trust as a precursor to belief revision

Belief revision is concerned with incorporating new information into a pre-existing set of beliefs. when the new information comes from another agent, we must first determine if that agent should be trusted. in this paper, we define trust as a pre-processing step before revision. we emphasize that trust in an agent is often restricted to a particular domain of expertise. we demonstrate that this form of trust can be captured by associating a state partition with each agent, then relativizing all reports to this partition before revising. we position the resulting family of trust-sensitive revision operators within the class of selective revision operators of fermé and hansson, and we prove a representation result that characterizes the class of trust-sensitive revision operators in terms of a set of postulates. we also show that trust-sensitive revision is manipulable, in the sense that agents can sometimes have incentive to pass on misleading information. © 2018 ai access foundation. all rights reserved.