Abstract
Research in models for experience-based trust management has either ignored the problem of modelling and reasoning about dynamically changing principal behaviour, or provided ad hoc solutions to it. Probability theory provides a foundation for addressing this and many other issues in a rigorous and mathematically sound manner. Using Hidden Markov Models to represent principal behaviours, we focus on computational trust frameworks based on the ‘beta’ probability distribution and the principle of exponential decay, and derive a precise analytical formula for the estimation error they induce. This allows potential adopters of beta-based computational trust frameworks and algorithms to better understand the implications of their choice.
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