Decision Analysis

Random set of important notes:

1. Where did Decision Analysis originate?:  DA comes from a combination of the maturity of management and probability theory.  The two most important historical influences are Thomas Bayes and Daniel Bernoulli.  Bayes (in the theorem named after him) put forward the notion that by observing how often event X has happened and not happened, we can evaluate that the probability of it happening lies somewhere between any two degrees of probability.

Consider the real world example attached on cancer screening (you do not need to become and expert on Bayes Theorem problems, but I want you to understand the basic application of it).

Bernoulli is the same Swiss mathematician who developed the foundations of fluid mechanics that govern much of what we understand about airplanes and boats….in our case he put forward the idea that when you are making a decision you should incorporate the likelihood of possible outcomes and state the relative desirability of those outcomes…this is the foundation of the work we do today.

Modern DA came to us via the intense operations research environment inspired by World War II…in reality DA was being used at the intersection of academic research and military planning, but we did not call DA until the late 1950s.

2.  Why are decisions difficult?:

When making a decision (simple or complex) you have a desired outcome (make sure to take the time to formalize this), an existing level of information, and some new information (that could help with ambiguity, complexity, uncertainty) that would cost you time or money to obtain….this is an important mental model for thinking about decisions!

Decisions have different consequences and frequency….this is what drives the amount of resources we may expend in making a good decision.  Our text uses the example of deciding which movie to see on a Friday night vs choosing between building and new production facility and leasing existing space.

Difficult decisions involve three key characteristics: Consequences, Uncertainty (risk), and Ambiguity.  Consequences are (obviously) the implications of what happens with different decisions, while uncertainty is a lack of clear/crisp understanding of the future.  Ambiguity (my favorite of the three)  is a lack of clear goals and objectives.  This is most often a problem when there are many organizations, departments, and decision makers involved (think government)

Your assignment is as follows:  Respond to this forum with a real world example where you have seen consequences, uncertainty, or ambiguity influence the outcome of a decision.  It can be from world or local events, or from your own life.

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