Over the last two years I became enthusiastic about the possibility to apply knowledge from physics, especially theoretical physics on the social life. In this post I am trying to compress all the knowledge I am familiar with, and expose you to those extremely promising fields of interest. Please note that this is the second introductory post for the topic. In the next posts on this series I will discuss real-life examples related to social deviance (crime) and economy (unemployment, stock prices), and the like.

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My previous post about the topic, dealt with systems thinking and its application on management. On this post, I will dive into a more specific field for knowledge, principally connected to geophysics and information. In the last decade, there is a group of scientist that are interested in applying mathematical models on social phenomena. The basic assumption is that social complex systems may exhibit similar behaviour to natural complex systems; and indeed Miller and Page (2009) found similarities between behaviour of animals, human beings, economy, politics, and physical systems.

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Melanie Mitchell, in her free online course “Introduction to Complexity“, touches many aspects of complex systems and shows stunning similarities between systems of birds, molecules, and other objects. She defines complex system as a system that consists of three elements: (1) components/agents; (2) nonlinear interactions between components; (3) no central control. Additionally, complex systems present an emergent behavior, and featured by: hierarchical organization; information processing – components do not make decisions, it is made collectively; complex dynamics – patterns in space and time – continually changing structure and behaviour; and evolution and learning.  Systems improve themselves to perform better, and computation helps to represent how it works.

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Additional important concept is Entropy. Entropy (firstly discovered by Boltzmann) is a measure of disorder / randomness. This degree of disorder cannot be 100% decreased, even when an intervention is performed to slow down molecules moving. On the contrary, entropy ALMOST always increases, however Newton’s second law of motion says motion is reversible. entropy can also be viewed as  hidden information, because the structure is too small and too numerous to keep track of it, but the info is never lost. You always know where you came from. The most striking element of entropy that cought my attention is its recurrence.  According to Boltzmann, fluctuations, recurrence are general property of a finite system, they will happen over and over again because of the finite number of options, and because of the fact the system cycle itself. Even though we are not living in a “finite system”, we can still keep recurrence in mind, when considering social complex systems. (for Shannonian information theory and more in-depth review – short online free course- please click here).

The last concept I would like to introduce today is Chaos. Chaos is one particular type of the dynamics of a system, and it has sensitive dependence on initial condition. It also holds the attribute of repeating behaviour – which is “universality“, things are repeating in chaotic systems. Additionally, a very helpful view of chaos is done by using bifurcation (the point it gets divided, and again and again) and roughness: The density of self-similarity. How much detail we can see when digging in more and more. Fractal-like dimensions exist everywhere in nature (such as tree. When we cut a small part and then a small prat from it, and repeatedly- we find the same structure). Therefore, iterations of simple rules can explain complex systems.

 

Applying the above into a general thinking about social-human complex systems leads us very fast to a feeling that those physical concepts may be relevant to human behaviour as well.

It is clear that social systems are complex. Hence, we can identify them by the Chaos theory, where we witness seemingly random behaviour. We can think of infinite variations in social life, where people tend to act in an unpredictable way. Is it?! The chaos theory makes some order in the entropy.

As said above, Chaos theory is looking into phenomenon which are dependent on a specific state at the start point. This theory argues that objects in the system will act with sensitive dependence on initial conditions.

In other words, social behaviour start point is when a healthy baby gets born. From this point, this baby interacts with others on the system. We can think about significant others for emotional and immediate needs, but we can also scale up and see other parts of the system. The larger family, the neighborhood, the political and economic situation at that time. We can even scale higher – global warming phase, earth and its relationship with the sun system, and so on.

This person will have a life full of interaction with enormous number of parts in the big system, and also affect and influence others who present some sort of relationship with him or her. However, given that we are trying to explain social behaviour by the Chaos theory, we assume that the initial condition has a life-long effect. Psychological as well as biological, and recently neurobiological theories are dealing with the question of “nature vs. nurture”, and nowadays the assumption is that the nature and nurture are working hand in hand to facilitate a regulated adaptive behavior (Spiegel et al., 2014). Hence, our biological systems cooperate with the social environment to create a dynamic and responsive behaviour.

We can continue thinking in this direction, and find that the above concepts actually make some order and make sense. In the next posts I will review recent scientific developments in respect to physical models on social-human behaviour; and their stunning ability to explain complex relationship between macro socio-economic variables.

 

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Miller, J. H., & Page, S. E. (2009). Complex adaptive systems: An introduction to computational models of social life. Princeton university press.

Spiegel, Ivo, Mardinly, A.R., Gabel, H.W., Bazinet, J.E., Couch, C.H., Tzeng, C.P., Harmin, D.A, Greenberg, M.E. (2014). Npas4 Regulates Excitatory-Inhibitory Balance within Neural Circuits through Cell-Type-Specific Gene Programs.  Cell , 157 (5), 1216 – 1229.