Research on free energy rate density

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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.

People Interested

Other scholars who have published on FERD topics at universal scale:

Scholars who have modeled FERD at biological scale:


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:

Research project:

Functional Performance Metrics/Learning Curves: