On AI, Markets and Machine Learning

What is fun about artificial intelligence (AI) is that it is fundamentally constructive! Rather than theorizing about the behavior of an existing, say social system, or understanding the way in which decisions are currently being made, one gets to ask a profound question: how should a system for making intelligent decisions be designed? In multi-agent systems we're often interested, in particular, in what happens when multiple participants, each with autonomy, selfinterest and potentially misaligned incentives come together. These systems involve people (frequently people and firms), as well as the use of AI to automate some parts of decision making. How should such a system be designed? In adopting this normative viewpoint, we follow a successful branch of economic theory that includes mechanism design, social choice and matching. But in pursuit of success, we must grapple with problems that are more complex than has been typical in economic theory, and solve these problems at scale. We want to attack diffcult problems by using the methods of AI together with the methods of economic theory