Social contagion induced by uncertain information

Abstract: Information and individual activities often spread globally through the network of social ties. While social contagion phenomena have been extensively studied within the framework of threshold models, it is common to make an assumption that may be violated in reality: each individual is able to observe the neighbors' states without error. Here, we analyze the dynamics of global cascades under uncertainty in an otherwise standard threshold model. Each individual uses statistical inference to estimate the probability distribution of the number of active neighbors when deciding whether to be active, which gives a probabilistic threshold rule. Unlike the deterministic threshold model, the spreading process is generally non-monotonic as the inferred distribution of neighbors' states may be updated as a new signal arrives. We find that social contagion may arise as a self-fulfilling event in that misperception may trigger a cascade in regions where cascades would never occur under certainty.