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A Data-Driven Approach to Modeling Choice
Vivek Farias · Srikanth Jagabathula · Devavrat Shah

Wed Dec 09 07:00 PM -- 11:59 PM (PST) @ None #None

We visit the following fundamental problem: For a `generic model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal preference information), how may one predict revenues from offering a particular assortment of choices? This problem is central to areas within operations research, marketing and econometrics. We present a framework to answer such questions and design a number of tractable algorithms (from a data and computational standpoint) for the same.

Author Information

Vivek Farias (Massachusetts Institute of Technology)
Srikanth Jagabathula (NYU)
Devavrat Shah (Massachusetts Institute of Technology)

Devavrat Shah is a professor of Electrical Engineering & Computer Science and Director of Statistics and Data Science at MIT. He received PhD in Computer Science from Stanford. He received Erlang Prize from Applied Probability Society of INFORMS in 2010 and NeuIPS best paper award in 2008.

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