Revision as of 23:30, 11 November 2010

Workshop 2011: APS March Meeting, Dallas, Texas, March 22-24, 11am-2:15pm each day. Return here Dec 2010 for registration information, or email John Smart to be placed on the invite list.

The Physics of Performance Curves: The Nature and Limits of Functional Performance Improvement in Technology Innovation

Technology performance curves, also known in manufacturing as progress functions, and in cognitive science as learning curves or experience curves, in which technological capacity or efficiency improves by exponential, power law, or other fashion with cumulative production over long time periods, have been studied by a small group of scholars since the 1930's. Given their accelerating impact on the technology environment, they are among the most important topics of technology innovation, strategy, and policy, and to improving our economic models of production functions. Yet in spite of their importance we do not have a good understanding of the physical basis of these curves, and many open questions remain. Fortunately, performance curve scholarship is on the rise, and the opportunity for high-impact collaboration and publication in this area has never been better.

Topics of Investigation:

• What models do we have for the physical basis of technology and complexity performance curves?
  
• Can we develop unifying theories for any classes (physical, efficiency, computational, informational) of performance curves today?
  
• For exponential curves, learning is based on a fixed percentage of what remains to be learnt. For power laws, learning slows down with experience. Which is the better model?
• What explains the long-term smoothness and predictability we find in many technology performance curves in our Performance Curve Databases?
  
• When are smoothness and predictability due to physical law, averaging, scale, collective learning, economic or psychological expectations, or other physical processes?
  
• Standard deviation and skew in performance times often show power law decreases with cumulative experience. Why and when does this occur?
• Why are scale reduction processes persistently exponential in performance improvement, and which physical processes are candidates for continued scale reduction?
  
• Why are technology product outliers (significantly off the curve) so often market failures, and can this observation lead to better R&D timing, strategy, and policy?
• How do we differentiate non-persistently exponential performance curves (market-limited, etc.) from persistently exponential (scale reduction, FERD, etc.) curves?
  
• How do non-computational (physical process, efficiency) performance curves differ from computational (computing, memory, communication) performance curves?
  
• How do computer hardware and software performance curves differ, and why does hardware exhibit consistently better long-term exponential performance improvement?
• When does technology substitution (creating a composite technology performance curve) occur in any technology platform? Under what circumstances can we predict it?
  
• When does exponential performance end in any performance curve? Under what circumstances can we predict it?
  
• What explains state switches (transitions to steeper or flatter exponential modes) in several technology performance curves?
  
• What physical processes differentiate superexponential, exponential, logistic, life cycle, and other performance curves?
  
• What do exponential and superexponential performance and efficiency curves imply for the future of technological innovation and sustainability?
  

We are seeking functional and technology performance curve scholars, technology substitution scholars, complexity transition and periodization scholars, acceleration scholars, technological evolution and development scholars, technical productivity and world system modelers, and their critics. Scholars who approach performance curve study from materials science, thermodynamic, computational, informational, evolutionary, developmental, economic, competitive, cognitive science, social science, systems theoretic and other perspectives are welcomed. We will seek to compare and critique a variety of performance curves data sets and models, and consider first-order implications of these models for technology innovation, strategy, sustainability, and policy, underscoring the great technical, political, economic, and social value of better scholarship and science in this area.

If you have an interest in working on the 2011 Workshop steering or advisory committees, or in sponsoring or providing other assistance, please contact Georgi Georgiev, Clément Vidal or John Smart.

Select Bibliography

• Bills, Albert G. (1934) General Experimental Psychology, Chap 10, The Curve of Learning (pp. 192-215), Longmans.
• Heathcote, Andrew et. al. (2000) The Power Law repealed: The case for an Exponential Law of Practice. Psychonomic Bulletin & Review. 7(2):185-207.
• Wright, T.P. (1936) Factors Affecting the Cost of Airplanes. Journal of Aeronautical Sciences, 3(4):122–128.
• Newell, A. & Rosenbloom, P.S. (1981) Mechanisms of skill acquisition and the law of practice. In: J.R. Anderson (Ed.), Cognitive skills and their acquisition. 1-51.
• Dutton, John M. & Thomas, A. (1984) Treating Progress Functions as a Managerial Opportunity. Academy of Management Review, 9(2):235-247.
• Ritter, F. E., & Schooler, L. J. (2002) The learning curve. In: Int. Encyc. of the Social and Behavioral Sciences, 8602-8605, Pergamon.