Academic journal article Emergence: Complexity and Organization

Interpreting "Big History" as Complex Adaptive System Dynamics with Nested Logistic Transitions in Energy Flow and Organization

Academic journal article Emergence: Complexity and Organization

Interpreting "Big History" as Complex Adaptive System Dynamics with Nested Logistic Transitions in Energy Flow and Organization

Article excerpt

Abstract

Big History might be considered the study of an evolving, large, complex adaptive system with three very different phases progressing geometrically from the early universe to the present day. A geometrical progression rate would suggest transitions to life evolution beginning at about 5 billion years ago; to brain evolution around 5 million years ago; and further transition to technological civilization development about 5.000 years ago. Characteristic properties of complex adaptive systems include: (1) a resource which drives the level of complexity, such as energy flow; (2) new options at critical nonlinear decision points along development paths tied to levels of energy flow; and (3) continuous logistic learning as the options are explored; (4) scaling of other dimensions besides energy, such as length and time scales of important processes. This paper presents indications that these processes are occurring through historical trends in energy, environment, economics, and organization. The understanding of these phenomena could contribute to our ability to develop and anticipate potential future scenarios with more integrated, systemic, and effective approaches and expectations.

Approaches to complex adaptive systems

Complex adaptive systems 16,9,26,41 displaying a range of common emergent characteristics have been found in a variety of fields such as biological evolution 25( ecosystems 52 and social systems 46. Some previous studies contribute to an interpretation that societies exhibit learning (of social organization, technologies, and energy use).

History may well form a large complex adaptive system 24,38,55,56. As systems progress, new options that arise for the systems may spontaneously bifurcate into two potential discrete states. While the simplest model of complex systems can be driven into chaos, more realistic models with limitations suggest a possible reversal of increasing complexity 57. Another approach is to take a longer view of historical trends and phases. Carl Sagan 51 presented stages of information processing, progressing exponentially from the early universe to the present day. These stages were the development of life, brains, and technology, starting with life origins about 5 billion years ago. A geometrical progression rate would suggest transitions from life evolution to brain evolution around 5 million years ago and further transition to civilization and technological development about 5.000 years ago.

Characteristic properties of complex adaptive systems include (1) a resource that drives the level of complexity, such as energy use 58,59,4,19,6,5 (2) new options at critical stages (bifurcations) along development paths; and (3) competition and learning as the options are explored (Figure 1). The difference in the dynamics of complex adaptive systems compared to a complex system is the adaptive or learning aspect. This changes the static diagram of complexity as a function of driving parameter in a complex system that might exist at one value of the parameter. The learning is logistic between bifurcations with increasing energy usage, which eventually leads to an environment that requires reorganization at a critical energy flow at the bifurcation points. Reorganization is required to control the increasing energy flow without crossing into chaos related to uncontrolled energy release such as fires, wars, and environmental degradation. The increasing energy flow drives environmental changes and challenges that might lead to a self-organized criticality at the bifurcation point. If the changes between bifurcation points are too large, the logistic learning phase might become nested (i.e., a single long logistic transition may be realized as several smaller logistic transitions). This has been observed in the development of fundamental physics discoveries that seem to be flowing within one large logistic growth pattern that could possibly be realized as seven sequential smaller logistic transitions32. …

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