This paper considers learning when the distinction between risk and ambiguity matters. It first describes thought experiments, dynamic variants of those provided by Ellsberg, that highlight a sense in which the Bayesian learning model is extreme - it models agents who are implausibly ambitious...

This paper considers learning when the distinction between risk and ambiguity (Knightian uncertainty) matters. Working within the framework of recursive multiple-priors utility, the paper formulates a counterpart of the Bayesian model of learning about an uncertain parameter from conditionally...

University of Minnesota Ph.D. dissertation. July 2010. Major: Business Administration. Advisor: Dr. Arthur V. Hill. 1 computer file (PDF); ix, 100, appendices 1-4.

We analyze how individuals make forecasts based on time-series data. Using a controlled laboratory experiment, we find that forecasting behavior systematically deviates from normative predictions: Forecasters overreact to forecast errors in relatively stable environments, but underreact to...

This paper tests a geography and growth model using regional data for Europe, the US, and Japan. We set up a standard geography and growth model with a poverty trap and derive a log-linearized growth equation that corresponds directly to a threshold regression technique in econometrics. In...