Vivek Farias (MIT) - A New Approach to Learning and Modeling Choice
Thursday, November 15, 2012 at 4:15pm
Frank H. T. Rhodes Hall, 253
Abstract: Modeling how customers choose from among a set of alternatives is a central problem in operations and marketing. Real world implementations of many of these models face the formidable (and very basic) stumbling block of simply selecting the “right” model of choice to use. Thus motivated, we visit the following 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, how may one predict revenues from offering a particular assortment of choices? Under what circumstances can one hope to learn such models? We present a non-parametric framework to answer these and other related questions. We design tractable algorithms from a data and computational standpoint for the same. Our approach represents a substantial departure from the typical attack on such basic questions. This departure is necessitated by problem scale and data availability. In addition, we will present details of an application of this approach at Ford Motor Co.
This is joint work with Srikanth Jagabathula and Devavrat Shah.
Bio: Vivek is interested in the development of new methodologies for large scale dynamic optimization and applications in RM, marketing and healthcare. He received his Ph.D. in Electrical Engineering from Stanford University in 2007 and has been at MIT since. Vivek is a recipient of an IEEE Region 6 Undergraduate Student Paper Prize (2002), an INFORMS MSOM Student Paper Prize (2006, 2010 (advised)), an MIT Solomon Buchsbaum Award (2008), an INFORMS JFIG paper prize (2009, 2011), the NSF CAREER award (2011), and was a finalist for the 2011 INFORMS Pierskalla award. Outside academia, Vivek co-developed the strategies used by GMO's (a USD 100B money manager) first successful high frequency fund.
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