skip to main content
article

Review on Computational Trust and Reputation Models

Published: 01 September 2005 Publication History

Abstract

The scientific research in the area of computational mechanisms for trust and reputation in virtual societies is a recent discipline oriented to increase the reliability and performance of electronic communities. Computer science has moved from the paradigm of isolated machines to the paradigm of networks and distributed computing. Likewise, artificial intelligence is quickly moving from the paradigm of isolated and non-situated intelligence to the paradigm of situated, social and collective intelligence. The new paradigm of the so called intelligent or autonomous agents and multi-agent systems (MAS) together with the spectacular emergence of the information society technologies (specially reflected by the popularization of electronic commerce) are responsible for the increasing interest on trust and reputation mechanisms applied to electronic societies. This review wants to offer a panoramic view on current computational trust and reputation models.

References

[1]
Abdul-Rahman, A. & Hailes, S. (2000). Supporting Trust in Virtual Communities. In: Proceedings of the Hawaii's International Conference on Systems Sciences, Maui, Hawaii.
[2]
Amazon (2002). Amazon Auctions. http://auctions.amazon.com.
[3]
Bacharach, M. & Gambetta, D. (2001). Trust in Society, Chapt. Trust in signs. Russell Sage Foundation.
[4]
Barber, K. S. & Kim, J. (2001). Belief Revision Process based on Trust: Simulation Experiment. In: Proceedings of the Fourth Workshop on Deception, Fraud and Trust in Agent Societies, Montreal, Canada, pp. 1-12.
[5]
Bromley, D. B. (1993). Reputation, Image and Impression Management. John Wiley & Sons.
[6]
Buskens, V. (1998). The Social Structure of Trust. Social Networks (20): 265-298.
[7]
Carbo, J., Molina, J. & Davila, J. (2002). Comparing Predictions of SPORAS vs. a Fuzzy Reputation Agent System. In: Third International Conference on Fuzzy Sets and Fuzzy Systems, Interlaken, pp. 147-153.
[8]
Carter, J., Bitting, E. & Ghorbani, A. (2002). Reputation Formalization for an Information-Sharing Multi-Agent Sytem. Computational Intelligence 18(2): 515-534.
[9]
Castelfranchi, C. & Falcone, R. (1998). Principles of Trust for MAS: Cognitive Anatomy, Social Importance, and Quantification. In: Proceedings of the International Conference on Multi-Agent Systems (ICMAS'98), Paris, France. pp. 72-79.
[10]
Castelfranchi, C. & Tan, Y.-H. (2001). Trust and Deception in Virtual Societies. Kluwer Academic Publishers.
[11]
Celentani, M., Fudenberg, D., Levine, D. K., & Psendorfer, W. (1966). Maintaining a Reputation Against a Long-Lived Opponent. Econometrica 64(3): 691-704.
[12]
Conte, R. & Paolucci, M. (2002). Reputation in Artificial Societies: Social Beliefs for Social Order. Kluwer Academic Publishers.
[13]
Dellarocas, C. (2003). The digitalization of Word-Of-Mouth: Promise and Challenges of Online Reputation Mechanisms. Management Science.
[14]
eBay. (2002). eBay. http://www.eBay.com.
[15]
Esfandiari, B. & Chandrasekharan, S. (2001). On How Agents Make friends: Mechanisms for Trust Acquisition. In: Proceedings of the Fourth Workshop on Deception, Fraud and Trust in Agent Societies, Montreal Canada. pp. 27-34.
[16]
Gambetta, D. (1990). Trust: Making and Breaking Cooperative Relations, Chapt. Can We Trust Trust? Basil Blackwell, Oxford, pp. 213-237.
[17]
Glickman, M. E. (1999). Parameter Estimation in Large Dynamic Paired Comparison Experiments. Applied Statistics (48): 377-394.
[18]
Grandison, T. & Sloman, M. (2000). A survey of trust in Internet application, IEEE, Communications Surveys, Fourth Quarter, 2000.
[19]
Hume, D. (1975). A Treatise of Human Nature (1737). Clarendon Press: Oxford.
[20]
Karlins, M. & Abelson, H. I. (1970). Persuasion, how opinion and attitudes are changed. Crosby Lockwood & Son.
[21]
Lashkari, Y., Metral, M. & Maes, P. (1994). Collaborative Interface Agents. In: Proceedings of the Twelfth National Conference on Artificial Intelligence, AAAI-Press .
[22]
Marimon, R., Nicolini, J. P. & Teles, P. (2000). Competition and Reputation. In: Proceedings of the World Conference Econometric Society, Seattle.
[23]
Marsh, S. (1994). Formalising Trust as a Computational Concept. Ph.D. thesis, Department of Mathematics and Computer Science, University of Stirling.
[24]
McKnight, D. H. & Chervany, N. L. (1996). The meanings of trust. Technical report, University of Minnesota Management Information Systems Research Center.
[25]
McKnight, D. H. & Chervany, N. L. (2002). Notions of Reputation in Multi-Agent Systems: A Review. In: Proceedings of the 34th Hawaii International Conference on System Sciences.
[26]
Montaner, M., Lopez, B. & de la Rosa, J. (2002). Developing Turst in Recommender Agents. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-02), Bologna, Italy. pp. 304-305.
[27]
Mui, L., Halberstadt, A. & Mohtashemi, M. (2002). Notions of Reputation in Multi-Agent Systems: A Review. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-02), Bologna, Italy. pp. 280-287.
[28]
OnSale (2002). OnSale. http://www.onsale.com.
[29]
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.
[30]
Plato (1955). The Republic (370BC). Viking Press.
[31]
Sabater, J. & Sierra, C. (2001). REGRET: A Reputation Model for Gregarious Societies, In: Proceedings of the Fourth Workshop on Deception, Fraud and Trust in Agent Societies, Montreal, Canada. pp. 61-69.
[32]
Sabater, J. & Sierra, C. (2002). Reputation and Social Network Analysis in Multi-Agent Systems. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-02), Bologna, Italy, pp. 475-482.
[33]
Schillo, M., Funk, P. & Rovatsos, M. (2000). Using Trust for Detecting Deceitful Agents in Artificial Societites. Applied Artificial Intelligence (Special Issue on Trust, Deception and Fraud in Agent Societies).
[34]
Scott, J. (2000). Social Network Analysis. SAGE Publications.
[35]
Sen, S. & Sajja, N. (2002). Robustness of Reputation-based Trust: Booblean Case. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-02), Bologna, Italy, pp. 288-293.
[36]
UCI (2003). UCI Machine Learning Repository. http:www.ics.uci.edu/~mlearn/MLRepository.html.
[37]
Yu, B. & Singh, M. P. (2001). Towards a Probabilistic Model of Distributed Reputation Management. In: Proceedings of the Fourth Workshop on Deception, Fraud and Trust in Agent Societies, Montreal, Canada, pp. 125-137.
[38]
Yu, B. & Singh, M. P. (2002a). Distributed Reputation Management for Electronic Commerce. Computational Intelligence 18(4), 535-549.
[39]
Yu, B. & Singh, M. P. (2002b). An Evidential Model of Distributed Reputation Management. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-02), Bologna, Italy, pp. 294-301.
[40]
Zacharia, G. (1999). Collaborative Reputation Mechanisms for Online Communities. Master's thesis, Massachusetts Institute of Technology.

Cited By

View all

Recommendations

Reviews

Marlin W Thomas

The Internet is as much a social construct as it is an engineering construct. In any social construct, issues of trust and reputation underlay confidence in communication, especially commercial communication in the form of monetary exchanges. The authors present a broadly conceived and well-detailed overview of these issues as they relate to artificial intelligence (AI) in a distributed environment, and to electronic commerce. The authors begin by describing the response of AI to electronic commerce in terms of two competing conceptual models: the cognitive and the game theoretical models. They explain the various information sources, such as witness information and sociological information, that are used to calculate trust and reputation. The heart of the paper is the review of a representatively broad selection of computational trust and reputation models. In addition to detailing their salient characteristics, the authors also provide critiques and situate the competing models within the field as a whole. The paper ends with an evaluative summary of the competing approaches, and with some indications of the future direction of research. The authors mange to convey the theoretical outlines of the field of trust and reputation, and to provide some detail regarding individual approaches to the issue. The paper is an extremely useful introduction to the field for novices, and it provides those knowledgeable in the field with a well-reasoned summary of research approaches. Online Computing Reviews Service

Access critical reviews of Computing literature here

Become a reviewer for Computing Reviews.

Comments

Information & Contributors

Information

Published In

cover image Artificial Intelligence Review
Artificial Intelligence Review  Volume 24, Issue 1
September 2005
96 pages

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 September 2005

Author Tags

  1. reputation
  2. trust

Qualifiers

  • Article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 22 Dec 2024

Other Metrics

Citations

Cited By

View all

View Options

View options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media