# Cause and effect diagrams

Diagrams of effects (DOE) give us a record that we can use to compare what we thought would happen with what actually happened, thereby giving us a starting point for improving our models, and from that, improving our (design and decision-making) processes for a next time. In the ones used here there is space for nebulous items, phenomenologically observables that may or may not be measurable, so that the right hemisphere can add and correct for potential blind spots.

More or less the same symbols are used as in an influence diagram but their meaning is entirely different:

• An `oval` represents a measurable variable.
• A `cloud` represents an observable variable. Its quantity may not be measured, but could be if it were worth the cost. If it becomes quantifiable it becomes an oval.
• An `arrow` between nodes indicates a relationship between the two variables. The A node has an effect on the B node if the base of the arrow is at A and the head of the arrow at B:
• If there is `no dot` on the arrow, it means that if A moves in a direction, B moves in the same direction.
• If there is a `dot` on the arrow, it means that if A moves in one direction, B moves in the opposite direction.
• If there is a `black/white square` on the arrow, it means a decision-maker can make a choice that has effect on the effect.
• If there are `two parallel lines` on the arrow, it means a delayed response.
• If the diagram contains that if A increases → B increases and if B increases → A increases, it is called a `self-reinforcing system`, and will continue to grow (or decline) until some system limit comes into play and ends the self-reinforcing action (and sometimes the entire system).
1. Brainstorm all the measurable and observable factors in the context.
2. Give each the type of node (oval or cloud) on post-its. Put post-its on sheet of paper.
3. Look for relationships between the variables. Draw arrows.
4. Voilà, a diagram that helps see the big picture (and more of the whole).
5. Look for reinforcing loops. Brainstorm interventions to change the effects.
6. For each intervention, brainstorm measurable and observable factors that might have an effect on the intervention. Check how those relate to the measurables and observables in the original diagram.
• A relationship between two factors is not necessarily linear.
• Do not be blind to effects that manifest after a time-delay. Those yummy “unexpected” backlashes …