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A Framework for Managing Risk [Decision Making
Posted on July 24, 2013 @ 05:03:00 AM by Paul Meagher

What is risk and how do you manage it?

One aspect of the definition of risk is that it involves quantifying the probability of the relevant decision variables so that you can formally understand the probabilities associated with various possible outcomes. When I say "quantifying the probability" I generally mean specifying the probability distribution for that variable and using distribution statistics, such as the mean and standard deviation, to characterize the shape of the probability distribution.

When you decide to formally manage risk in your line of business you might consider using a decision making framework consisting of Actions, Events, and Outcomes.

A decision problem starts when you have the choice between multiple possible actions {a1, a2, etc...} and must make a decision as to which one to choose.

The effect of each action is not deterministic. If you decide to hire a new sales person, for example, you can't predict exactly what the effect of that decision will be other than that it will likely increase sales revenue by a certain amount, or better, by a range of sales revenue amounts with differing levels of probability. Various events affect the probability that you will achieve a certain level of sales - the economy, competitors, production capacity, etc... So, in addition to specifying the possible actions we can take, we must also identify the main events {e1, e2, etc....} that affect the outcomes we can expect.

The final component of a risk management framework involves specifying the outcomes {o1, o2, etc....} that are relevant to our decision making (e.g., o1 = increase sales by 25% to 50%, o2 = increase sales by 50% to 75%, o3 = increase sales by 75% to 100%).

We can now be very specific about what risk is: Risk = Actions {A} + Events {E} + Outcomes {O} where Events and Outcomes are quantified as probability distributions. In a later blog, I'll discuss how to use this framework to make calculations, but I'll devulge the goal of these calculations now - to compute p(O| A & E), in other words, the full conditional probability distribution.




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