………………………… 35 3.3.2 Multi-context Reputation ……………………………………………... 36 3.3.3 Reputation Learning ………………………………………………….. 36 3.3.4 Indirect Inference by Rating Propagation …………………………… 36 3.4 Centrality-based Rating ………………………………………………… 38 3.5 Preference-based Rating ………………………………………………. 39 3.5.1 Binary Pair-wise Ratings ………………………………………………. 39 3.5.2 Continuous Pair-wise Ratings ………………………………………… 40 3.5.3 Ratings Propagation …………………………………………………… 41 5 3.6 Bayesian Estimate Rating ……………………………………………… 42 3.6.1 Delegation of Approval: a Bayesian Inference ……………………… 42 3.6.2 Complete Strangers: Prior Assumptions …………………………….. 44 3.6.3 Known Strangers: Rating Propagation Function …………………… 44 3.6.4 Inference Propagation ………………………………………………… 45 3.7 Prelude to Experiments ………………………………………………… 45 CHAPTER 4 RATING EXPERIMENTS 4.1 Experimental Framework ………………………………………………. 46 4.1.1 The Simulation System ……………………………………………….. 46 4.1.2 User Specification ……………………………………………………… 47 4.1.3 Resource Specification ………………………………………………... 47 4.1.4 Simulation Engine ……………………………………………………… 47 4.1.5 Analysis Package ………………………………………………………. 48 4.1.6 Error Measure for Analysis …………………………………………… 48 4.2 Restaurant Rating Simulation ………………………………………… 49 4.2.1 Level of Approval ………………………………………………………. 49 4.2.1.1 Threshold Algorithm …………………………………………… 49 4.2.1.2 Agreement Likelihood Algorithm ………………………………. 50 4.2.2 Rating Propagation Algorithms ………………………………………. 50 4.2.3 Multiple Paths and Loops ……………………………………………... 51 4.3 Movie Rating Experiments …………………………………………….. 52 4.4 Experimental Results …………………………………………………… 53 4.4.1 Rating Propagation: Network Density Variation ……………………. 53 4.4.2 Rating Propagation: Network Size Variation ………………………… 57 4.4.3 Rating Propagation: Sampling Size Variation ……………………… 60 4.4.4 Multiple Paths …………………………………………………………... 63 4.4.4.1 Multiple Paths in Movie Ratings ………………………………………. 64 4.4.4.2 Restaurant Bayesian Estimate Propagation: Multiple Paths ……….. 66 4.4.4.3 Restaurant Preference-based Propagation: Multiple Paths …………. 67 4.5 Conclusion and Discussions ………………………………………….. 69 CHAPTER 5 A COMPUTATIONAL MODEL OF TRUST AND REPUTATION 5.1 Model Rationales ………………………………………………………… 72 6 5.1.1 Reciprocity ………………………………………………………………. 74 5.1.2 Reputation ………………………………………………………………. 74 5.1.3 Trust ……………………………………………………………………… 75 5.2 Notations ………………………………………………………………….. 75 5.3 Computational Models ………………………………………………….. 76 5.3.1 Complete Stranger Prior Assumption ………………………………… 78 5.3.2 Mechanisms for Inferring Reputation ………………………………… 78 5.3.2.1 Parallel Network of Acquaintances …………………………………. 78 5.3.2.2 Generalized Network of Acquaintances ……………………………. 80 5.4 Discussions ………………………………………………………………. 82 5.4.1 The Ghandi or Christ Question ……………………………………….. 82 5.4.2 The Einstein Problem ………………………………………………….. 82 5.5 Conclusion …………………………………………………………………
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