This short, non-technical introduction to systems thinking by Daniel Aronson explains the difference between analysis (attempting to understand the whole by studying its parts) and systems thinking (trying to understand how the parts of a system interact). Aranson provides an excellent example that illustrates the difference by focusing on the unintended consequences that may arise from attempts to reduce crop damage by insects without understanding the system in which the insect problem occurs. Quoting:
Traditional analysis focuses on separating the individual pieces of what is being studied: in fact, the word “analysis” actually comes from the root meaning “to break into constituent parts.” Systems thinking, in contrast focuses on how the thing being studied interacts with the other constituents of the system – a set of elements that interact to produce behavior – of which it is a part.
This means that instead of isolating smaller and smaller parts of the system being studied, systems thinking works by expanding its view to take into account larger and larger numbers of interactions as an issue is being studied. This results in sometimes strikingly different conclusions than those by traditional forms of analysis, especially when what is being studied is dynamically complex or has a great deal of feedback from other sources, internal or external.
The character of systems thinking makes it extremely effective on the most difficult types of problems to solve: those involving complex issues, those that depend a great deal on the past or actions of others and those stemming from ineffective coordination among those involved. Examples of areas in which systems thinking has proven of value include:
--Complex problems that involve helping many actors see the “big picture” and not just their part of it
--Recurring problems or those that have been made worse by past attempts to fix them
-- Issues where an action affects (or is affected by) the environment surrounding the issue, either the natural environment or the competitive environment.
--Problems whose solutions are not obvious.