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The Level of Math in Risk Management March 28, 2015

Posted by Edwin Ritter in Project Management.
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Managing risk and selecting the best mitigation choice can be hard. There are many factors that will influence what choice is selected. Using data to manage risks and assess choices is always good. Having a process to use that data to evaluate choices is better. Having good data to assess risks and a process to determine choices is the point of this post.

Some background first. Decision theory is a complicated subject and is used in many different fields in business and leisure (i.e. – games). There are 4 basic elements in decision theory: acts, events, outcomes, and payoffs. A formal definition is “the mathematical study of strategies for optimal decision-making between options involving different risks or expectations of gain or loss depending on the outcome.” An informal definition of decision theory could be “identifying the valuesuncertainties and other issues relevant in a given decision, its rationality, and the resulting optimal decision.

Got that? Good. Sorting out between what is known and unknown can assist in which risk mitigation choice is best. It is always preferable to be in the magic quadrant and deal with knowns rather than unknowns.  Consider these groups in a matrix:

  1. Known Known – circumstances or outcomes that are known to be possible, it is known that they will be realized with some probability.
  2. Known Unknown – circumstances or outcomes that are known to be possible, but it is unknown whether or not they will be realized. This is known as a Risk.
  3. Unknown Known – circumstances or outcome a modeler intentionally refuse to acknowledge that he/she knows.
  4. Unknown Unknown – circumstances or outcomes that were not conceived of by an observer at a given point in time. This is known as an uncertainty.

In short, those things we know we don’t know are risks and those things we don’t know what we don’t know are uncertainties. In the magic quadrant, both are below the line.

RiskTreeStill with me? The uncertainties, the unknown unknowns, are the hardest pieces to deal with. With decision theory, assigning values of probabilities helps determine which choice is best. As an example, consider the following image. Getting the data to assign the options and organizing into a concise arrangement is where the math comes in.

Not all risks require this level of math and analysis to determine the best mitigation choice. The level of math should be appropriate to the complexity of the project and the potential risks involved. It is worth noting that the difference between an issue and a risk is :

– an issue is something that has occurred which impacts* the project.

– a risk is something that may or may not occur that can impact the project.

* impact can be positive or negative.

I prefer to stay above the line and deal with knowns and those risks that are relatively small on my projects. It makes for simple math and for projects where my preferences matter, I find those are the best projects to manage.

Glad I did not lose you and you got this far. How do you manage risks? What is the level of math required for risks? Simple? Complex? Hybrid? Comments invited.

 

 

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