Adaptive trust modeling in multi-agent systems: utilizing experience and reputation
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Trust among individuals is essential for transactions. A human or software agent in need of resources may reduce transaction risk by modeling the trustworthiness of potential partners. Experience- and reputation-based trust models have unique advantages and disadvantages depending on environment factors, including availability of experience opportunities, trustee trustworthiness dynamics, reputation accuracy, and reputation cost. This research identifies how trusters may utilize both experience- and reputation-based trust modeling to achieve more accurate decision-making tools than using either modeling technique alone. The research produces: 1) the Adaptive Trust Modeling technique for combining experience- vs. reputation-based models to produce the most accurate aggregated model possible, 2) a quantitative analysis of the tradeoffs between experience- and reputation-based models to determine conditions under which each type of model is favorable, and 3) an Adaptive Cost Selection algorithm for assessing the value of trust information given acquisition costs. Experiments show that Adaptive Trust Modeling yields an aggregate trust model more accurate than either experience- or reputation-based modeling alone, and Adaptive Cost Selection acquires the optimal combination of trust information, maximizing a truster's transaction payoff while minimizing trust information costs. These tools enable humans and software agents to make effective trust-based decisions given dynamic system conditions.