Computational Trust in Multiplayer Online Games
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Computational Trust in Multiplayer Online Games A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Muhammad Aurangzeb Ahmad IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy Jaideep Srivastava May, 2012 c Muhammad Aurangzeb Ahmad 2012 ALL RIGHTS RESERVED Acknowledgements There are a number of people and organizations that I would like to thank without whose help the current endeavor would not have been possible. I would like to thank my advisor and mentor Professor Jaideep Srivastava for his guidance in the academic as well as in the non-academic arena. I have learned many vaulable life long lessons while working with Professor Srivastava. I would also like to thank all of my labmates for their collaborative efforts and their support. In particular I would like to thank the following individuals in the lab, in alphabetical order: Zoheb Borbora, David Kuo-Wei Hsu, Xia Hu, Young Ae Kim, Komal Kapoor, Amogh Mahapatra, Nishith Pathak, Kyong Jin Shim and Nisheeth Srivastava. Outside of University of Minnesota, I would like to acknowledge my collaborators at the Virtual Worlds Observatory Project. Firstly I want to acknowledge the faculty in the project Professor Noshir Contractor, Professor Dmitri Williams and Professor Marshall Scott Poole, all of whom were also instrumental in broadening my horizons given the inter-disciplinary nature of this project. Amongst the students in this project I would like to thank the following individuals: David Huffaker, Brian Keegan, Cuihua Shen, Jeffrey Treen and Jing 'Annie' Wang. I would also like to thank the committe members Professor Abhishek Chandra, Professor David Knoke and Professor John Riedl in addition to my adviser for agreeing to be part of the committee. Last but not least I would like to thank my family, my parents (Mushtaq Ahmad Mirza, Khalida Parveen), my brothers (Muhammad Farooq Ahmad, Muhammad Maqsood Ahmed, Muhammad Abubakar Ahmad), my sister in laws (Zoonaz Murad, Rabia Haneef, Shawana Afzal) for the love and support all these years. i Dedication This thesis is dedicated to my parents: My father, Mushtaq Ahmad Mirza who taught me the meaning of patience without ever uttering a single word about patience and my mother Khalida Parveen who is the person who not just gave me more love than any person in the universe but also more than what any person can in the universe. ii - Waris Shah 1 (1722-1798) 1 Hazrat Waris Shah is considered to be the greatest poet of the Punjabi langauge. His Magnus Opus Heer Ranjha is considered to be the crowning achievement of the Punjabi literature. iii Abstract Trust is a ubiquitous phenomenon in human societies. Computational trust refers to the mediation of trust via a computational infrastructure. It has been studied in a variety of contexts e.g., peer-to-peer systems, multi-agent systems, recommendation systems etc. While this is an active area of research, the types of questions that have been explored in this field has been limited mainly because of limitations in the types of datasets which are available to researchers. In this thesis questions related to trust in complex social environments represented by Massively Multiplayer Online Games (MMOGs) are explored. The main emphasis is that trust is a multi-level phenomenon both in terms of how it operates at multiple levels of network granularities and how trust relates to other social phenomenon like homophily, expertise, mentoring, clandestine behaviors etc. Social contexts and social environments affect not just the qualitative aspects of trust but this phenomenon is also manifested with respect to the network and structural signatures of trust network Additionally trust is also explored in the context of predictive tasks: Previously described prediction tasks like link prediction are studied in the context of trust within the context of the link prediction family of problems: Link formation, link breakage, change in links etc. Additionally we define and explore new trust-related prediction problems i.e., trust propensity prediction, trust prediction across networks which can be generalized to the inter-network link prediction problem and success prediction based on using network measures of a person's social capital as a proxy. iv Contents Acknowledgements i Dedication ii Abstract iv List of Tables xi List of Figures xiv 1 Introduction 1 1.1 Introduction . .1 1.2 Representing Trust . .2 1.3 Trust as a Network Phenomenon . .3 1.3.1 Trust as a Multi-Level Phenomenon . .5 1.3.2 Trust-related Concepts in Network Terms . .7 1.3.3 Trust at Multiple-Temporal Resolutions . .8 1.4 Virtual Worlds as Testbeds for a Multi-Level Exploration of Trust . .9 1.4.1 Psychological Factors . 11 1.4.2 Trust between two People (Dyadic Trust) . 11 1.4.3 Trust in Triads . 11 1.4.4 Intra-Group Trust . 12 1.4.5 Inter-Group Trust . 12 1.4.6 Trust as a Global Network Phenomenon . 12 1.5 Modeling Issues . 13 v 1.6 An MTML Theory of Trust? . 14 1.7 Conclusion . 14 2 Trust and Socialization in MMOs 16 2.1 Introduction . 16 2.2 Related Work . 17 2.3 Dataset . 18 2.4 Temporal Characteristics of Trust Networks in MMOGs . 20 2.5 Trust in Social Networks: Adversarial vs. Cooperative Settings . 20 2.5.1 ERGM/p* Models . 20 2.5.2 ERGM/p* Models for Trust Networks . 21 2.6 Conclusion . 23 3 Trust in Social Contexts 24 3.1 Introduction . 24 3.2 Related Work . 25 3.3 Network Datasets . 26 3.3.1 Trust Based Social Networks . 27 3.3.2 Additional Networks . 27 3.4 Exchange in Trust Networks . 28 3.4.1 Specialized Exchange in Trust networks . 29 3.5 Network Motifs . 32 3.6 Exchange in Trust Networks with Birth and Death of Nodes . 34 3.7 Conclusion . 37 4 Trust, Expertise and Homophily 38 4.1 Introduction . 38 4.2 Dataset . 41 4.3 Trust And Homophily in MMOs . 44 4.4 Conclusion . 50 5 Trust and Clandestine Behaviors 51 5.1 Introduction . 52 vi 5.2 Related Work . 53 5.3 Background . 54 5.3.1 Game Mechanics . 54 5.3.2 Gold Farming . 54 5.4 Dataset . 56 5.5 Methods . 57 5.5.1 Phase I: Deductive logit model . 58 5.5.2 Phase II: Inductive machine learning models . 59 5.6 Results . 62 5.6.1 Phase I: Deductive logit model . 62 5.6.2 B. Phase II: Inductive machine learning models . 63 5.7 Classifier Selection . 67 5.8 Conclusion . 69 5.9 Introduction . 71 5.10 Motivation and Background . 72 5.11 Housing Permissions as Trust in EverQuest II . 72 5.12 Dataset . 77 5.13 Analysis of the Housing-Trust Network . 78 5.14 Discussion . 83 5.15 Introduction . 85 5.16 Related Work . 86 5.17 Legal vs. Illicit Trade Activity in MMOGs . 87 5.18 Clandestine Social Networks & Illicit Trade in MMOGs . 89 5.18.1 Item Projection Networks . 89 5.18.2 Frequent pattern mining analysis . 92 5.18.3 Frequent-Networks of Contraband in MMOGs . 94 5.19 Contraband Based Prediction in Clandestine Networks . 97 5.19.1 Datset . 97 5.19.2 Model Descriptions . 97 5.19.3 Experiments and Results . 98 5.20 Summary . 100 5.21 Conclusion and Future Work . 100 vii 6 Trust and Mentoring 102 6.1 Introduction . 103 6.2 Exchange as a Basis for Network Generation . 104 6.3 Data Description and Observations . 106 6.4 Graph Laws in the Mentoring Network . 108 6.5 GTPA Graph Generative Model . 109 6.6 Properties . 111 6.7 Experiments . 112 6.8 Conclusion . 113 6.9 Introduction . 115 6.10 Related Work . 115 6.11 Mentoring in EverQuest II . 116 6.12 Mentoring Clusters . 119 6.12.1 Social Characteristics of Mentors . 120 6.12.2 Behavioral Signatures of Mentors . 123 6.12.3 Life Cycle of Mentorship . 124 6.13 A Network Model of Mentoring . 125 6.14 Discussion . 127 7 Trust Prediction Family of Problems 129 7.1 Introduction . 130 7.2 Related Work . 131 7.3 Trust Prediction Problems . 132 7.3.1 Trust Edge Formation Prediction . 132 7.3.2 Trust Change Prediction . 133 7.3.3 Trust Breakage Prediction . 133.