AI and Economics have many commonalities, notably sharing a focus on utilitarian models of rational agency and relying upon the statistical analysis of data. Much work at the intersection between the two disciplines focuses on cases where rational agents interact, asking questions about how such agents reason about each other, what behaviors will emerge in systems of such agents, and how such systems can be structured by a designer to elicit socially beneficial outcomes. Another key point of focus goes beyond such rational agent models, asking what biases agents exhibit, investigating the way economic mechanisms should adapt to such biased agents, and learning about these biases from data. A third topic of interest performs statistical analysis of data arising from economic systems; e.g., inferring agents' interests from their behaviors or distinguishing correlation from causation in observational data. In all three of these lines of work, computational tools are used to address traditionally economic problems. A fourth line of work reverses this dynamic, applying economic ideas to the design of computer systems such as peer-to-peer file-sharing systems, cryptocurrencies, crowdsourcing platforms, prediction markets, or peer-grading systems.
Team:
Kevin Leyton-Brown
Hu Fu