Causal Loops

Introduction

Causal Loops

Concept Maps

The Performance Map

Living in a Non-Linear World

Because the world is round it turns me on
Because the world is round...aaaaaahhhhhh

Because the wind is high it blows my mind
Because the wind is high......aaaaaaaahhhh
- Because by the Beatles

In most discussions concerning models, we assume they happen in a linear fashion, for example, the ISD model:


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However, in life, things are normally more complicated. We know that effect follows cause. For example, Newtonian cause and effect relationships are used to describe physics. When behaviorism first became in vogue, it borrowed from this Newtonian cause and effect relationship, for example, we can control people by changing the environment. However, when describing living systems and systems created by humans, what is often left out of the relationship is that effect is also another cause. Thus, this effect-cause-effect chain loops back upon itself to describe non-linear behavior, which in reality, gives the ISD model circular causality:


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This circular causality or casual loop is not only found in systems created by humans, but also mother nature. For example, cheetahs hunt gazelles, which in turn, puts selection pressure on gazelles for running speed. As they evolve to be faster, it in turn, puts pressure back on the cheetahs to be faster:

Reality is made up of circles but we see straight lines - Peter Senge in The Fifth Discipline (p. 73).

Cause and effect (causal) relationships can normally be viewed from three competing viewpoints (Hitchins, 2000):


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  • Static-Cause and Effect: Effect follows cause and there is no observable connection between one or the another.
  • Linear-Cause and Effect: This relationship has causes and effects acting like a string of dominoes falling. Most aspects of physics work in this manner. This is for those who believe in a clockwork universe.
  • Causal Looping: The systems are interconnecting feedback loops, in that cause and effect chains loop back upon themselves. Causal looping shows how systems are composed of non-linear dynamics and chaos.
One of the advantages of a causal loop model is that it forces the user to tie in the ends, thus helping to eliminate misconceptions. For example, ISD traditionally starts out with analysis. Yet, in reality, a negative impact caused the analysis. And once the ISD process has been fully traversed, the type of impact that it yields will determine if the ISD process was a success or failure. And in turn, it will determine if another analysis is called for or not.

Two types of arrowheads are used to show the relationship between two variables:

  • Open arrowheads indicate that the items at the tail and the pointed end move in sympathy, e.g. if food growth rates rise or fall, then the population rises or falls in sympathy -- or in causal terms, e.g. "a rise in crops causes a rise in population"
  • Filled-in (closed arrowheads) indicate the inverse relationship -- a rise in population causes a drop in food supplies.
The diagram below is a small snippet of the main performance map used on this site:

The closed arrowhead radiating from the performance box shows that it is having a negative impact, which in turn, cause an analysis to take place.

Causal loop models help us to better understand system and processes. This is important as system-modeling has grown considerably in recent years (Senge, et. el. 1994). However, they are far from perfect. One of the main problems with causal-loop diagrams is that they make no distinction between information links and rate-to-level links, sometimes called "conserved flows" (Richardson, 1986).

In systems thinking nothing is ever influenced in just one direction, thus the important concept of feedback -- any reciprocal flow of influence. Causal loop models, in turn, make good concept maps.

Reference

Hitchins, D. K. (Feb 2000). System Thinking.

Richardson, George (1986). Problems with causal loop diagrams. System Dynamics Review, 2.2 (summer), pp. 158-170. Pegasus Communications.

Senge, P., Kleiner, A., Roberts, C., Ross, R., & Smith, B. (1994). The Fifth Discipline Fieldbook. New York: Doubleday, pp. 27-29.


 

Notes

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Created July 5, 2004
Updated March 3, 2008

 

A Big Dog, Little Dog and Knowledge Jump Production.
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