Via my ERC starting grant, it became possible to open a new road of investigation in which we could apply our logical systems for belief revision to the analysis of information flow in social contexts. In the paper on 'logical models of informational cascades' (2013), we analyze the decision processes of individuals that lead to the social herding phenomenon known as informational cascades. The question we address in this paper deals with whether rational agents who use their higher-order reasoning powers and who can reflect on the fact that they are part of an informational cascade, can ultimately stop the cascade from happening. To answer this question we use dynamic epistemic logic to give a complete analysis of the information flow in an informational cascade, capturing the agent's observations, their communication and their higher-order reasoning power. Our models provide the surprising answer that individual rationality isn't always a cure that can help us to stop a cascade. However, other factors that deal with the underlying communication protocol or that focus on the reliability of agents in the group, give rise to conditions that can be imposed to prevent or stop an informational cascade from happening in certain scenarios. This paper provides a first detailed analysis of the entire reasoning process that happens in specific cascade scenarios and can help give society the required insight on how to stop them from happening.