A Bayesian model of information cascades

TL;DR

This paper presents a Bayesian model of information cascades showing that cascades may not always occur and that incorporating prior information can delay cascade formation, challenging previous assumptions.

Contribution

It introduces a Bayesian framework with priors over unobserved agents' information and a weighted random choice model, differing from prior cascade models.

Findings

Cascades are not inevitable in the new model.

Adding prior information delays cascade onset.

Weighted random choice alters cascade dynamics.

Abstract

An information cascade is a circumstance where agents make decisions in a sequential fashion by following other agents. Bikhchandani et al., predict that once a cascade starts it continues, even if it is wrong, until agents receive an external input such as public information. In an information cascade, even if an agent has its own personal choice, it is always overridden by observation of previous agents' actions. This could mean agents end up in a situation where they may act without valuing their own information. As information cascades can have serious social consequences, it is important to have a good understanding of what causes them. We present a detailed Bayesian model of the information gained by agents when observing the choices of other agents and their own private information. Compared to prior work, we remove the high impact of the first observed agent's action by incorporating a prior probability distribution over the information of unobserved agents and investigate an alternative model of choice to that considered in prior work: weighted random choice. Our results show that, in contrast to Bikhchandani's results, cascades will not necessarily occur and adding prior agents' information will delay the effects of cascades.