Yuval Salant

Professor of Managerial Economics & Decision Sciences at Kellogg School of Management

Schools

  • Kellogg School of Management

Links

Biography

Kellogg School of Management

Yuval Salant is a Professor of Managerial Economics and Decision Sciences at the Kellogg School of Management. He has a PhD in Economic Analysis and Policy from the Stanford Graduate School of Business. His research interests include foundations of behavioral economics and bounded rationality.

Education

  • Ph.D., 2008, Economic Analysis and Policy, Stanford University
  • M.Sc., 2003, Computer Science, Hebrew University of Jerusalem, Summa Cum Laude
  • B.Sc., 2000, Computer Science, Hebrew University of Jerusalem, Summa Cum Laude

Academic Positions

  • Professor, Managerial Economics & Decision Sciences, Kellogg School of Management, Northwestern University, 2020-present
  • Associate Professor (with Tenure 01/09/2014), Managerial Economics & Decision Sciences, Kellogg School of Management, Northwestern University, 2012-2020
  • Assistant Professor, Managerial Economics & Decision Sciences, Kellogg School of Management, Northwestern University, 2009-2012
  • Donald P. Jacobs Scholar, Managerial Economics & Decision Sciences, Kellogg School of Management, 2008-2009

Awards

  • Sidney J. Levy Award for Excellence in Teaching
  • Sidney J. Levy Award for Excellence in Teaching

Read about executive education

Cases

Rubinstein, Ariel and Yuval Salant. 2012. Eliciting Welfare Preferences from Behavioral Datasets. Review of Economic Studies. 79(1): 375-387.

An individual displays various preference orderings in different payoff-irrelevant circumstances. It is assumed that the variation in the observed preference orderings is the outcome of some cognitive process that distorts the underlying preferences of the individual. We introduce a framework for eliciting the individual's underlying preferences in such cases and then demonstrate it for two cognitive processes - satisficing and small assessment errors.

Salant, Yuval. 2007. On the Learnability of Majority Rule. Journal of Economic Theory. 135(1): 196-213.

I establish how large a sample of past decisions is required to predict future decisions of a committee with few members. The committee uses majority rule to choose between pairs of alternatives. Each members vote is derived from a linear ordering over all the alternatives. I prove that there are cases in which an observer cannot predict precisely any decision of a committee based on its past decisions. Nonetheless, approximate prediction is possible after observing relatively few random past decisions.

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