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The Science of Performance Curves:
Improving Global Scholarship of Technology Performance Growth

Organizer: Performance Curves SIG, Evo Devo Universe Community
Location: Complex Systems Conference Satellite Meeting.
Date: 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. 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.

Example Questions

  • What models do we have for the physical and computational foundations of technology performance curves?
  • 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 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?


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)

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.

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