(I wrote this during the workshop a few weeks ago, and just realized that I never actually hit “publish.” Better late then never, I guess!)
Every year in the fall, all the folks in southern California interested in the intersection of economics and engineering/computer science get together and have a two-day workshop that we call NEGT for “Network Economics and Game Theory.” Hosting duties rotate between USC, UCLA, and Caltech, and this year it was our job. The workshop is just wrapping up and, thanks to our amazing admin Sydney Garstang, everything went wonderfully!
There were lots of great talks, and the slides will eventually start to show up here. Of the many highlights, our two external speakers both gave really great talks. Our first keynote, Tim Roughgarden, gave a great overview of recent results in the area of approximate mechanism design. This is a direction that many folks in the Algorithmic Game Theory community have been pushing on in a while, but Tim showed some very interesting new results. Plus, it is always interesting to see how economists react to this direction, which is very different than the traditional viewpoint. Our second keynote, Markus Mobius, gave a really interesting empirical take on the power of social learning. He showed results from an experiment involving Harvard undergraduates performing a task that required social learning and was able to test various conjectures for how such learning occurs (as well as the magnitude of social learning that occurs). Given the huge focus in CS on models where we learn from our friends, it was quite interesting to see that the magnitude of such social learning is actually pretty small, and seems to occur only in vary specific ways.