Synthesizing Historical Research Leads to a Simple, Compatible, and Extensible Big History Framework and Periodization


David J. LePoire


Many questions remain unanswered regarding how Big History events should be organized and interpreted. For example, should Big History be divided into periods or processes, when it is clearly a complex process of simultaneous interacting processes? In such an uncertain environment, how are models constructed and evaluated? When we look at Big History, at what level of abstraction do we start?

This paper gleans important insights from a number of historical papers to develop a consistent view of Big History. It is important to consider many aspects of an evolving system. For example, how does the evolving system 1) learn from its experiences in the environment; 2) extract energy and resources to combat the trend towards chaos and higher entropy; 3) organize itself at multiple levels to meet new challenges?
Various traditional scholarly disciplines contribute to various aspects of Big History, including astronomy, geology, evolutionary biology, evolutionary anthropology, and the history of civilizations. This suggests a base framework of cosmic and terrestrial phases. The terrestrial phase saw the sequential evolution of life, humans, and civilization. Based on this framework, the various disciplines’ timelines are consolidated along with their dynamic systems models. This base framework is then expanded to include more details. This simple approach meets many criteria for an effective framework: it integrates with knowledge from other disciplines; it provides a simple, understandable model; it can be extended in detail with nested transitions; and it immediately expresses the acceleration of evolving complex adaptive systems (CAS). Similarly, proposed frameworks offer slightly different perspectives.


Author Biography

David J. LePoire, Argonne National Laboratory

David LePoire researches, develops and applies science principles in environmental issues, Big History evolutionary trends, and particle scattering. He has a BS in physics from CalTech, a Ph.D. in computer science from DePaul University, and over thirty years’ experience at the Argonne National Laboratory in the development of scientific analyses, software, training, and modeling. His research includes Big History synergistic trends among energy, environment, organization, and information.