Expected values vs uncertainty Most models I have seen use expected values. Why is this a better economic model than uncertainty and economic agents thus having to make 'best guesses', with the result of 'animal spirits' playing a large role. For example, it seems like no one knows where bitcoin would head for sure, hence the rife animal spirits-eqsue speculation involved and variable price.
One can distinguish between probabilistic randomness (which can be quantified) and uncertainty. This article by a Fed Researcher discusses this: “The Stock Market: Beyond Risk Lies Uncertainty” (Frank Schmid). As the tag to the question suggests, this is associated with Keynes, and post-Keynesian economics at present. Therefore, if you look at the post-Keynesian literature, you will see more discussion of uncertainty.

“Mainstream” economics and finance focus on quantifiable models. As a result, they rely on probability theory, and expected values are very common. However, option pricing theory does take into account the whole probability distribution, and so “uncertainty” might be thought of as the properties of the tail of the distribution. Rather than describing the price of bitcoin as uncertain, the tendency would be to find a model (such as a “rational bubble”) that might fit observed data. That is, one can argue that it is possible to analyse bitcoin using a standard probabilistic framework.

Whether the probabilistic approach or the more qualitative post-Keynesian approach is “better” is going to be opinion-based, and so I do not want to pursue that. (As a disclaimer, I am in the post-Keynesian camp.)