Research on the science of performance curves
From Evo Devo Universe
Technology performance curves, also known in engineering, economics, and manufacturing as progress or production functions, and in cognitive science as learning curves or experience curves, involve the growth of technological capability or efficiency by exponential, power law, logistic, or other fashion with cumulative experience or production. Technology is broadly interpreted here to include information, computation, communication, and physical production and transformation technologies. These curves have been studied by a small group of scholars around the world since the 1930's from physical, engineering, planning, manufacturing, management, policy, computational, psychological, philosophical, and other perspectives. There are also a few collections of them, such as Santa Fe Institute's Performance Curve Databases, but no broadly representative databases yet. Given their accelerating impact on the technology environment, they seem a particularly useful topic of technology innovation, strategy, economics, and policy. Yet in spite of their increasing importance, we do not presently have broadly accepted theory or understanding of the physical and informational basis, limits, and reliability of long-term forecasts of these curves. Performance data are growing, but remain poorly organized, and many open questions remain. The scientific, technical, and policy potential for scholarship and collaboration in this emerging area has never been greater.
We need a better understanding of the science of performance curves.
- How can we best improve today's early scholarship of technology performance growth?
- What models do we have for the physical and computational foundations of technology performance curves?
- What are first-order implications of these models for technology innovation, strategy, sustainability, economics, and policy?
- What are the limitations of performance curve models?
- How can we make performance curve forecasting more precise and improve our data sets?
- How can we relate performance curves to information theory, learning theory, and complex, hierarchically structured systems?
- How are cognitive performance curves in individual and collective learning related to technology performance curves?
- How do these models differ from performance curve models in socioeconomic, biological, ecological, and other complex systems domains?
- What physical processes differentiate superexponential, exponential, logistic, life cycle, and other tech performance curves?
- When can logistic, agent based, cellular automata, and other modeling approaches explain technology performance curve behavior?
- Can we develop unifying theories (physical, efficiency, computational, informational, psychological) among performance curve models?
- How do non-computational (physical process, efficiency) performance curves differ from computational (computing, memory, communication) performance curves?
- When does exponential performance end in any technology performance curve? Under what circumstances can we predict a transition to a logistic, catastrophic, or other regime?
- Can we reliably differentiate non-persistently exponential performance curves (market-limited, etc.) from persistently exponential (scale reduction, ERD, etc.) curves?
- What models (perceived and actual risk, etc.) explain decreasing technology performance in some social domains, such as (eg., Eroom's law for new drug introduction)?.
- What models explain growth rate switches (transitions to steeper or flatter exponential modes) in technology performance curves?
- When does technology substitution (creating a composite technology performance curve) occur in any technology platform? How can we predict the rate and extent of substitution?
- The most rapidly accelerating performance curves appear to occur in technologies where the greatest rates of densification, efficiency increases, and virtualization (subsitution of informational for physical process) are occurring (e.g., nanotechnologies, computing, and communications technologies). How do we measure these processes?
- Densification or localization of nodes and edges of many technological, social, and information networks is also occurring over time, following a power law (Leskovec 2005). As one example, increasingly dense/local metropoli have been outcompeting less dense cities and rural areas as civilization develops, with the densest environments usually delivering greater rates of innovation and life services efficiency per dollar, per capita (Bettencourt et.al. 2007). When and why can we expect densification/localization to occur, and how do we model its contribution to performance curves?
- For exponential curves, learning is based on a fixed percentage of what remains to be learnt. For power laws, learning slows down with experience. When is each model valid?
- Standard deviation and skew in performance times often show power law decreases with cumulative experience. Why and when does this occur?
- How do computer hardware and software performance curves differ, and why does hardware exhibit consistently better long-term exponential performance improvement?
- Why are technology product outliers so often market failures? How are outliers typically distributed (normal, log-normal, etc.) vs. the curve?
- Do performance curves that appear hyperbolic (tending toward a finite-time singularity) signal an impending phase change in physical systems?
Progressing on these issues
We are looking for researchers to collaborate on investigating performance curves and their larger implications. Team members who could be particularly valuable to the Performance Curve Research Project:
- Physicists, systems and process engineers, functional performance capability planners, management and learning theorists
- Neuroscientists, cognitive scientists, technology substitution scholars, miniaturization, densification, dematerialization, virtualization, simulation and automation scholars
- Computer scientists, economists, complexity theorists, technological evolution and development scholars 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.
- Anyone else who has studied these issues, or is interested in helping us improve the data sets, and methodology, and validation or falsification of performance curve growth models.
An improved quantitative understanding of these processes, that will allow us to better characterize technological performance in universal context.
EDU Scholars Interested
- Tessaleno C. Devezas, physicist, materials scientist, and scholar of global technoeconomic development.
Affiliations: Associate Professor, University of Beira Interior, Covilhã, Portugal.
- Georgi Georgiev, physicist working on understanding the mechanisms for the measured exponential growth in complexity through time.
Affiliations: Assistant Professor, Assumption College, Worcester, MA USA.
- Cadell Last, evolutionary anthropologist integrating our evolutionary past into a systems-level analysis of future development.
Affiliations: Doctoral Student, Vrije Universiteit Brussel, Brussels, Belgium.
- John M. Smart, systems theorist studying accelerating change and evolutionary development.
Affiliations: Lecturer, Naval Postgraduate School, Monterey, CA, USA; President, Acceleration Studies Foundation, Los Gatos, CA USA.
- Clement Vidal, philosopher of science and cosmology, focusing on natural, comprehensive and systematic philosophy.
Affiliations: Post-Doc, Free University of Brussels (VUB); Researcher, Centre Leo Apostel, ECCO Group, and Global Brain Institute, Brussels, Belgium.
- Steven R. Walk, electrical engineer, studying quantitative technology forecasting and social change using natural performance and diffusion models.
Affiliations: Assistant Professor, Engineering and Technology, Old Dominion University, Norfolk, VA USA.
External scholars who have published on performance curve topics from a physical or informational perspective (starter list):
- Robert Aunger
- Robert Bryce
- Quan Bui
- Eric Chaisson
- James P. Crutchfield
- George Ellis
- Doyne Farmer
- Heebyung Koh
- Chris Magee
- Bela Nagy
- Jessica Trancik
- Geoffrey West
In 2010, Bela Nagy set up a prototype website at the Santa Fe Institute, the Performance Curve Database (PCDB) to explore learning/experience curves (also known as functional performance metrics) in technology and other learning systems. The website allows researchers to download and upload datasets. He has a brief video introduction to the PCDB.
- Albright, R. (2002) What Can Past Technology Forecasts Tell Us About the Future? Tech. Forecasting & Social Change 69(5):443–464.
- Arthur, W.B. (2009) The Nature of Technology, Free Press.
- Aunger, R. (2007) Major transitions in 'big' history, and A rigorous periodization of 'big' history. Tech. Forecasting & Social Change 74(8):1137-1178.
- Ausubel, Jesse H. (1989) Regularities in Technological Development. In: Technology and Environment, Ausubel, J.H. and Sladovich, H.E. (eds.), National Academies Press.
- Bettencourt, Luis M.A. et.al. (2007). Growth, innovation, scaling, and the pace of life in cities. PNAS 104(17):7301–7306.
- Bills, Albert G. (1934) General Experimental Psychology, Chap 10, The Curve of Learning (pp. 192-215), Longmans.
- Chaisson, E.J. (2003) A Unifying Concept for Astrobiology, International Journal of Astrobiology, 2:91-101.
- Clauset, A. et.al. (2009) Power-law distributions in empirical data. SIAM Review 51:661-703.
- Devezas, T.C. and Modelski, G. (2003) Power law behavior and world system evolution. Technol. Forecast. Soc. Change 70:819–859.
- Dutton, John M. & Thomas, A. (1984) Treating Progress Functions as a Managerial Opportunity. Academy of Management Review, 9(2):235-247.
- Gantz, J.F. et.al. (2008) The Diverse and Exploding Digital Universe: A Forecast of Worldwide Information Growth Through 2011, IDC.
- Grubler, A. (1990) The Rise and Fall of Infrastructures: Dynamics of Evolution and Technological Change in Transport,, Physica-Verlag Heidelberg.
- Grubler, A. et.al. (1999) Dynamics of energy technologies and global change. Energy Policy 27:247-280.
- Hartle, J.B. (1997) Sources of Predictability, Complexity 3(1):22-25.
- Heathcote, Andrew et.al. (2000) The Power Law repealed: The case for an Exponential Law of Practice. Psychonomic Bulletin & Review. 7(2):185-207.
- Jenkins, Alastair D. (2005) Thermodynamics and economics, Arxiv.org.
- Kelly, Kevin (2010) What Technology Wants, Viking.
- Koh, Heebyung, and Christopher L. Magee. (2006) A functional approach for studying technological progress: Application to information technology. Technological Forecasting & Social Change 73, no. 9 (November): 1061-1083. doi:10.1016/j.techfore.2006.06.001.
- Koh, Heebyung, and Christopher L. Magee. (2008) A functional approach for studying technological progress: Extension to energy technology. Technological Forecasting & Social Change 75, no. 6 (July): 735-758. doi:10.1016/j.techfore.2007.05.007.
- Leskovec, J. et.al. (2005) Graphs Over Time: Densification Laws, Shrinking Diameters and Possible Explanations KDD2005, August 21–24, 2005, Chicago, Illinois, USA.
- Limpert, E. et.al. (2001) Log-normal distributions across the sciences: Keys and clues. BioScience 51(5):341-352.
- Magee, C.L. (2009) Towards quantification of the role of materials innovation in overall technological development. Working Paper 2009-09, MIT Engineering Systems Division, 31pp.
- Magee, Christopher L. 2012. “Towards Quantification of the Role of Materials Innovation in Overall Technological Development.” Complexity 18 (1): 10–25. doi:10.1002/cplx.20309.
- Nagy, Béla, J. Doyne Farmer, Jessika E. Trancik, and John Paul Gonzales. (2011) Superexponential Long-term Trends in Information Technology, Tech. Forecasting & Social Change 73:1061-1083.
- Nagy, Béla, J. Doyne Farmer, Quan M. Bui, and Jessika E. Trancik. (2013) “Statistical Basis for Predicting Technological Progress.” PLoS ONE 8 (2) (February 28): e52669. doi:10.1371/journal.pone.0052669.
- 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.
- Nordhaus, W.D. (2001) The Progress of Computing. Cowles Foundation Discussion Paper No. 1324, 61 p.
- Nordhaus, W.D. (2007) Two Centuries of Productivity Growth in Computing. The Journal of Economic History 67(1):128-159.
- Ritter, F.E., & Schooler, L. J. (2002) The learning curve. In: Int. Encyc. of the Social and Behavioral Sciences, 8602-8605, Pergamon.
- Speelman, C.P. and Kirsner, K. (2005) Beyond the Learning Curve: The Construction of Mind. Oxford U. Press.
- Triplett, J.E. (1999) The Solow productivity paradox: what do computers do to productivity?, Canadian J. of Economics 32(2):309-334.
- Vidal, Clément. 2010. Big History and our Future: extension, evaluation and significance of a universal complexity metric. Research proposal.
- Walk, S.R. (2012) Quantitative Technology Forecasting Techniques. In: Technological Change, Aurora Teixeira (Ed.), InTech.
- Wernick, I.K. et.al. (1997) Materialization and Dematerialization: Measures and Trends. In: Technological Trajectories and the Human Environment, Ausubel, J.H. and Langford, H.D. (eds.), National Academies Press.
- Wright, T.P. (1936) Factors Affecting the Cost of Airplanes. Journal of Aeronautical Sciences, 3(4):122–128.