Application of Complexity Theory: Away from Reductionist Phase Transitions

Reductionism persists as a useful node in the possibility space of understanding and managing the world around us. However the possibility space is now expanding to higher levels of resolution such as a focus on complex systems. Learning and tools are ratcheting in lock-step.

Some of the key complexity-related concepts in understanding collective behavior in real-life physical systems like the burning of a forest fire include:

• Organization and Self-Organization: Self-orchestration into order in both living and non-living systems, for example: salt crystals, graphene, protein molecules, schools of fish, flocks of birds, bee hives, intelligence and the brain, social structures 
• Order and Stability of Systems: Measurements of order, stability, and dynamical break-down in systems such as entropy, symmetry (and symmetry-breaking), critical point, phase transition, boundaries, and fractals (101 primer)
• Tunable Parameters: An element or parameter which doesn’t control the system, but can be tuned to influence the performance of the system (for example, temperature is a tunable parameter in the complex system of water becoming ice) 
• Perturbation and Reset: How and how quickly systems reset after being perturbed is another interesting aspect of complex systems 

 

Complexity science is not new as a field. What is new is first, a more congruous conceptual application of complexity thought in the sense of appreciating overall continuum of systems phenomena, not trying to grasp for the specific moment of a phase transition. Exemplar of this more comprehensive systems level thinking is Marcelo Gleiser’s reframe of the Grand Unified Theory problem and Sara Walker’s reframe of the Origins of Life problem. The other aspect that is new is the idea of working in an applied manner with complex systems, particularly with tools that are straightforward to implement like the math tools of non-linear dynamics, networks, chaos, fractals, and power laws (many inspired by the work of Stan Strogatz), and Software Tools like NetLogo, a multi-agent programmable modeling environment and ChucK, a digital audio programming language.

 

Phase transition in intelligence

There could be at least three approaches to the long-term future of intelligence: engineering life into technology, simulated intelligence, and artificial intelligence. Further, while the story of evolutionary history is the domination of one form of intelligence, the future could hold ecosystems with multiple kinds of intelligence, particular specialized by purpose/task.

There are significant technical hurdles in executing simulated intelligence and artificial intelligence, but the areas have been progressing in Moore’s Law fashion. The engineering of life into technology will need to proceed expediently to keep pace with technological advance, and tie a lot of wetware loose ends together.

At present, the mutation rate of genetic replication puts an upward bound on how complex biological organisms can be. The human cannot be more than about 10x as complex as Drosophila (the fruit fly), for example. However, if the error rate in the genetic replication machinery could be improved, maybe it would be possible to have organisms 10x more complex than humans, and so on, and so on…

Engineering life into technology

Information optimization, presently known as intelligence, is a centerpiece phenomenon in the universe. It arises from simplicity, then continuously breaks symmetry and cycles through instability on its progression to increasingly dense nodes of complexity and diversity.

A contemporary imbalance has arisen that exponentially growing technology is potentially poised to be a sole successor to human intelligence. A complex dynamical system is emerging in response, the engineering of life into technology. Numerous macroscopic and microscopic elements are under development which could together stimulate advancement to the next node of symmetry and stability, creating a phase transition in intelligence which could broadly include many varieties of sentience.

The macroscopic and microscopic network elements that comprise the complex adaptive system, engineering life into technology, are illustrated in Figure 1.

Figure 1: Elements of engineering life into technology