REGRESSION-BASED SOCIAL INFLUENCE NETWORKS AND THE LINEARITY OF AGGREGATED BELIEF

Consider an agent-based social influence (belief adoption) network where agents share beliefs with neighbors using a linear regression model. One relevant question is: can aggregated, system-level belief also be fit by a linear regression model? Earlier work demonstrated several scenarios where system-level linearity of belief holds. This paper extends that research, varying model and simulation factors through experimental design. When linearity does not hold, we isolate the responsible factors. Finally, we investigate whether system-level linearity is as an absorbing state, that is, when system-level linearity is present at some time t, it continues to hold for all later times.