Cognitive Bias-Aware Dissemination Strategies for Opinion Dynamics with External Information Sources
Abstract
The opinions of members of a population are influenced by opinions of their peers, their own internal predispositions, and information from external sources such as the media. Agents might perceive the received information differently due to various cognitive biases. In this paper, we propose a model of opinion evolution that uses prospect theory to represent the perception of information provided by an external source. Using the proposed model, we study the problem of selecting dissemination strategies for the external source to adopt in order to drive the opinions of individuals toward a desired value. As the initial predispositions of agents and functions characterizing agents' perceptions of information disseminated might be unknown to the source, we estimate the unknown terms in the dynamics and find the optimal strategy by leveraging Gaussian process learning. Our simulations on three different widely-used large graph networks demonstrate that the external source can effectively drive a larger fraction of opinions towards a desired value by using a prospect-theory-based dissemination strategies.
