Complex Adaptive Systems: Primer 1/N

Over the past year, I have dedicated a large portion of my research to the study of complex systems. This started out as me being bored in an econometrics course and reading random Wikipedia pages during class without any specific goal in mind.

Protip 1: If you’re finding yourself bored in class and are desperate for an escape, use class time to teach yourself about things that interest you; this is a classic case of using opportunity costs wisely.

Protip 2: Diversify your portfolio; sometimes the best ideas come from reading things unrelated to your research interests.

Since then, my knowledge and understanding of complex systems have come into fruition to something greater and will probably form the basis of my dissertation. Without further ado, let’s start our journey into complexity:

Systems are considered complex if they meet the following criteria:

  1.  The system is composed of interacting agents.
  2.  The system exhibits emergent properties.

The second criterion is of utmost importance. If you think of a system of gears (the gears being interacting agents), like a manual transmission, there is nothing unexpected that can happen. In my Toyota Matrix, I go through the same procedure every time I find myself stopped at a stop sign: 1) Disengage clutch, 2) Shift into first, 3) Let out clutch until I can feel the friction point, 4) Engage accelerator gently, 5) When car begins moving, remove my foot from the clutch. If I have done everything correctly, I will be on my way towards my destination. If I don’t, the engine will die and I will have to start again. This is a binary outcome. The transmission only allows for these two outcomes; even though, my transmission allows for eight different parameters: six gears, reverse, and neutral. No other outcome or pattern can emerge from this set of parameter and inputs (throttle and clutch engagement); the gears cannot create new combinations of movement, nor can they decide to go in any new direction. The transmission is closed system. It is not complex because of the inability for it to create new modes or patterns of interaction between the agents comprising the system.

This is a far cry from social systems which allow for pattern formation and new types of interactions. Patterns in our systems exist everywhere: The American system of government is pattern formed through over 200 hundred years of social interactions piled on top of the initial foray in 1776. This and all other forms of governance did not exist prior to the system, but are instead outcomes of the system. Importantly, forms of governance, from that of the family to corporations to governments to international organizations are the result of the interactions of individuals. Every so often, we create new modes of interacting: language, Morse code, Bell’s telephone, radio signals, television, cellphones, internet, email, texting, social media, Snap Chat, etc.

Talking about social systems allows us to introduce an additional criterion:

3. The agents can change based on prior and current conditions/parameters endogenous to the system; at this point the system can be classified as a complex adaptive system.

Think of how individuals and groups adapt to changing conditions in a social system. In the 1980s, laws were passed requiring that only certified electricians be allowed to work on electrical wiring in residential/commercial properties. This subsequently increased the cost of hiring an electrician; in response, homeowners were more likely to work on their own residential electrical systems. This was the first adaptation. The 1980s saw a rise in residential electrocutions as under-qualified individuals attempted to repair their homes. A second adaptation would have been one of homeowners realizing the dangers and then preferring to hire qualified electricians instead of risking injury. This is just one slice of adaptations; it doesn’t capture all the schools who expanded their electrician programs, those who lost work because they lacked the new qualifications, and etc.

Interactions breed consequences (not necessarily a negative term) and adaptations.

 

Author: Deric Tilson

I am a classically-trained economist and doctoral student at George Mason. I'm an ecopragmatist and interested in the cross-section where economics, ecology, and ethology meet. I hope to work for non-for-profits specializing in economic development and eventually moving to either the public sector or a think tank. My research interests include the political economy of war, resource economics, the applications of complexity theory, the mitigation of risk by impoverished individuals, and global water scarcity.

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