  It is common in online markets for agents to learn from other's actions. Such
observational learning can lead to herding or information cascades in which
agents eventually "follow the crowd". Models for such cascades have been well
studied for Bayes-rational agents that choose pay-off optimal actions. In this
paper, we additionally consider the presence of fake agents that seek to
influence other agents into taking one particular action. To that end, these
agents take a fixed action in order to influence the subsequent agents towards
their preferred action. We characterize how the fraction of such fake agents
impacts behavior of the remaining agents and show that in certain scenarios, an
increase in the fraction of fake agents in fact reduces the chances of their
preferred outcome.
