We present SmartRate, a trust and reputation-based decision framework for Smart Grid, based on the available ratings provided by other customers. This model considers multiple trust factors associated with the broker and the preferences of customers for each of these factors. We define a decision framework for broker selection based on multi-attribute utility function and show how learning customers' rating behaviors helps to increase a decision maker's utility. We evaluate this framework by simulating a market based on real-world data. Our results show that learning the characteristics of a rating population helps to interpret and personalize the ratings, which results in better decision making and an increase in customer satisfaction.

My thesis contributes to the field of multi-agent systems by proposing a novel trust-based decision model for supply chain management.

Many real-world applications, such as Supply Chain Management (SCM), can be modeled using multi-agent systems. One shortcoming of current SCM models is that their trust models are ad hoc and do not have a strong theoretical basis. We propose a trust model for SCM that is grounded in probabilistic game theory. In this model, trust can be gained through direct interactions, and/or by asking for information from other trustworthy agents. We will use this model to simulate and study supply chain market behavior.