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Two Measures of Difficulty

Page, Scott E. (1995) Two Measures of Difficulty. Social Science Working Paper, 927. California Institute of Technology , Pasadena, CA. (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20170818-133754197

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Abstract

This paper constructs two measures of difficulty for functions defined over binary strings. The first of these measures, cove r size, captures the difficulty of solving a problem in parallel. The second measure, ascent size, captures the difficulty of solving a problem sequential. We show how these measures can help us to better understand the performance of genetic algorithms and simulated annealing, two widely used search algorithms. We also show how disparities in these two measures may shed light on the organizational structure of firms.


Item Type:Report or Paper (Working Paper)
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http://resolver.caltech.edu/CaltechAUTHORS:20170821-135134067Related ItemPublished Version
Additional Information:This paper has benefited from comments by seminar participants the NBER\CEME Decentralization Conference, UC-Irvine, and UC-San Diego. The author would like to thank Stan Reiter for his guidance and for his many suggestions in helping refine the concepts developed in this paper. Art Devany, Gordon Green, Jim Jordan, Nick Vriend, and an anonymous referee made helpful comments on earlier versions of this paper. Published as Page, Scott E. "Two measures of difficulty." Economic Theory 8, no. 2 (1996): 321-346.
Group:Social Science Working Papers
Series Name:Social Science Working Paper
Issue or Number:927
Classification Code:JEL: O22
Record Number:CaltechAUTHORS:20170818-133754197
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170818-133754197
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:80622
Collection:CaltechAUTHORS
Deposited By: Jacquelyn Bussone
Deposited On:18 Aug 2017 21:44
Last Modified:03 Oct 2019 18:33

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