Published August 18, 2017
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Two Measures of Difficulty
- Creators
- Page, Scott E.
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.
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.Attached Files
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Additional details
- Eprint ID
- 80622
- Resolver ID
- CaltechAUTHORS:20170818-133754197
- Created
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2017-08-18Created from EPrint's datestamp field
- Updated
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2019-10-03Created from EPrint's last_modified field
- Caltech groups
- Social Science Working Papers
- Series Name
- Social Science Working Paper
- Series Volume or Issue Number
- 927