Sociology of Complexity

The following is a copy of my final paper from a Sociology of Complexity class that I took.

Sociology of Complexity

             Simply put, the sociology of complexity is what happens when a complexity scientist studies a social phenomenon.  The reverse is also true.  If a sociologist studies a topic within sociology and uses the tools of complexity theory to do so, again it can be said to be a part of the sociology of complexity.  Or as Dr. Brian Castellani states, “While interdisciplinary in perspective, the sociology of complexity sits at the intersection of sociology and complexity science.”

            Sociology of complexity is a way of looking at social phenomenon as a network of interacting and interdependent complex social systems.  It is also the study of the characteristics of these complex social systems, which includes their emergence, self-organization and evolution.  Currently, the main areas of research for the sociology of complexity are complex network theory, the new social systems theory, sociocybernetics, and computational sociology.        

Historical Overview

             The sociology of complexity did not emerge fully formed from the void like Venus’ arrival on the oyster shell.  Many other theories from a multitude of disciplines have contributed to the formation of this new sociological toolset.  The disciplines include mathematics, physics, computer science, and sociology.

            The first roots of what was to become sociology of complexity can be found in the 1940’s with the start of systems science.  From this ground work, the fields of general systems theory and cybernetics emerged as separate sciences.  Through the merger of these two fields, another was developed.  This is the field of artificial intelligence, which uses computers to attempt to simulate the human mind.  It can be said that systems science is the theoretical framework for complexity science, while artificial intelligence gives us a method for studying it.  It wasn’t until the period circa. 1975 – 1985 that artificial intelligence realized it’s full implementation through the development of computers capable of performing the complex calculations necessary to support the computational mathematics that lie at the heart of the artificial intelligence puzzle.

            Once these new tools were available to researchers, the development of techniques such as, cellular automa, fuzzy logic, genetic algorithms, neural networking, computational sociology, and multi-agent modeling swiftly followed.  Another important advance came with the development of dynamical systems theory.  This is actually a field of mathematics, not the study of chaos as is commonly thought.

            It is through the development and interaction of these fields of study, as well as, work in the fields of chaos theory and fractal geometry, that the field of complexity science has emerged as a field of its own.

 Complex Systems

             Every complex system has its own form and inherent characteristics.  But in order to be considered a true complex system, it must display these following characteristics.  It must be:

• Emergent.
• Self-Organizing
• Complex
• Dynamic
• Evolving
• Agent-Based
• Operating in a position far from equilibrium.

             A true complex system is more than the sum of its parts.  This is due to the interactions between the agents that make up the system.  Once these interactions have caused a complex system to emerge, you cannot reduce the system to something less than it is without it becoming a different system or ceasing to be a complex system all together. 

            A complex system is considered be to complex because the agents that work together to make up the system are themselves complex systems.  The inter-relationships between the members of this web of sub-systems give the over-lying complex system the ability to react when faced with a threat or other conditions that differ from the norm.  The system reacts to these outside stimuli as well as feedback inputs that are generated internally.  Because of this, complex systems are dynamic in nature.  They will never reach a point of equilibrium.

            If equilibrium is reached, the system will become static and will eventually stagnate and die.  Complex systems do not seek a state of equilibrium.  Instead, they seek a state of self-organized criticality at a point far from equilibrium.  This steady state of operation is distributed across the system as a whole and it allows the system to adapt to the changes or perturbations that are introduced into the system.

 Complex Social Systems

            A complex social system is a form of social organization that has the same general characteristics as a complex system plus several other characteristics that are unique in nature.  The complex social system exhibits the characteristics of being emergent, self-organizing, complex, agent-based, dynamic, evolving, and operating at a position far from equilibrium, just like a complex system, but with the added caveat that the sub-systems of a complex social system will mostly be social in nature.

            The emergence of a complex social system is both an inherent property of the system and an intellectual act by the researcher.  The system emerges from the larger field of relations that surrounds it.  As the researcher chooses the variables that he deems important to his line of inquiry, these variables become the agents that form the web of sub-systems that interact to form the emergent complex social system.

 Agent-Based Modeling

             Agent-based modeling is a new methodology for research that has been developed thanks in large part to the introduction of computer systems that have the computational power to run very complex calculations at a very high rate of speed.  The pre-cursor to agent-based modeling is computational modeling, which in turn is a form of mathematical modeling.  Before the advent of high powered computer systems, the large amounts of calculations (or iterations) necessary for this type of analysis made the large scale use of this method of research impossible.

            Agent-based modeling is an effective tool for modeling and examining complex systems because it is designed to act like a complex system.  Just like a complex system, agent-based models are built from the ground up.  They react to things based on the meanings those things have for them.  They create self-organizing networks of interacting, rule-bound agents.  They operate at a point far from equilibrium and they are designed to be dynamic, nonlinear, adaptive, recursive, and distributed.

            This ability to react and adapt to changes in the system is what sets agent-based modeling apart from other methodologies like traditional mathematics and statistics.  These methodologies are linear in nature and have difficulty dealing with the nonlinearity, fuzziness and change inherent in complex systems.  Because they share the dynamic nature of the complex systems that they are designed to emulate, agent-based models easily handle the fuzzy logic and nonlinearity present in these systems.

            Although they are designed to operate in a manner that is similar to complex systems, agent-based models are not able to reach the level of complexity that is present in a true complex system.  However, they can be used to simulate the complex system on a simpler level.  Even with this limitation, these simulations can be made complex enough to provide insights on the basic interactions and interdependencies that are taking place in the complex system that is being studied.

 Applying the Theory and Method

             For an example of social complexity theory and its methods in action, I chose to examine traffic patterns in an urban area.  The first step is to define my area of inquiry.  I will be looking at how groups with different socio-economic statuses affect the traffic patterns in a large urban area.  Next, it is necessary to select my variables for this inquiry.  These variables will become the web of sub-systems for the overlying complex social system that I am trying to model.

                         Web of Sub-Systems (in no particular order)

• Geographic Location
• Gender
• Type of Transportation
• Occupation
• Environment
• Commute Time
• Zoning
• Commercial Needs

*A graphical representation of this list:

 

            Once I have established my web of sub-systems, I can look at the network of competing clusters that may result from the operation of the complex social system model.  These clusters (or groups) will be the agents that will operate in the model to allow it to evolve dynamically and self-organize into a complex social system. (Q.E.D. Agent-based modeling)

                       Network of Competing Clusters (in no particular order)

• Inner-City Blue Collar Public Transportation (ICBC Pub)
• Inner-City Blue Collar Private Transportation (ICBC Priv)
• Inner-City White Collar Public Transportation (ICWC Pub)
• Inner-City White Collar Private Transportation (ICWC Priv)
• Suburb White Collar Public Transportation (SWC Pub)
• Suburb White Collar Private Transportation (SWC Priv)
• Suburb Professional Private Transportation (SP Priv)
• Rural White Collar Private Transportation (RWC Priv)
• Rural Professional Private Transportation (RP Priv)
• Pedestrian Traffic (PED)
• Local Commercial Traffic (LCT)
• Non-Local Commercial Traffic (NLCT)
• Pass-Thru Traffic (PTT)

*A graphical representation of this list:

             Although I listed gender under my web of sub-systems, I did not break down my competing clusters by gender.  Without any hard data on the effect of gender on the likelihood of choosing public or private transportation, I chose to err on the side of caution.  In Figure 2, I have also attempted to show possible interactions and/or conflicts between the clusters by adding directional arrows to show these relationships.  A single red arrow point denotes conflict and double black arrows denote interactions.

            To take the method to the next level and actually use an agent-based model to simulate the system is the next logical step.  The Electronic Arts program, SimCity 4 provides a ready-made system that is capable of modeling this type of complex social system.  I have attempted to run a base-line model using SimCity 4 to demonstrate the effectiveness of the program to simulate traffic patterns.

            First, I laid out a network of roads.  Then I added areas zoned for different types of construction, and a supplied basic infrastructure, such as utilities, an educational system, medical services, and emergency services.

 

Once this framework was in place, I started the simulation and let it run for ten Sim-years.  The central urban area and the outlying suburbs developed nicely, resulting in a Sim population of about 52,000.

            Once you have a viable city up and running in the simulation, you can use the monitoring tools in the program to examine the traffic patterns that have developed.  The monitoring tools allow a very granular look at the patterns, letting you view overall congestion or track the volume of traffic by type or time of day.

I ran this simulation several times using the base-line template that I created.  It should be noted that each time the simulation was ran, the Sim population, the placement of buildings and the resulting traffic patterns were different.  I see this as evidence that the simulation is self-organizing in a different way each time.  This strongly suggests that the program is making use of fuzzy logic to govern the agents in the system.

            Because SimCity 4 is a commercial product, I do not have access to the source code for the program.  To do proper research using this tool, it would be necessary to be able to view and control all of the rules controlling the agents in the simulation.

 Critical Review

             The sociology of complexity can provide us with a better understanding of the world around us and the complex societies that we are an integral part of.  The advantage of this field of study is that it gives us a very robust toolkit for modeling and understanding the complex interactions that take place between social groups and organizations.

As I have shown, the use of simulations to examine complex social systems, such as simulating traffic patterns with SimCity 4, can result in very complex results that can provide us with insights into the workings of real complex social systems.  But the disadvantage is that we must be careful not to mistake the model for the reality that it is trying to represent.  As the models become more and more complex, the human tendency is to accept those models as reality.

            The sociology of complexity is not the final answer to life, the universe and everything.  It is simply a very sophisticated set of tools that we can use to get a glimpse of the supremely complex reality that we live in.