  Reputation is crucial to enabling human or software agents to select among
alternative providers. Although several effective reputation assessment methods
exist, they typically distil reputation into a numerical representation, with
no accompanying explanation of the rationale behind the assessment. Such
explanations would allow users or clients to make a richer assessment of
providers, and tailor selection according to their preferences and current
context. In this paper, we propose an approach to explain the rationale behind
assessments from quantitative reputation models, by generating arguments that
are combined to form explanations. Our approach adapts, extends and combines
existing approaches for explaining decisions made using multi-attribute
decision models in the context of reputation. We present example argument
templates, and describe how to select their parameters using explanation
algorithms. Our proposal was evaluated by means of a user study, which followed
an existing protocol. Our results give evidence that although explanations
present a subset of the information of trust scores, they are sufficient to
equally evaluate providers recommended based on their trust score. Moreover,
when explanation arguments reveal implicit model information, they are less
persuasive than scores.
