A Robust Decision is the best possible choicee; one found by eliminating all the uncertainty possible within available resources, and then choosing with known and acceptable levels of satisfaction and risk.

Robust Decision-Making techniques are designed to address:

  • The information we have is uncertain
  • We each have different interpretations of the available information
  • We each think different things are important
  • We don’t have a good decision-making strategy
  • It is not clear what to do next to reach a decision
  • We don’t understand the risks with each option
  • We must manage alternative and criteria evolution
  • We must get buy-in on any decision we make

 The Robust Decision approach is based on a wide variety of sources ranging from the writings of Benjamin Franklin to those of Genichi Taguchi; on mathematical methods such as Multi-Attribute Utility Theory, Bayesian Probabilities, and simple Pro-Con lists; and on research from psychology, engineering, artificial intelligence, and sociology. 

To read more about this approach see David Ullman's book Making Robust Decisions

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