My current research is in mechanism design, learning in games, and decision theory. I split my time between our economic theory and theoretical computer science communities, including Jason Hartline's Online Markets Lab.
Economists predict behavior using a simple identifying assumption: that economic agents act in their own best interest. We tend to define "best interest" narrowly enough to derive useful conclusions from a given model, but broadly enough to include certain behaviors that we see empirically. Along the way, we acknowledge that many of our models assume a level of competence that we ourselves would find difficult to satisfy. But only rarely do we take advantage of the fact that statisticians and computer scientists have spent decades formalizing the ways in which decision problems are difficult, and characterizing the difficulty that state-of-the-art methods can handle.
So: can we leverage theories of complexity to weaken or strengthen our behavioral assumptions in a way that (a) makes our theory more credible, where needed, (b) justifies existing theory, where possible, and (c) modifies our predictions appropriately as a problem becomes more complicated? Can these revised models explain real-world phenomena that are difficult or impossible to express via existing models? My dissertation research answers these questions affirmatively.
In particular, my work uses a three-step methodology to revise classic models in mechanism design and decision theory. It is easy to describe in the abstract (although the execution can be more intricate). Given an existing model:
- Figure out what we're asking agents to do and how real-world experts would accomplish that task.
- Identify the performance benchmarks that real-world experts can guarantee (resp. that are impossible to achieve).
- Assume that agents weakly overperform (resp. strictly underperform) those benchmarks.
For now, I'll leave it at that. Of course, I'll upload the working papers here once they're ready. In the meantime, I may post some related discussions or results in the section below.
I'll occasionally write about topics that I'm interested in, self-contained results from my research, and/or opinions that I'd like to express. See the links below.
- A Path Towards Social Engineering
- Observability Issues in Decision Theory
- An Axiomatic Foundation for Narrow Choice Bracketing
Please feel free to reach out if anything interests you.