Evolution and Development of the UniverseComplex Systems Conference Satellite Meeting
Technology Performance Curves: Questions, Data, Theory, and Causal Models
Location: Host Complex Systems Conference (TBD).
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. These curves have been studied by a small group of scholars since the 1930's from physical, engineering, planning, manufacturing, management, policy, computational, psychological, philosophical, and other perspectives. 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 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.
- What models do we have for the physical and computational foundations of 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?
- What explains the long-term smoothness and predictability we find in some technology performance curves in our Performance Curve Databases?
- 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, FERD, etc.) curves?
- What models explain decreasing technology performance (eg., Eroom's law for new drug introduction)?.
- Can we use actual or perceived risk models, or other factors, to improve a priori prediction of increasing or decreasing price performance in various technologies?
- What processes typically cause 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? Under what circumstances can we predict it?
- The most rapidly accelerating performance curves appear to occur in a subset of technologies (e.g., nanotechnologies, computing, and communications technologies) where the greatest rates of miniaturization and virtualization are occurring. What is the physical basis for this, and where can we expect it to continue?
- Densification 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, dense metropoli have been outcompeting less dense cities and rural areas, particularly since the advent of electronic networks, by delivering greater rates of innovation and life services efficiency per dollar, per capita (Bettencourt et.al. 2007). When and why can we expect densification to occur, and how do we model its contribution to performance curves?
- How and when does efficiency (dematerialization) in physical processes cause, sustain, or relate to performance improvement?
- How and when does virtualization and modeling intelligence (simulation, automation, machine learning) in physical processes cause, sustain, or relate to performance improvement?
- How are cognitive performance curves in individual learning related to organizational and industrial performance curves?
- When are performance curve dynamics due to physical law, averaging, scale, collective learning, economic or psychological expectations, or other physical processes?
- Why are technology product outliers so often market failures? How are outliers typically distributed (normal, log-normal, etc.) vs. the curve?
- 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?
We are seeking 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. 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, economics, and policy, underscoring the great technical, political, economic, and social value of better scholarship and science in this area.
Conference Steering Committee (incomplete)
- Tessaleno C. Devezas, physicist, materials scientist, and scholar of global technoeconomic development. (Covilhã, Portugal)
- Georgi Georgiev, physicist working on understanding the mechanisms for the measured exponential growth in complexity through time. (Worcester, MA USA)
- John M. Smart, systems theorist studying accelerating change and evolutionary development. (Mountain View, CA USA)
- Clement Vidal, philosopher and systems theorist studying evolutionary cosmology. (Brussels, Belgium)
- Steven R. Walk, electrical engineer, studying quantitative technology forecasting and social change using natural performance and diffusion models. (Norfolk, VA USA)
If you have an interest in working on the Conference steering or scientific committees, or in sponsoring or providing other assistance, please contact Georgi Georgiev.
- 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. 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, H. and Magee, C.L. (2006) A functional approach for studying technological progress: Application to information technology. Tech. Forecasting & Social Change 73:1061-1083.
- Koh, H. and Magee, C.L. (2007) A functional approach for studying technological progress: Extension to energy technology. Tech. Forecasting & Social Change 75:735-758.
- 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.
- 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.