When you read about a new study, you may wonder: How accurate are these results' MIT economist Isaiah Andrews PhD '14 often asks that as well, especially about social sciences research. Unlike most of us, though, Andrews' job involves answering that question. Andrews, a professor in MIT's Department of Economics, is an expert in econometrics, the study of the methods used in economics. But the purpose of his specialty defies simple boundaries. After all, the point of refining research methods is to make applied studies better ' and to better grasp their limits. 'There are many fields in economics that answer socially significant questions,' Andrews says. 'There are things it would be good for us to understand, but I often find myself interested in how sure we are about them. To what extent do we know the things we think we know' To what extent is there more to know, based on the uncertainty and degree of confidence' These issues of uncertainty matter because the answers to the substantive questions matter.'...
Where do we go from here' And how do we get there' Those are questions I have been asking myself over the past few years as I've reflected on the various crises that have been plaguing the social sciences. I began my graduate training two years after the now famous 'false-positive psychology' paper triggered a crisis of confidence about the state of evidence in psychology and the other social sciences (e.g., experimental economics) that engaged in similar meta-scientific reflections. Then came 2020. After a few years spent working through strategies to address that crisis of evidence, the Covid-19 global pandemic and the temporary reckoning about racial and social justice reignited the crisis of relevance. In addition to debates about how to improve (quantitative) methods, social scientists also debated about whether the kinds of knowledge our fields were producing were actually useful for speaking to pressing issues in society. Taken together, it seemed that we had somehow gotten ourselves into a situation in which, despite having decades of research under our collective belts, we had great difficulty understanding the nature of behaviors well enough to make good predictions about how to change them. This state of affairs has been particularly concerning due to its implications for our readiness to respond during moments of crisis. Part of the reason for this status quo seems to be the history of studying a narrow sliver of humanity in a limited set of circumstances, which has inhibited our ability to learn about the range of factors that influence people's thoughts, feelings, and behaviors. Moreover, research projects are often developed without input from the people whose lives the work is intended to represent or influence....
Daskalakis is the first person appointed to this position generously endowed by Armen Avanessians '81. Established in the MIT Schwarzman College of Computing, the new chair provides Daskalakis with additional support to pursue his research and develop his career. 'I'm delighted to recognize Costis for his scholarship and extraordinary achievements with this distinguished professorship,' says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. A professor in the MIT Department of Electrical Engineering and Computer Science, Daskalakis is a theoretical computer scientist who works at the interface of game theory, economics, probability theory, statistics, and machine learning. He has resolved long-standing open problems about the computational complexity of the Nash equilibrium, the mathematical structure and computational complexity of multi-item auctions, and the behavior of machine-learning methods such as the expectation-maximization algorithm. He has obtained computationally and statistically efficient methods for statistical hypothesis testing and learning in high-dimensional settings, as well as results characterizing the structure and concentration properties of high-dimensional distributions. His current work focuses on multi-agent learning, learning from biased and dependent data, causal inference, and econometrics....
Specialized teams ' particularly technology teams ' often face challenges as they strive to work cross-functionally, especially at legacy organizations. For Kobi Abayomi, vice president of data science at Warner Music Group, addressing such challenges starts with hiring strong talent into the technology function. Kobi Abayomi is the senior vice president for data science at Warner Music Group, where he and his team enable the company to understand, respond to, and predict trends and opportunities in listening. Abayomi has authored novel work in statistics (multivariate data imputation), econometrics (measures of inequality), and probability (distributions with fixed marginal and information theoretic measures) and has two patents pending in fraud detection and audience activation. Abayomi serves on the Data Science Advisory Council at Seton Hall University and on the Ivan Allen College Advisory Board at the Georgia Institute of Technology. In this episode of the Me, Myself, and AI podcast, Kobi joins hosts Sam Ransbotham and Shervin Khodabandeh to explain how the music company is moving its infrastructure into the digital era, how it leverages vast amounts of consumer data to make informed decisions in an increasingly challenging landscape, and how AI is helping customers discover new music they'll love....
