acceleration and performance increase in a complex world
Location: Project for a Complex Systems Conference Satellite Meeting, venue 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.
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