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TATM: A Trust Mechanism for Social Traders in Double Auctions

  • Jacob Dumesny
  • Tim Miller
  • Michael Kirley
  • Liz Sonenberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7106)

Abstract

Traders that operate in markets with multiple competing marketplaces can use learning to choose in which marketplace they will trade, and how much they will shout in that marketplace. If traders are able to share information with each other about their shout price and market choice over a social network, they can trend towards the market equilibrium more quickly, leading to higher profits for individual traders, and a more efficient market overall. However, if some traders share false information, profit and market efficiency can suffer as a result of traders acting on incorrect information. We present the Trading Agent Trust Model (TATM) that individual traders employ to detect deceptive traders and mitigate their influence on the individual’s actions. Using the JCAT double-auction simulator, we assess TATM by performing an experimental evaluation of traders sharing information about their actions over a social network in the presence of deceptive traders. Results indicate that TATM is effective at mitigating traders sharing false information, and can increase the profit of TATM traders relative to non-TATM traders.

Keywords

Trust Model Multiagent System False Information Double Auction Trust Mechanism 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Castelfranchi, C., Falcone, R.: Trust Theory: A Socio-Cognitive and Computational Model. John Wiley & Sons, Ltd. (2010)Google Scholar
  2. 2.
    Elsenbroich, C.: Review of Trust Theory: A socio-cognitive and computational model: Castelfranchi, Cristiano and Falcone, Rino. Journal of Artificial Societies and Social Simulation 14(2) (2011)Google Scholar
  3. 3.
    Friedman, D.: The double auction institution: A survey. In: Friedman, D., Rust, J. (eds.) The Double Auction Market: Institutions, Theories and Evidence, ch. 1, pp. 3–25 (1993)Google Scholar
  4. 4.
    Huynh, T.D., Jennings, N.R., Shadbolt, N.R.: An integrated trust and reputation model for open multi-agent systems. Autonomous Agents and Multi-Agent Systems 13(2), 119–154 (2006)CrossRefGoogle Scholar
  5. 5.
    Marsh, S.: Formalising Trust as a Computational Concept. PhD thesis, University of Stirling (1994)Google Scholar
  6. 6.
    McKnight, D.H., Chervany, N.L.: Trust and Distrust Definitions: One Bite at a Time. In: Falcone, R., Singh, M., Tan, Y.-H. (eds.) AA-WS 2000. LNCS (LNAI), vol. 2246, pp. 27–54. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. 7.
    Mui, L., Mohtashemi, M., Halberstadt, A.: A computational model of trust and reputation. In: Proceedings of the 35th Annual Hawaii International Conference on System Sciences, pp. 2431–2439. IEEE (2002)Google Scholar
  8. 8.
    Niu, J., Cai, K., Gerding, E., McBurney, P., Parsons, S.: JCAT: A platform for the TAC market design competition. In: Proc. of 7th Int. Conf. on Autonomous Agents and Multiagent Systems, pp. 1649–1650. IFAAMAS (2008)Google Scholar
  9. 9.
    Smith, M.J., Desjardins, M.: Learning to trust in the competence and commitment of agents. Autonomous Agents and Multi-Agent Systems 18(1), 36–82 (2009)CrossRefGoogle Scholar
  10. 10.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction (1998)Google Scholar
  11. 11.
    Walter, F.E., Battiston, S., Schweitzer, F.: A model of a trust-based recommendation system on a social network. Autonomous Agents and Multi-Agent Systems 16(1), 57–74 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jacob Dumesny
    • 1
  • Tim Miller
    • 1
  • Michael Kirley
    • 1
  • Liz Sonenberg
    • 2
  1. 1.Dept. of Computer Science & Software EngineeringUniversity of MelbourneAustralia
  2. 2.Dept. of Information SystemsUniversity of MelbourneAustralia

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