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+---
+id:
+aliases: []
+title: How to Measure Anything in Project Management
+tags: []
+author: Douglas W. Hubbard & Dr. Alexander Budzier & Andreas Bang Leed
+---
+# How to Measure Anything in Project Management
+
+## Foreword
+
+## Preface
+
+## Acknowledgments
+
+## About the Authors
+
+## Chapter 1 --- A World-scale Risk and a World-scale Opportunity
+
+### The Size of Projects
+
+### The Size of Project Problems
+
+### Efforts to Fix Projects: The Emergence of Project Management
+
+### A Path Forward: The Meta-Project
+
+### Notes
+
+## Chapter 2 --- A Measurement Primer for Project Management
+
+### The Concept of Measurement
+
+#### A Definition of Measurement
+
+#### Definition
+
+#### Measurement and Probabilities for Practical Decision-making
+
+#### Are Scales Really Measurements?
+
+### The Object of Measurement
+
+#### What Do You See When You See More of It?
+
+#### Why Do You Care?
+
+### The Methods of Measurement
+
+#### Statistical Significance: What's the Significance?
+
+#### Small Samples Tell You More Than You Think
+
+#### The Rule of Five
+
+### Other Sources of Measurement Aversion
+
+#### The Cost Objection
+
+#### Measurements Change What Is Being Measured
+
+#### Statistics Can Prove Anything
+
+#### Ethical Objections to Measurement
+
+### Notes
+
+## Chapter 3 --- How We Know What Works
+
+### Skepticism for Project Managers
+
+#### The Analysis Placebo
+
+#### The Problem of Feedback and Learning
+
+### How to Test Methods
+
+#### Controlled Experiments and Component Testing
+
+#### Evaluating Sources
+
+### The Performance of Quantitative Methods
+
+#### Experts Versus Algorithms
+
+#### The <span class="class_s9dd">Exsupero Ursus</span> Fallacy: Algorithm Aversion
+
+#### Potential Reasons for <span class="class_s9dd">Exsupero Ursus</span>
+
+### Improving the Human Expert
+
+#### Calibrating the Expert
+
+#### The Expert Consistency Component
+
+#### Collaboration on Estimates
+
+#### The Decomposition Component
+
+### A Summary of Research on Other Project Planning and Management Methods
+
+#### Reference Class Forecasting
+
+#### Various Project Management Methods
+
+#### The Performance of Monte Carlo Simulations
+
+### Notes
+
+## Chapter 4 --- The Project Decision Model: The Reason for Measurements
+
+### Two Types of Project Measurements
+
+#### Proto-purpose Discovery Measurements
+
+#### Decision-driven Measurements
+
+#### Unproductive Incentives vs. Measurements
+
+### Decisions Before: Thinking Slow
+
+#### Exploration vs. Exploitation
+
+#### Tracking the Outside World
+
+#### Choosing How to Run the Project
+
+### How Models Indicate What to Measure
+
+#### The Expected Value of Information: A Simple Introduction
+
+#### The Measurement Inversion: Measuring the Wrong Things
+
+#### The Value of Imperfect Measurements
+
+### An Aspirational Model
+
+#### The Rise of Digital Twins
+
+#### Digital Twins in Project Management
+
+#### A Practical Path Forward
+
+### Notes
+
+## Chapter 5 --- Project Uncertainty and Risk: A Primer
+
+### Basic Concepts and Definitions
+
+#### Uncertainty as a Probability Distribution
+
+#### Risk: A Special Case of Uncertainty
+
+#### Definitions for Uncertainty, Risk, and Their Measurements
+
+### The Problem with Current Methods
+
+#### Why Risk "Scores" Don't Work
+
+#### How the Risk Matrix Makes Scores Worse
+
+### A Quantitative Risk Model: Starting <span class="class_s9dd">Very</span> Simple
+
+#### The One-for-One Substitution
+
+#### Monte Carlo Mechanics: A Brief Introduction
+
+### Supporting Decisions
+
+#### A Return on Mitigation
+
+#### How Much Risk Do You Tolerate?
+
+#### Risk Versus Return: The Powerful Theory of Utility
+
+### Simple Tools for Measuring Uncertainty and Risk
+
+#### A First Estimate of a Discrete Probability
+
+#### A First Estimate of a Continuous Probability
+
+### Final Clarifications
+
+#### Case Examples for What Probability Means
+
+#### Uncertainty Versus Risk Versus Opportunity
+
+#### Epistemic Versus Aleatory Uncertainty
+
+#### Even More Ordinal Scales
+
+#### Risk as Governance or Compliance
+
+#### The Problem of "Black Swans"
+
+#### Some Items That Aren't Really Risks
+
+### More Improvements to Come
+
+### Notes
+
+## Chapter 6 --- Calibrated Subjective Probability Estimates
+
+### Introduction to Subjective Probability
+
+#### Two Extremes of Subjective Confidence
+
+### Calibration Exercise
+
+#### The Calibration Exercises
+
+#### Evaluating Performance and Typical Results
+
+### Improving Calibration
+
+#### The Equivalent Bet
+
+#### More Techniques
+
+#### More Advanced Calibration Topics to Come
+
+### The Effects of Calibration
+
+### Conceptual Obstacles to Calibration
+
+#### Conflating Uncertainty with Knowing Nothing
+
+#### Hypotheses That Contradict the Data
+
+#### Objections Based on the Philosophical Debate in Statistics
+
+### Notes
+
+## Chapter 7 --- Cost and Schedule Measurements
+
+### The Big Plan Versus Iteration: Meta-measurements of Common Estimation Methods
+
+### Top-down Estimations: Reference Class Forecasting
+
+### Bottom-up Forecasting with Monte Carlo
+
+#### A Deterministic View of Tasks
+
+#### Probability Distributions for Project Tasks
+
+#### Correlations
+
+#### Multiple Prerequisites and Stochastic Critical Paths
+
+#### Parade of Trades
+
+### Comparing Top-Down and Bottom-Up: Case Examples
+
+#### The Swedish Nuclear Waste Program
+
+#### High-speed Rail
+
+### How to Improve the Models
+
+#### The Granularity of the Monte Carlo Model
+
+#### Distributions and Biases
+
+#### Correlations
+
+#### Improving the RCF with Monte Carlo
+
+### Notes
+
+## Chapter 8 --- Betting on Benefits
+
+### Meta-Measurements of Benefits
+
+#### How Much Should Benefits Be to Justify a Project?
+
+#### Why This May Be Optimistic
+
+#### Why Measuring Benefits Is Rare
+
+### Fermi Decompositions for Benefits
+
+#### Introduction to Fermi
+
+#### Some Example Decompositions
+
+### Monetizing Benefits
+
+#### Forecasts of Monetary Impacts
+
+#### Preferences
+
+#### Quantifying Preferences
+
+#### The Use of Scores and Multiple Objectives
+
+#### An Example of Challenging Benefit Measurement: Biodiversity
+
+### Measuring What Matters in Projects
+
+#### A (Slightly) More Realistic Information Value Calculation
+
+#### The High Information Values for Projects
+
+#### Getting Started on Measuring What Matters
+
+### Considering Risk and Return
+
+#### A Risk Neutral Decision-maker for Projects
+
+#### Adding Utility Theory to Projects
+
+#### Some Alternatives Within Utility Math
+
+#### Are Executives Too Risk Averse for Projects?
+
+#### The (Apparent) Utility Paradox
+
+### A Framework and Its Consequences
+
+#### Findings from Quantitative Analysis of Past Projects
+
+#### How and When, Not Just Whether
+
+#### Benefits Are Not Just for Project Approval Decisions
+
+### Notes
+
+## Chapter 9 --- Measuring Progress
+
+### The Progress Problem
+
+#### Simple Progress, Simple Interventions
+
+### Earned Value Management
+
+#### EVM Basics
+
+#### The XRL Example
+
+#### Recovery vs. Performance
+
+#### Forecasting with EVM
+
+### Progress in Information Projects
+
+#### Waterfall
+
+#### Agile and Measurement in Other Software Development Methods
+
+#### Summarizing Software Metric Difficulties
+
+### Four Stories and Lessons
+
+#### Interfaces in a Global Bank Transformation
+
+#### An Energy Project Front End
+
+#### Construction Constraints
+
+#### Testing as Software Checkpoints
+
+#### Lessons
+
+### The Remaining Project Simulation
+
+#### Conditional Reference Class Forecasting (CRCF)
+
+#### The Bottom-Up Simulation for the Remaining Project
+
+#### Further Considerations for the RPA
+
+### Notes
+
+## Chapter 10 --- More Measurement Methods Made Easy
+
+### Intuition for the Habitually Scientific
+
+#### A Jelly Bean Example
+
+#### A Little Probability Theory
+
+#### Consequences of Probability Theory
+
+#### Myths Exposed by Probability Theory
+
+#### Significant Points About Statistical Significance
+
+#### Basic Sampling Methods
+
+#### The "Mathless" Table for Medians
+
+#### Estimating a Population Proportion
+
+### Project Cancellation Rates as a Function of Duration
+
+#### Measuring Population Size
+
+### Measuring Some Things by Knowing Other Things
+
+#### Controlled Experiments
+
+#### Regression
+
+#### More Advanced Methods of Regression and Classification
+
+### Estimating the Whole Distribution
+
+### Summarizing Methods
+
+#### Brainstorming a Measurement Approach
+
+#### Data Gathering Methods
+
+### A Review of Methods in This Chapter
+
+### Notes on Surveys
+
+### Notes
+
+## Chapter 11 --- The Meta-Project: Implementing Better Project Measurements
+
+### Start with the End in Mind: The Continuous Improvement Process
+
+#### Measure What Matters
+
+#### (Real) Skepticism and Meta-measurements
+
+#### Measuring and Forecasting the Outside World
+
+#### AI: The Most Important Project Ecosystem Measurement?
+
+#### More Thinking, Fewer Projects, Bigger Wins
+
+### Start Your Meta-Project
+
+#### Examples of Meta-Projects Deliverables: Continuous Improvement
+
+#### Develop an Initial Team
+
+#### Assess the Current State of the Project Portfolio
+
+#### Considerations for the Meta-Project Plan
+
+#### The Pilot Project
+
+#### Scaling to the Final Deliverable
+
+### Organizational Challenges
+
+#### Resistance to Change
+
+#### Addressing Organizational Objections to Measurement
+
+#### The Politics of Measurement
+
+### Notes
+
+## Chapter 12 --- A Call to Action for the Industry
+
+### Call to Action for Project Software Vendors
+
+#### Put Decisions at the Center
+
+#### Deal in Uncertainties
+
+#### Build the User-buyer-builder Federation
+
+#### Be the Vendor That Measures Its Performance
+
+#### Be Forward-Looking
+
+### Call to Action for the Standard-Setting Bodies
+
+### Call to Action for Consultants, Researchers, and Advisory Firms
+
+### Big Future Projects
+
+#### A Mars Mission
+
+#### Stopping Hurricanes
+
+#### The Meta-Project
+
+### Notes
+
+## Appendix 1 --- Analysis of Survey Responses on Project Management Practices
+
+### Introduction and Data Overview
+
+#### Success Metrics: Cost and Schedule Overrun Ratios
+
+### Overview of Project Management Practices Reported in the Survey
+
+#### Project Management Methodologies
+
+#### Cost and Schedule Estimation Methods
+
+#### Uncertainty and Risk Assessment Tools
+
+#### Certifications
+
+### Results
+
+#### Project Management Methodologies
+
+#### Cost and Schedule Estimation Methods
+
+#### Uncertainty and Risk Assessment Tools
+
+#### Certifications
+
+#### Interpreting the (Mostly) Statistically Insignificant Results
+
+## Appendix 2 --- Reference Class Data on Project Cost, Schedule, and Benefit Overruns
+
+### Relevance of the Data and Reference Class Forecasting
+
+### Using Historical Data to Improve Estimates -- An Example
+
+### Notes
+
+## Appendix 3 --- Selected Distributions
+
+### Uniform
+
+### Beta
+
+### Beta PERT
+
+### Triangular
+
+### Binary
+
+### Normal
+
+### Lognormal
+
+### Power Law
+
+### Truncated Power Law
+
+### Quantile-parameterized
+
+### Gamma Poisson
+
+### Stochastic Information Packet
+
+## Appendix 4 --- Chapter 6 Calibration Question Answers
+
+### Appendix Answers to Confidence Interval Questions
+
+### Answers to True/False Questions
+
+## Appendix 5 --- Measuring Biodiversity
+
+### The Benefits of Biodiversity
+
+### Variables to Measure for Biodiversity
+
+### Notes
diff --git a/timestamped/2026-04-06_16-23-54.md b/timestamped/2026-04-06_16-23-54.md
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+---
+id: 2026-04-06T16:23:54-04:00
+aliases: []
+title: 2026-04-06 16:23:54
+tags:
+  - authorship/original
+  - destiny/permanent
+  - status/draft
+  - type/periodic/timestamped
+dg-publish: true
+date-created: 2026-04-06T16:23:54-04:00
+daily: "[[2026-04-06]]"
+weekly: "[[2026-W15]]"
+monthly: "[[2026-04]]"
+quarterly: "[[2026-Q2]]"
+yearly: "[[2026]]"
+---
+# 2026-04-06 16:23:54
+
+[tksheet](https://pypi.org/project/tksheet/) is a spreadsheet library for tkinter.
+
+It's likely a streamlined `takeoff-calcs.xlsx` clone
+could be made easily with `tksheet`,
+and experimental estimating patterns
+like my graph editor `edger`
+could be added as plugins.
diff --git a/timestamped/2026-04-06_16-47-44.md b/timestamped/2026-04-06_16-47-44.md
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+---
+id: 2026-04-06T16:47:44-04:00
+aliases: []
+title: 2026-04-06 16:47:44
+tags:
+  - authorship/original
+  - destiny/permanent
+  - status/draft
+  - type/periodic/timestamped
+dg-publish: true
+date-created: 2026-04-06T16:47:44-04:00
+daily: "[[2026-04-06]]"
+weekly: "[[2026-W15]]"
+monthly: "[[2026-04]]"
+quarterly: "[[2026-Q2]]"
+yearly: "[[2026]]"
+---
+# 2026-04-06 16:47:44
+
+## Nirvana Fallacy
+
+The [nirvana fallacy](https://en.wikipedia.org/wiki/Nirvana_fallacy)
+also called the "perfect solution fallacy"
+is the mistake made when a solution proposed to replace one existing
+is judged against a hypothetical perfect solution
+rather than the solution in current use.
+
+> [!quote] Harold Demsetz "Information and Efficiency: Another Viewpoint" (1969)[^1]
+> The view that now pervades much public policy economics
+> implicitly presents the relevant choice
+> as between an ideal norm and an existing "imperfect" institutional arrangement.
+> This _nirvana_ approach differs considerably
+> from a _comparative institution_ approach
+> in which the relevant choice is between alternative real institutional arrangements.
+
+[^1]: Harold Demsetz
+    "Information and Efficiency: Another Viewpoint"
+    _The Journal of Law and Economics_
+    Volume 12, Number 1
+    (April 1969)
+    [doi:10.1086/466657](https://doi.org/10.1086%2F466657)
+
+The "_[[the-failure-of-risk-management#_Exsupero Ursus_|exsupero ursus]]_ fallacy"
+as coined by Douglas Hubbard in [[hubbard_2020_failure]][^2]
+describes the same problem.
+
+[^2]: Or earlier? I can't recall.