  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.
