This post describes what I mean by Cause-alities and their relationship to the concepts of Complexity Science.
My premise is that to develop actionable insights into their problems managers must map out the network of cause and effect that surrounds the problem. When the causal structure is understood then strategies to alter the problem behavior can be developed, tested, and implemented. This is the Cause-alities paradigm.
There are a variety of reasons (some of which will become future posts) that make this much easier said than done.
The most fundamental cause of difficulty is that for most interesting issues the relevant network of cause and effect is what the sciences describe as a "complex system". The definition of a complex system system is elusive; a bit like trying to define pornography -- no one can agree on a definition but they know when they see it -- so for now I want to focus on just the most important attribute. The behavior of a complex system, and particularly the behavior of the issues of interest, emerge over time. Complex systems are dynamic. A complex systems model can begin to describe "How the future is going to emerge from the present".
Complex systems, and therefore the Cause-alities paradigm, cannot be described and modeled in spreadsheets (see my rant on WMDs here) and attempts to do so amounts to willful ignorance of the underlying drivers that are important. We need the tools of Complexity Science, specifically System Dynamics and Agent Base Modeling, to pursue the Cause-alities paradigm.
In closing, what's with the "Complexity Science With an Attitude" tagline? Just this: In all of the buzz and intellectual excitement created by the "new complexity sciences" the application in our everyday businesses and lives is often neglected. This blog is about the ongoing application of complexity science to implement the Cause-alities paradigm.
