However, while this result shows that a population of Bayesian agents interacting with chatbots will move no further forward in their beliefs, the experience of an individual agent will differ. That agent will be receiving repeated samples from the distribution p(d|h∗)p(d|h^{*}). By an analysis analogous to that given above for the p(d|true process)p(d|\text{true process}), the agent’s beliefs will become increasingly concentrated on h∗h^{*} as nn increases. Since h∗h^{*} was selected based only on the original piece of evidence d0d_{0}, this creates an illusion of confirmation without getting the agent any closer to the truth. As a result, the agent is likely to become increasingly confident in an incorrect hypotheses about the underlying process.
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That’s the simplest case—static dispatch that is known at compile time—but higher-level languages introduce dynamic dispatch, where a function call could end up jumping to one of many different locations. A great example of this is Java: