I have a new paper on Bayesian learning. It extends my model of paying for precision to a setting where the unknown state changes over time. This makes the agent keep buying new information as his existing information becomes out of date. I show how his demand for information depends on whether he is myopic or forward-looking, and on the Gaussian process defining how the state evolves.

The paper stems from my research with Anirudh Sankar on how people learn across contexts. Suppose I ask you for advice, and you say “X worked for me.” But will X work for me? We’re different people with different contexts (e.g., physical and social positions). Our outcomes might be different.

Imagine there’s a function mapping contexts to outcomes. If I know this function then I can invert it, taking information generated in your context and porting it into mine. But if I don’t know the function then I can’t invert it, which makes learning from you hard. Anirudh and my research formalizes this idea: the more I know about the function mapping contexts to outcomes, the easier it is to learn across contexts.

Mathematically, learning across contexts is like learning across time: the function mapping contexts to outcomes is like a stochastic process mapping times to states. But contexts, unlike time, can have many dimensions and may not be totally orderable. Contexts are more general, and so models of learning across them can lead to more general insights. I hope to share some of those insights in the future.