Research on free energy rate density
A metric to characterize the complexity of physical, biological and cultural systems in the universe has been proposed by Chaisson (2001; 2003) (see below). It is called Free Energy Rate Density (FERD).
- How can we make this metric more precise and improve its data sets?
- What are the limitations of this metric?
- How can we relate this approach with network thermodynamics in complex, hierarchically structured systems? (as suggested by (Ellis 2001)).
- Can we complete the curve to understand the past (early universe) and the future (acceleration of technology)?
- What happens if we use this metric for the early universe?
- How well does the free energy rate density curve fit with Moore's law? If we extrapolate those two trends, do they have any functional relation?
Progressing on these issues
We are looking for researchers to collaborate on investigating FERD and its larger human implications, as described in this brief FERD Research Project Overview (Vidal 2010). Team members who could be particularly valuable to the FERD Research Project:
- Cosmologists, astrophysicists, complexity scholars, systems theorists, and investigators of "Big History" who have or are interested in using this metric on the emergence of structural and functional complexity in the universe (early, middle, and recent), including FERD trends on Earth.
- Engineers, physicists, mathematicians, computer scientists who model FERD dynamics in chemical and biological systems.
- Technology scholars, cliometricians, and statisticians who construct learning/experience curves historically and extrapolate them to the future, across the emergence of structural and functional complexity in technology.
- Complexity scholars and evolutionary developmental biologists who study learning theory in complex adaptive systems, and the role of free energy and metabolism in marginal and total learning.
- Anyone else who has studied these issues, or is interested in helping us improve the data sets, and methodology, and validation or falsification of FERD growth, complexity transition, or learning curve models.
An improved quantitative understanding of these processes will allow us to better characterize the evolutionary development of complexity in our universe.
Other scholars who have published on FERD topics at universal scale:
Scholars who have modeled FERD at biological scale:
- Harish Narayanan (FERD as a universal measure for quantification of the physical processes that govern tumor progression).
Bela Nagy has set up a 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.
Free Energy Rate Density:
- Aunger, Robert. 2007a. A rigorous periodization of 'big' history. Technological Forecasting & Social Change 74, no. 8 (October): 1164-1178. doi:10.1016/j.techfore.2007.01.007.
- ———. 2007b. Major transitions in 'big' history. Technological Forecasting & Social Change 74, no. 8 (October): 1137-1163. doi:10.1016/j.techfore.2007.01.006.
- Chaisson, E.J. 2001. Cosmic Evolution: The Rise of Complexity in Nature, Harvard U. Press. ISBN 067400342X
- ———. 2003. A Unifying Concept for Astrobiology, International Journal of Astrobiology 2:91-101.
- Ellis, George. 2001. An energetic view of nature (Book Review of Cosmic Evolution, Chaisson 2001). Nature 412, no. 6847: 587-588. doi:10.1038/35088114. http://dx.doi.org/10.1038/35088114.
- Vidal, Clément. 2010. Big History and our Future: extension, evaluation and significance of a universal complexity metric. Research proposal.
Functional Performance Metrics/Learning Curves:
- 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.
- ———. 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. .
- Nordhaus, W. D. 2007. Two centuries of productivity growth in computing. The Journal of Economic History 67, no. 01: 128-159. .
- Bryce, R. 2014. "Smaller, faster, lighter, denser, cheaper" (New York, Public Affairs)