本文为《How to measure anything?》Douglas W. Hubbard读书笔记

General framework for any measurement problem

  1. Define the decision and the variables that matter to it. (See Chapter 4)
  2. Model the current state of uncertainty about those variables. (See Chapters 5 and 6)
  3. Compute the value of additional measurements. (See Chapter 7)
  4. Measure the high-value uncertainties in a way that is economically justified. (See Chapters 8 through 13)
  5. Make a risk/return decision after the economically justified amount of uncertainty is reduced. (See the risk/return decision described in Chapters 6 and 11) Return to step 1 for the next decision.

specific procedures in Organizational settings

Phase 0: Project Preparation

  • Initial research
  • Expert identification
  • Workshop planning

Phase 1: Decision Modeling

  • Decision problem definition
  • Decision model detail
  • Initial calibrated estimates

Phase 2: Optimal Measurements

  • Value of information analysis (VIA)
  • Preliminary measurement method designs
  • Measurements methods
  • Updated decision model
  • Final value of information analysis

Phase 3: Decision Optimization and the Final Recommendation

  • Completed risk/return analysis
  • Identified metrics procedures
  • Decision optimization
  • Final report and presentation.

Philosophy

  • If it’s really that important, it’s something you can define. If it’s something you think exists at all, it’s something you’ve already observed somehow.
  • If it’s something important and something uncertain, you have a cost of being wrong and a chance of being wrong.
  • You can quantify your current uncertainty with calibrated estimates.
  • You can compute the value of additional information by knowing the “threshold” of the measurement where it begins to make a difference compared to your existing uncertainty.
  • Once you know what it’s worth to measure something, you can put the measurement effort in context and decide on the effort it should take.
  • Knowing just a few methods for random sampling, controlled experiments, or even merely improving on the judgments of experts can lead to a significant reduction in uncertainty.