High Value Measurements and Uncertainty in Decision Theory

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In decision theory, sensitivity analysis and information value calculation are compared. While sensitivity analysis identifies the sensitivity of variables, it does not assign monetary value to each variable. Instead, information value calculation in decision theory provides the monetary value of variables, revealing that organizations often measure the wrong aspects, such as focusing more on measuring costs in IT projects rather than benefits which are more uncertain. This measurement inversion leads to overlooking crucial variables like the chance of project cancellation and technology adoption rate, which greatly impact the project's success but are often not included in business cases. Failure to measure these high-value variables is a significant risk in decision-making.

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Can we measure everything that matters to us? When is measuring the correlates of a thing pretty much just as good as measuring the thing itself? Why are some people resistant to measuring certain things? What are some things people should be measuring but aren't? What's the connection between measuring things and assigning probabilities to events? How much do we know about how well human intuition performs against "doing the math"? How inconsistent are we at applying our own principles in decision-making? What kinds of calibration training are effective? What is "value of information"? What is the Rule of 5? What are the top three things we can do to improve our decision-making?

Douglas Hubbard is an author, consultant, and recognized expert in decision theory and risk analysis. He has written several books on measurement and measuring risk. His work spans various industries including insurance, finance, pharmaceuticals, aerospace, military, energy, government, tech, and nonprofit organizations. Connect with Doug on LinkedIn, email him at dwhubbard@hubbardresearch.com, or learn more about his work at hubbardresearch.com.

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