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Theory-Experimental Seminar
Michael Richter (with L. Pejsachowicz and S. Geng)
Royal Holloway, University of London
29-Jan-2018
Seminar 3 – 14:30
Abstract
We consider a fundamental trade-off in search: when choosing between multiple unknown alternatives, is it better to learn a little about all of them (breadth) or a lot about a single one (depth)? In choice settings where a distribution is exogenous, we find that breadth is optimal for “small” problems and that depth is optimal for “large” ones. But in IO settings, where rms endogenously choose distributions, we find breadth to be always optimal. Finally, we consider extensions to fat-tails and correlation, and find that in these extensions, breadth is superior.