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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Castelfranchi, C., Falcone, R.: Trust Theory: A Socio-Cognitive and Computational Model. John Wiley & Sons, Ltd. (2010)
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)
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)
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)
Marsh, S.: Formalising Trust as a Computational Concept. PhD thesis, University of Stirling (1994)
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)
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)
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)
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)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction (1998)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dumesny, J., Miller, T., Kirley, M., Sonenberg, L. (2011). TATM: A Trust Mechanism for Social Traders in Double Auctions. In: Wang, D., Reynolds, M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science(), vol 7106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25832-9_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-25832-9_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25831-2
Online ISBN: 978-3-642-25832-9
eBook Packages: Computer ScienceComputer Science (R0)