Decision Theoretic Assessment Model for Online Business Games

##plugins.themes.academic_pro.article.main##

Sanat Kumar Bista
Keshav Dahal
Peter Cowling

Abstract

Several approaches to reputation and trustworthiness assessment based on probabilistic assessment have considered studying its usefulness in online business environments. It is seen that probabilistic models of reputation and trustworthiness assessment aids in resolving uncertainty by assessing the trustworthiness level of players. In this paper we intend to further enhance the assessment by combining the probabilistic assessment part with the expected utility of each player, thus resulting in a decision theoretic assessment. In this form of assessment, a player makes a decision on the basis of what it believes (given by the probabilistic assessment) and what it wants (given by the utility value of the choice). This method ensures that the players make a continuous measure of the state quality. The assessment of trustworthiness in this model is guided by the principle of maximum expected utility, which enforces a rational player to choose an action only if that meets its expected utility. Our results show that the decision theoretic models of assessment positively contribute the evolution of cooperation in a player society. Experiments have been carried out in a business game environment based on the principles of Iterated Prisoner's Dilemma.

##plugins.themes.academic_pro.article.details##

How to Cite
Sanat Kumar Bista, Keshav Dahal, & Peter Cowling. (2012). Decision Theoretic Assessment Model for Online Business Games. International Journal of Next-Generation Computing, 3(1), 87–104. https://doi.org/10.47164/ijngc.v3i1.28

References

  1. Aberer, K. and D. Zoran 2006. The Complex Facets of Reputation and Trust. 9th Fuzzy Days, International conference on computational intelligence Fuzzy Logic Neural Networks and Evolutionary Algorithms, Dortmund, Germany.
  2. Aljazzaf, Z. M.and M. Perry 2010. Trust in Web Services. Services (SERVICES-1), 2010 6th World Congress on.
  3. Avinash, L. V. and Ashwin S. 2008. A decision theoretic framework for analyzing binary hash-based content identi cation systems. Proceedings of the 8th ACM workshop on Digital rights management. Alexandria, Virginia, USA, ACM.
  4. Axelrod, R. and Ed. 1984. The Evolution of Cooperation, Basic Books, New York.
  5. Axelrod, R.and Ed. 1987. The Evolution of Strategies in the Iterated Prisoner's Dilemma. Genetic Algorithms and Simulated Annealing. San Francisco, CA, USA, Morgan Kaufmann Publishers Inc.
  6. Ba, S. and A. B. Whintson 2003. Building Trust in online auction markets through an economic incentive mechanism." Decision Support Systems 35: 273-286.
  7. Berger, J. O. 1985. Statistical Decision Theory and Bayesian Analysis. New York, Springer-Verlag New York Inc.
  8. Bianconi, M. 2003. Financial economics, risk and information: an introduction to methods and models Singapore, World Scienti c Publishing Co.Pte Ltd.
  9. Bista, S. K.and K. P. Dahal et al. 2008. Unravelling the Evolution of Defectors in online Business Games. 2nd International Conference on Software Knowledge and Information Management (SKIMA) Kathmandu, SKIMA.
  10. Blythe, J. 1999. Decision Theoretic Planning." AI magazine 20(2): 37-54.
  11. Chaudhuri, A. and B. Sopher, et al. 2002. Cooperation in Social Dilemmas, trust and reciprocity." Journal of Economic Psychology 23: 231-249.
  12. Chong, S. Y. and X. Yao 2007. Multiple choices and Reputation in Multiagent Interactions." IEEE Transactions on Evolutionary Computation 11(6): 689-710.
  13. Cohen, W. W. and R. E. Schapire, et al. 1998. Learning to order things." Advances in Neural Information Processing, Morgan Kaufmann 10.
  14. Daniel Olmedilla , R. L.,Axel Polleres and Holger Lausen 2004. Trust Negotiation for Semantic Web Services. Semantic Web Services and Web Process Composition-SWSWPC. A. P. S. Jorge Cardoso. San Diego, CA, USA.
  15. Dragoni, N. 2010. A Survey on Trust-Based Web Service Provision Approaches. Dependability (DEPEND), 2010 Third International Conference on.
  16. Errity, A. 2003. Evolving Strategies for Prisoner's dilemma. Dublin, Dublin City University.
  17. Frean, M. R. and E. R. Abraham 2001. A Voter Model of the Spatial Prisoner's Dilemma " IEEE Transactions on Evolutionary Computation 5(2): 117-121.
  18. Friedman, M. and L. Savage 1948. The Utility Analysis of Choices Involving Risk." The Journal of Political Economy 56(4): 279-304.
  19. Goldbeck, J.2002. Evolving Strategies for the Prisoner's Dilemma. Advances in Intelligent Systems, Fuzzy Systems, and Evolutionary Computation: 299-306.
  20. Goldberg, D. E. 1989. Genetic Algorithms in search, Optimization and Machine Learning, Pearson Education Inc.
  21. Hansson, S. O.1994. Decision theory a brief Introduction. Risk Analysis Course Material. Stockholm, Department ot Philosophy and the History of Technology, Royal Institute of Technology(KTH).
  22. Holland, J.1975. Adaptation in Natural and Arti cial Systems. Ann Arbor, University of Michigan Press.
  23. Horvitz, E. J.and J. S. Breese, et al.1988. Decision Theory in Expert Systems and Arti cial Intelligence." International Journal of Approximate Reasoning 2(Special Issue on uncertainity in AI): 247-302.
  24. Howley, E.and C. o. Riordan2007. The e ects of viscosity in choice and refusal IPD environments " Arti cial Intelligence Review Springer Netherlands 26(1-2): 103-114.
  25. Janssen, M.2006. Evolution of Cooperation when feedback to reputation scores is voluntary." Journal of Arti cial Scocieties and Social Simulation 9(1): 17.
  26. Jordaan, I. J.2005. The Concept of Utility. Decisions under uncertainity: probabilistic analysis for engineering decisions. Cambridge, Cambridge Univer: 162-219.
  27. Jurca, R.and B. Flatings2004. An Incentive Compatible Reputation Mechanism. IEEE Conference on Electronic Commerce, CA, USA, IEEE Computer Society.
  28. Kahneman, D.and A. Tversky1979. Prospect Theory: An Analysis of Decision Under Risk." Econometrica 47(2): 263-292.
  29. Kamvar, S. D.and M. T. Scholsser, et al.2003. The Eigen Trust Algorithm for Reputation Management in P2P Networks. ACM WWW2003, Budapest, Hungary, ACM.
  30. Kaneko, M.and M. H. Wooders2004. Handbook of Utility Theory Volume 2. Dordrecht, The Netherlands, Kluwer Academic Publishers.
  31. Kritzman, M.1998. Risk and utility:Basics. Investment Management. P. L. Bernstein and A. Damodaran. New York, John Wiley and Sons: 27-99.
  32. Kuhn, S.2007. Prisoner's Dilemma." The Stanford Encyclopedia of Philosophy Winter 2007 Edition, from http:plato.stanford.eduarchiveswin2007entriesprisoner-dilemma.
  33. Li, J.-w.2004. What Determines a Game to be Cooperative or Non-Cooperative?" SSRN eLibrary.
  34. Malik, Z.and A. Bouguettaya2009. Reputation Bootstrapping for Trust Establishment among Web Services." Internet Computing, IEEE 13(1): 40-47.
  35. Masuda, N.and K. Aihara2003. patial prisoner's dilemma optimally played in small-world networks." Elsevier Physics Letters A(313): 55-61.
  36. Nepal, S.and Z. Malik, et al.2011. Reputation Management for Composite Services in Service-Oriented Systems." International journal of web services research 8(2): 29-52.
  37. Parsons, S.and M. Wooldridge2002. Game Theory and Decision Theory in Multi-Agent Systems." Autonomous Agents and Multi-Agent Systems 5(3): 243-254.
  38. Poli, R.and W. B. Langdon1997. Genetic Programming with one-Point Crossover. Second On-Line World Conference on Soft Computing in Engineering Design and Manufacturing, Springer-Verlag London.
  39. Riegelsberger, J.and M. A. Sasse, et al.2005. The mechanics of trust: A framework for research and design." International Journal of Human-Computer Studies 62: 381-422.
  40. Ross, D.2009. Game Theory." The Stanford Encyclopedia of Philosophy (Fall 2009 Edition). Retrieved 2nd October 2009, 2009, from http:plato.stanford.eduarchivesfall2009entriesgame-theory.
  41. Russell, S. J.and P. Norvig 1998. Arti cial intelligence : a modern approach. Upper Saddle River, N.J., Prentice Hall.
  42. Russell, S. J.and P. Norvig 2010. Arti cial intelligence : a modern approach. Upper Saddle River, N.J., Prentice Hall. and (2006).
  43. Weisstein, E. W.2009. Fair Game." MathWorld{A Wolfram Web Resource. Retrieved July 7 2009, 2009, from http:mathworld.wolfram.comFairGame.html
  44. West, A. G.and J. Chang, et al.2011. Trust in collaborative web applications." Future Generation Computer Systems(0).
  45. Xiong, L.and L. Liu 2004. PeerTrust: Supporting Reputation Based Trust for Peer-to-Peer electronic Communities." IEEE Transactions on Knowledge and Data Engineering 16(7): 843-857.
  46. Ye Diana, W.and F. Guisseppi2006. A decision-theoretic approach to the evaluation of information retrieval systems." Inf. Process. Manage. 42(4): 863-874.
  47. Yu, B.and M. P. Singh2000. A Social Mechanism of Reputation Management in Electronic Communitoes. 4th International Workshop on Cooperative Information Agents(CIA) Berlin, Springer-Verlag.
  48. Yu, B.and M. P. Singh2002. Search in Referral Networks. AAMAS Workshop on Regulated Agent-Based Social Systems: Theories and Applications, 2002, Bologna, Italy.
  49. Zhou, R.and K. Hwang2007. PowerTrust: A Robust and Scalable Reputation System for Trusted Peer-to-Peer Computing." IEEE Transactions on Parallel and Distributed Systems 18(4): 460-473.
  50. Zoeter, O.and M. Taylor, et al.2008. A Decision Theoretic Framework for Ranking using Implicit Feedback. SIGIR 2008 Workshop on Learning to Rank for Information Retrieval. Singapore.