Editing Functional decomposition (section)

==Motivation for decomposition==
[[Image:West-side-highway traffic.png|thumb|400px|Causal influences on West Side Highway traffic. Weather and GW Bridge traffic ''screen off'' other influences]]
Decomposition of a function into non-interacting components generally permits more economical representations of the function.  Intuitively, this reduction in representation size is achieved simply because each variable depends only on a subset of the other variables. Thus, variable <math>x_1</math> only depends directly on variable <math>x_2</math>, rather than depending on the ''entire set'' of variables. We would say that variable <math>x_2</math> ''screens off'' variable <math>x_1</math> from the rest of the world. Practical examples of this phenomenon surround us. 

Consider the particular case of "northbound traffic on the [[West Side Highway]]."  Let us assume this variable (<math>{x_1}</math>) takes on three possible values of {"moving slow", "moving deadly slow", "not moving at all"}. Now, let's say the variable <math>{x_1}</math> depends on two other variables,  "weather" with values of {"sun", "rain", "snow"}, and "[[GW Bridge]] traffic" with values {"10mph", "5mph", "1mph"}.  The point here is that while there are certainly many secondary variables that affect the weather variable (e.g., low pressure system over Canada, [[Butterfly Effect|butterfly flapping]] in Japan, etc.) and the Bridge traffic variable (e.g., an accident on [[Interstate 95 in New York|I-95]], presidential motorcade, etc.) all these other secondary variables are not directly relevant to the West Side Highway traffic. All we need (hypothetically) in order to predict the West Side Highway traffic is the weather and the GW Bridge traffic, because these two variables ''screen off'' West Side Highway traffic from all other potential influences. That is, all other influences act ''through'' them.