Steering information cascades in a social system by selective rewiring and incentive seeding

Information cascade characterizes the situation where rational agents are overloaded with social observations and can make decisions ignoring their own personal evidence. If the social observations are misleading, the overall system performance can be subsequently compromised. Therefore, it is of interests to design a socially or interactively networked system falling into undesirable or desirable cascades. In this paper, we study the driving factors of steering information cascades in a socially networked system by considering a sequential Bayesian social learning model with arbitrary network topology and utility function. In particular, we analyze the impacts from different random network topologies on the overall system performance such as error probability, fraction of cascades, and total utility. Furthermore, we demonstrate two systematic approaches, selective rewiring and incentive seeding, which can change the information structure observed by agents and hence steer information cascades. Our results show that when agents restructure their observation network and provide proper incentives for cascades in the correct decisions, the overall social system performance can be well-steered.