Research on free energy rate density
A metric, FERD, to characterize the complexity of physical, biological and cultural systems in the universe has been proposed by Chaisson (2001; 2003) (see below).
- 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 who are interested in conducting and collaborating on more precise research as described in the research project ([File:Vidal 2010-Big History and our Future.pdf Vidal 2010]).
- We are looking for cosmologists, astrophysicists, and complexity scholars 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).
- We are looking for 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.
- We are looking for 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.
- We are seeking 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 and 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 researchers who have published on this issue:
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.
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. .