Who sees what online — and why

Andreas Haupt is a PhD candidate in Engineering-Economic Systems at IDSS, and the Computer Science and Artificial Intelligence Laboratory (CSAIL). He obtained his MS in Economics and Mathematics from the University of Bonn, and his BS in Computer Science from the University of Frankfurt. He has worked in platform enforcement on both sides of the Atlantic. During his time at MIT, he has led the Science Policy Initiative and the MIT AI Ethics and Policy group.

What is the focus of your research? What sort of knowledge and disciplines does it bring together? How will it make an impact?

I design markets in the era of machine learning. The market is my connection to economics and by “market”, I mean any technology that helps people to get what they want. There are markets where people interact with it through money, but lots of work in economics has been done regarding the design of money-free markets. For example, designing the rules of how students are assigned to schools they’d like to attend is market design. My focus is one type of money-free market: the recommendation system.

Whenever a service online chooses what you see—for example on Instagram, TikTok, or Snapchat—such a system is at work. Recommendation systems, like other markets, need to allow users to express what they like, and leverage this information to make an informed choice on who is recommended what, while taking into account what this means for content contributors whose content is either viewed, or not.

Market Design not only discusses such technologies in the abstract, but actually proposes concrete algorithms to use on real data. In working on market design, I propose designs that are based on online learning. Online learning algorithms make decisions while receiving data and dynamically learning from reactions to these decisions. For recommender systems, these decisions consist of recommendations, the data consists of user interactions with recommendations, and the system attempts to learn the user’s preferences.

What did you do before IDSS and why did you choose SES?

I was a high school teacher at a high school in Germany, where I taught math and computer science. I wanted a change from this line of work, and was attracted by data science, which led me to apply to Ph.D. programs in the U.S. SES certainly stood out as a very ambitious program, in forcing candidates to become fluent both in engineering and in social science—I loved it.

What are your favorite things about the IDSS and MIT communities? What do you do for fun?

I love the curiosity and ambition in this community. It is quite striking to me how many people I have come across who, in response to a passing reference to a concept, will return after a few weeks having mastered the concept, apparently learning from books for themselves, only to apply it to a problem they were working on. What I love about IDSS in particular is that there is a clear sense that the thing you are learning and the problem you are solving are only constrained by the problem, not the discipline. I feel welcome, given my shared identity as a computer scientist and economist.

In my private life, I like cooking, chance encounters at the many social events around Cambridge (or at CSAIL’s espresso machine), and the feeling of accomplishment after an early gym session with friends.