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the-failure-of-risk-management.md

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+---
+id: the-failure-of-risk-management
+aliases: []
+tags:
+  - media-commentary
+---
+# *The Failure of Risk Management*
+
+The Failure of Risk Management
+(Why It's Broken and How to Fix It)
+by Douglas W. Hubbard
+
+## Key Takeaways
+
+### Qualitative Metrics Must Be Avoided
+
+Qualitative risk analysis
+(i.e. risk matrices, scoring charts)
+departs from legitimate statistical methodology
+and has no robust evidence to suggest its efficacy.
+There is good reason to believe that such methods
+are deleterious to their intended purpose
+in contradiction to the common response
+that they are "better than nothing".
+
+### Utility as a Measure of Value
+
+Expected Value (Probability × Magnitude)
+alone can not predict or inform risky decisions,
+except for risk-neutral parties.
+People and organizations are risk-averse
+...
+
+* [ ] Finish this paragraph.  (see chapter 6) ➕ 2025-07-04
+
+### Expert Opinion Must Be ~~Adjusted~~
+
+Expert opinion is valuable despite its flaws.
+...
+
+The book details the statistically observable tendency for people
+to underestimate risk and to be overconfident in their beliefs.
+It describes the process of "calibration"
+by which people can be trained to compensate for this bias
+and make predictions far more accurately.
+
+Experts tend to be good at creating heuristics,
+but do not apply them consistently in practice.
+
+> [!example]
+> Chapter 7 describes a study where experts
+> were shown to estimate risk differently for identical cases.
+
+### Luck Looks Like Skill
+
+> Chapter 7 p.154
+
+Hubbard describes a study which concluded that,
+given the number of German pilots and their overall victory/defeat figures,
+there was a ~30% chance an individual would achieve The Red Baron's record
+_by luck alone_.
+
+He later refers to the tendency to overvalue competence
+in the role of achieving improbable accomplishments
+as the "Red Baron effect".
+
+### There's Always Enough Data
+
+Hubbard challenges the popular rebuttal
+that X industry lacks the data to use quantitative models.
+
+> [!quote] Fallacy of Close Analogy - p.236
+> ...the belief that unless two things are identical in every way,
+> nothing learned from one can be applied to the other.
+
+## Mentioned Topics and Abbreviations
+
+* Analytic Hierarchy Process (AHP)
+* Multi-Attribute Utility Theory (MAUT)
+* Actuarial Science
+* Options Theory (OT)
+* Modern Portfolio Theory (MPT)
+* Probabalistic Risk Analysis (PRA)
+* Value at Risk (VaR)
+* Loss-Exceedance Curve (LEC)
+* Risk Tolerance
+
+## Critiques
+
+### _Exsupero Ursus_
+
+> Chapter 9 p.195
+
+Hubbard uses _exsupero ursus_ to describe the tendency of his detractors
+to dismiss quantitative methods as inappropriate to their industry-specific risks.
+He provides another analogy in which one car is picked of two (ordinary) cars 
+because the other car can't fly.
+Based on this strawman it is clear Hubbard believes his detractors are _correct_
+that qualitative methods can not capture the entire nuance of risk probability,
+but that they are failing to acknowledge that their preferred alternatives
+are not demonstrably more effective at doing so.
+The nuance Hubbard dismisses without addressing is the possibility of model _improvement_.
+A most competant detractor would be aware of the apparent contradiction
+but argue that their methods will eventually surpass quantitative methods
+if they are further developed.
+Such a position would additionally contextualize Hubbard's observations
+that detractors become emotional in their defense.
+To them, Hubbards methods represent an attractive short-term gain
+excluding a long-term payoff.
+
+Hubbard's dismissal rubs me wrong
+because it reads exactly as he describes the "at least we're doing _something_" argument
+throughout the book and just pages earlier.