Stereotypical Trust and Bias in Dynamic Multiagent Systems

Christopher Burnett, TIMOTHY J. NORMAN and Katia Sycara
Journal Article, ACM Transactions in Intelligent Systems and Technology, DOI: 10.1145/2438653.2438661, Vol. 4, No. 2, January, 2013

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Large-scale multiagent systems have the potential to be highly dynamic. Trust and reputation are crucial concepts in these environments, as it may be necessary for agents to rely on their peers to perform as expected, and learn to avoid untrustworthy partners. However, aspects of highly dynamic systems introduce issues which make the formation of trust relationships difficult. For example, they may be short-lived, precluding agents from gaining the necessary experiences to make an accurate trust evaluation. This article describes a new approach, inspired by theories of human organizational behavior, whereby agents generalize their experiences with previously encountered partners as stereotypes, based on the observable features of those partners and their behaviors. Subsequently, these stereotypes are applied when evaluating new and unknown partners. Furthermore, these stereotypical opinions can be communicated within the society, resulting in the notion of stereotypical reputation. We show how this approach can complement existing state-of-the-art trust models, and enhance the confidence in the evaluations that can be made about trustees when direct and reputational information is lacking or limited. Furthermore, we show how a stereotyping approach can help agents detect unwanted biases in the reputational opinions they receive from others in the society.

author = {Christopher Burnett and TIMOTHY J. NORMAN and Katia Sycara},
title = {Stereotypical Trust and Bias in Dynamic Multiagent Systems},
journal = {ACM Transactions in Intelligent Systems and Technology, DOI: 10.1145/2438653.2438661},
year = {2013},
month = {January},
volume = {4},
number = {2},
} 2017-09-13T10:39:30-04:00