My job market paper examines a scenario where a decision-maker trying to learn about an underlying state of the world must decide not only how much information to purchase, but also from which sources. I develop an approximate consumer theory for information, valid when information purchases are large and the relevant probabilities are well-described by large-deviations theory.

In this project, we generalize some of the results from my job market paper to a Bayesian estimation setting and consider implications for optimal experiment design. We show that the optimal design at large samples generally maximizes the Fisher information per dollar for the worst-case true value of the parameter of interest.

Optimal fast experimentation, with Lones Smith
Bargaining with multiple buyers: evidence from eBay

Like most grad students entering the job market this year, I’ve had to wrestle with the transition to giving talks remotely. Personally, I have some trouble focusing on virtual seminars since the format tends to be fairly static: mostly still slides with perhaps a camera window to the side. To avoid this, I’ve spent a fair amount of time (read: procrastinating) trying to put together a setup that allows me to move around and interact with my materials a bit more. Here, I outline an approach that’s worked reasonably well for me.