19 May 2017, Computational Social Choice Seminar, Umberto Grandi
Classical models of opinion diffusion on social networks are based on simple representations of opinions such as 0-1 decisions or real numbers. Existing diffusion models are therefore not well-suited to deal with complex or qualitative representations of individual opinions such as preferences (e.g., linear or weak orders over a set of alternatives), qualitative beliefs, or binary views over interconnected issues. In this talk I will survey two models of opinion diffusion grounded in techniques from preference and judgment aggregation. Both models are discrete-time iterative processes, where at every step one or more individuals perform an opinion update by aggregating the opinions of their influencers defined by the network. I will present a number of results that investigate the termination of the iterative processes, depending on the topology of the network and on the aggregation procedure used, as well as the properties of the opinion profiles at termination (consensus, "aligned" profiles, ...).