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Scientific Programming
Volume 2018, Article ID 7515860, 15 pages
Research Article

Psychology-Inspired Trust Restoration Framework in Distributed Multiagent Systems

1School of Software Technology, Dalian University of Technology, Dalian, Liaoning 116621, China
2Department of Business Administration, Faculty of Management Sciences, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand

Correspondence should be addressed to Ruchdee Binmad;

Received 21 October 2017; Revised 4 February 2018; Accepted 12 February 2018; Published 1 April 2018

Academic Editor: Danilo Pianini

Copyright © 2018 Ruchdee Binmad and Mingchu Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Trust violation during cooperation of autonomous agents in multiagent systems is usually unavoidable and can arise due to a wide number of reasons. From a psychological point of view, the violation of an agent’s trust is a result of one agent (which is a transgressor) expressing a very low weight on the welfare of another agent (which is a victim) by inflicting a high cost for a very small benefit. In order for the victim to make an effective decision about whether to cooperate or punish for the next interaction, a psychological variable called welfare tradeoff ratio (WTR) can be used to upregulate the transgressor’s disposition so that the number of exploitive behaviors that are likely to happen in the future will be decreased. In this paper, we propose computational models of metrics based on the welfare tradeoff ratio along with the way by which multiple metrics can be integrated to provide the final result. Additionally, a number of experiments based on social network analysis are conducted to evaluate the performance of the proposed framework and the results show that by implementing WTR the simulated network is able to deal with different levels of trust violation effectively.