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The Evolution of Systems Thinking

By: Jorge Taborga | 23 Jun | 2 comments

 


Systems thinking as a method of inquiry deals with complexity from the perspective of the whole, not the parts.  Most methods of inquiry follow the traditional path of reductionism as established by our sciences.  We have learned to answer life’s difficult questions by dissecting our subjects into parts with the idea that they are easier to study and understand.  Reductionism has worked well for closed or mechanical systems.  However, in the earlier part of the 20th century scientists started to question whether reductionism applied to human and social systems.

The progression of systems thinking started with Ludwig von Bertalanffy.   As early as the 1940s, von Bertalanffy postulated his general system theory in which he stated that systems continually interacted with their environment.  This gave way to the notion of open systems.  Von Bertalanffy was emphatic about the ability of open systems to resist maximum entropy and disorder which are the laws followed by closed systems.  In von Bertalanffy’s view, open systems exhibited a tendency toward order.  His view concerning open systems along with the interest of other scientists led to the creation of the International Society for the Systems Sciences (ISSS) in 1954, giving birth to systems thinking as a discipline.

In this new field of study, practitioners applied scientific methods to solve complex problems with human systems.  Operations research and cybernetics are two of the offshoots of this early discipline that made its way to universities and industry.  The subject of complexity assumed the forefront of systems thinking and it is the reason why it became relevant.  We now know that all open systems, particularly those dealing with intricate social situations, are inherently complex and need to be dealt with as a whole, not as discrete parts as reductionism would have us do.

Additionally, systems thinking introduced the concept of feedback loops.  A loop is formed by the causal relationships from variables in a system forming a circle of causality.  Variables are the parts in a system.  An example of a causal loop is the relationships formed when A affects B, B affects C, and therefore C affects A.  The causality circle or loop is formed by connecting variables A, B, C and then C to A.  Loops have very different relationships than the causality we normally deal with which is linear.  In a linear causal relationship, the connection from variable C to A is missing.  However, if it exists, it means that the variables in the loop will exhibit a joint behavior as a whole and not just as independent variables. 

The significance of feedback loops and their value in the understanding of a system is profound.  Much of the complexity and thus the behavior of systems come from these loops and the potential causal delays between variables.  The system thinking body of knowledge includes archetypes that represent well understood behaviors of interconnected causal loops.  These archetypes can be extremely useful in determining the root cause of complex problems simply by understanding the causal relationship between variables.  Systems thinking borrows modeling and analytical tools from the field of system dynamics to represent and analyze causal loops in the form of diagrams.  These diagrams are called causal loop diagrams, or CLDs. 

During the 1950s and 1960s, systems thinking thought leaders were disillusioned with the capabilities and application of this discipline.  The three reasons for this state of affairs were as follows:

  1. Traditional systems thinking required a clear and accepted objective.  The reason for this was the heavy reliance in mathematical models to solve problems.  Without a single and clear objective this was impossible.
  2. Problems with complex social systems could not be addressed.  Again, systems with high complexity were impossible to model mathematically, making systems thinking impractical.
  3. The practitioners of the day, as a group, were conservative in their scientific perspective.  This perspective blocked emerging knowledge that pushed for new approaches and tools.

During the 1970s and 1980s, several luminaries acting in discontent with traditional systems thinking developed new frameworks and intervention methods.  The results changed systems thinking to what it is today, a discipline capable of dealing with extreme complexity through planned interventions where participants include those with ownership of the problem.  Soft systems methodology, organizational cybernetics and emancipatory systems thinking are the main branches of systems thinking that are responsible for the body of knowledge we have today.

As the 1990s transpired, these branches converged into what is referred to as critical systems thinking.  In his 1990 book The Fifth Discipline, Peter Senge popularized systems thinking.  Thanks to this work and its acclaim, systems thinking made it into boardrooms, enterprises, non-profits, and education.  There is no industry or field where systems thinking cannot be applied.  All organizations are operated by humans and as such are social systems capable of great complexity.  They are composed of many variables and causal loops and can experience systemic problems.  Systems thinking is the discipline geared to address such problems.

Read other posts by Jorge Taborga

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Comments and Discussions

re: The Evolution of Systems Thinking

This would have been a more complete essay if - instead of saying "...several luminaries acting in discontent with traditional systems thinking.." - you had specifically referenced the pioneering work in the Social Systems Sciences of Dr. Russell L. Ackoff of The Wharton School, who - prior to his death in 2009 - was considered by many (including Peter Senge) to be the dean of America's systems thinking community.

For more information...

http://en.wikipedia.org/wiki/Russell_L._Ackoff

http://www.huffingtonpost.com/steven-g-brant/russell-ackoff---the-eins_b_341349.html

http://youtu.be/ZLU3aoQ7t7c

Relationship of CASs and Network Theory to System Thinking?

What do you think about the last 20 years of developing thought about complex adaptive systems and its impact on general systems theory? Does its concepts, methods, and tools offer a lense that significantly moves the project of GST forward or does it address different aspects of systems knowledge?

As tools, what do you think are the value of computational agent-based models, genetic algorithms, or principles of network/graph theory in making the structure and dynamics of complex systems visible?

from jim best (best.jim@gmail.com)

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