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 title: 2025-12-16
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 # 2025-12-16
 ## 2025-12-16 09:20
 [Probability Management](https://www.probabilitymanagement.org/)
 [Handbook of Decision Analysis | Wiley Online Books](https://onlinelibrary.wiley.com/doi/book/10.1002/9781118515853)
@@ -15,7 +15,13 @@ This is the commentary companion to [[taleb_2001_fooled-by-randomness]].
 ## Critiques
-### "Logic Without Statistics"
+### Intellectual Insecurity
 So far I've gotten the distinct impression
 that statistics hurt Taleb's head,
 and he is intimidated by academics.
 #### "Logic Without Statistics"
 FbR uses few citations,
 relying on the strength of Taleb's logic alone, by his own stating.
@@ -38,7 +44,20 @@ when _Taleb_ is wrong, I question the foundation of all his arguments.
 Less politely, I wonder why I'm listening to him just make up justifications
 for what he already believed.
-### Qualitative Probability
+So far I'm lead to believe what Taleb means by "logic"
 is only anecdotes and aphorisms.
 He wants to be Plato, but he comes off as "this came to me in a dream".
 > [!quote] Chapter 2? (pp.)
 > Scientists can not meaningfully describe the probability of black swans
 > because it would require observing the future.
 > [!quote] Chapter 2? (pp.)
 > Accountants don't care about probability,
 > if they did they wouldn't be accountants,
 > and if they were they would make an error on your tax returns.
 #### Qualitative Probability
 Taleb loses me in the introduction
 when he states that he defines _probability_ qualitatively.
@@ -47,3 +66,56 @@ when he states that he defines _probability_ qualitatively.
 of the terms **uncertainty**, **probability**, and **risk**,
 Later it's clear he what he means by probability is **uncertainty**.
 > [!quote] Chapter 2? (pp.)
 > Certainty is something likely to occur in the largest number of possible worlds,
 > uncertainty concerns what is unlikely to occur in many possible worlds.
 #### The Black Swan
 **The black swan**, or the unforeseen event,
 is the idea that no quantitative risk management is possible
 because of the possibility of a single loss
 that would negate all previous gains.
 Hubbard points out that Taleb's position is paradoxical.
 > [!quote] [[hubbard_2020_failure#A Note About Black Swans]]
 > ...he is assessing the validity of using historical examples
 > by using _historical examples_.
 Besides its credibility, the suggestion reeks of intellectual insecurity.
 It is very convenient to dismiss the whole of statistics based on logic alone,
 much more so to dismiss the tests used to prove the validity of statistical methods.
 ### False Lucky Fools
 Taleb repeatedly conflates legitimate lucky fools
 with people with ideas he doesn't like.
 > [[hubbard_2020_failure]]
 > does a much better job of explaining "lucky fools"
 > (see [[the-failure-of-risk-management#Red Baron Effect]]).
 #### Nero Tulip vs. John
 Taleb uses the story of Nero Tulip,
 an incredibly cautious investor,
 and his success over his risk-loving rival John
 to promote the idea that modern quantitative methods
 (as John is presumed to represent)
 are inherently flawed.
 One of many problems with this presentation
 is that John is described as young, inexperienced,
 and of low intelligence.
 John is a typical lucky fool:
 there is no indication that he has any strategy,
 much less that he is practicing modern portfolio theory.
 If I mistake Taleb's intent,
 and the story was only meant to convey
 that experienced, conscientious, and cautious decision-making
 can lead a person to success,
 then I'm not sure who he's arguing against.
@@ -150,6 +150,257 @@ For commentary see the companion
 #### A Note About Black Swans
 The *exsupero ursus* fallacy is reinforced by authors of very popular books
 who seem to depend heavily on some version of the fallacy.
 One such author is former Wall Street trader and mathematician Nassim Taleb.
 He wrote *The Black Swan* and other books critical of common practice in risk management,
 especially in (but not limited to) the financial world,
 as well as the nonquantitative hubris of Wall Street.
 A heretic of financial convention, he argues that Nobel Prize---
 winning modern portfolio theory and options theory (briefly mentioned in chapter 5)
 are fundamentally flawed and are in fact no better than astrology.
 In fact, Taleb considers this prize is itself an intellectual fraud.
 After all, as he rightly points out, it was not established in the will of Alfred Nobel,
 but by the Royal Bank of Sweden seventy-five years after Nobel's death.
 He even claims that once, in a public forum, he riled up one such prizewinner
 to the point of red-faced, fist-pounding anger.
 Taleb bases a lot of his thesis on the fact that the impact of chance
 is unappreciated by mostly everyone.
 He sees the most significant events in history as being completely unforeseeable.
 He calls these events *black swans* in reference to an old European expression
 that went something like "That's about as likely as finding a black swan."
 The expression was based on the fact that no European
 had ever seen a swan that was black---until Europeans traveled to Australia.
 Until the first black swans were sighted, black swans were a metaphor for impossibility.
 Taleb puts September 11, 2001, stock market crashes, major scientific discoveries,
 and the rise of Google in his set of black swans.
 Each event, he argues, was not only unforeseen
 but *utterly unforeseeable* based on our previous experience.
 People will routinely confuse luck with competence
 and they will presume that the lack of seeing an unusual event to date
 is somehow proof that the event cannot occur.
 Managers, traders, and the media seem to be especially susceptible to these errors.
 Out of a large number of managers,
 some managers will have made several good choices in a row by chance alone.
 This is what I called the Red Baron effect in a previous chapter.
 Such managers will see their past success
 as indicators of competence and, unfortunately,
 will act with high confidence on equally erroneous thinking in the future.
 Taleb recognizes the problems of overconfidence researched by Kahneman and others.
 Indeed, Taleb says Kahneman is the only Economics Nobel Prize winner he respects.
 I think part of Taleb's skepticism is refreshing and on point.
 I agree with many of Taleb's observations on the misplaced faith in some models
 and will discuss this further in the next chapter.
 I might even include Taleb as one source of inspiration
 for identifying new categories of fallacies
 (and giving it a Latin name in order to sound official).
 Taleb coined a fallacy he refers to as the *ludic fallacy*,
 derived from the Latin word for "games of chance."
 Taleb defines the ludic fallacy as the assumption that the real world
 necessarily follows the same rules as well-defined games of chance.
 Now, here is where Taleb errs.
 He doesn't just argue that risk management is flawed.
 He argues that risk management itself is impossible
 and that all we can do is make ourselves *antifragile*.
 I think he is just using a very different definition of risk management---
 which even he uses inconsistently.
 No matter what he calls it, he is promoting a particular set of (vaguely defined) methods
 that have the objective of reducing risk.
 This reduction in risk will require resources.
 Using the definition I propose in [chapter 6],
 determining how to use resources to reduce risk is part of risk management.
 He actually contradicts himself on this point
 when he promotes redundancy as a method of becoming antifragile
 and refers to it as the "central risk management property of natural systems."
 So, yes, we are both talking about risk management.
 He focuses on particular approaches to it, but it is risk management just the same.
 Confusion and inconsistency about whether managing fragility is, in practice,
 part of managing risks is not the only problem in his thesis.
 Taleb commits every form of the *exsupero ursus* fallacy
 throughout most of what he writes.
 Specifically,
 (1) he presumes the lack of perfection of one model
 automatically necessitates use of the other regardless of relative performance,
 (2) he commits the anecdotal fallacy
 when looking for evidence of relative performance, and
 (3) he presumes that a given model was even being used
 when he identifies them as the culprit in major risk events.
 In an interview for *Fortune* Taleb claimed,
 "No model is better than a faulty model."
 Again, having no model is never an option.
 One way or another, a model is being used.
 Taleb's model is simply his common sense,
 which is, as Albert Einstein defines it,
 "merely the deposit of prejudice laid down in the human mind before the age of eighteen."
 As with every other model, common sense has its own special errors.
 We've seen the research that shows overwhelming evidence
 of the flaws of unaided intuition compared to even simple statistical models,
 and Taleb offers no empirical data to the contrary.
 Taleb does briefly mention the work of Meehl but dismisses it.
 Without making any mention of the huge numbers
 of conclusive results by Meehl and his colleagues,
 Taleb claims the entire body of research is invalid
 by claiming "that these researchers did not have a clear idea
 of where the burden of empirical evidence lies"
 and goes on to suggest that they lacked "rigorous empiricism."
 He offers no details about how more than one hundred peer-reviewed,
 published studies by several researchers veers from the required rigorous empiricism.
 Kahneman, who actually is a psychologist like Meehl,
 would apparently disagree with Taleb on Meehl's methods.
 Taleb considers Kahneman a significant influence on his work,
 but who does Kahneman consider to be a significant influence on his work?
 <u>Meehl</u>.
 I wouldn't presume to speak for Kahneman
 but I wonder if he might point out to Taleb
 how the burden of proof was accepted and met overwhelmingly by Meehl,
 whereas Taleb's evidence merely amounts to, at best,
 selected anecdotes of shortcomings or entirely imagined straw man arguments.
 Taleb even sometimes cites the work of Phil Tetlock
 to support some other point he makes
 but never references Tetlock's enormous twenty-year study
 where he concluded that it was "impossible"
 to find a domain where humans clearly outperformed algorithms.
 Instead of relying on large controlled studies,
 Taleb commits the error of arguing that single events
 effectively disprove a probabilistic model.
 He uses the apparent unforeseeability of specific events
 as evidence of a flaw in risk analysis.
 The implication is that if quantitative analysis worked,
 then we could make exact predictions of specific and extraordinary events
 such as 9/11 or the rise of Google.
 When arguing against the use of various statistical models in economics
 he states that "the simple argument that Black Swans and tail events
 run the socioeconomic world---and these events cannot be predicted---
 is sufficient to invalidate their statistics."[^09-12]
 Yes, the rare events---black swans---
 are individually impossible to predict precisely.
 But unless he can show that his alternative model (apparently his intuition)
 would also have predicted such events exactly,
 then he commits *exsupero ursus* when he says imperfection alone
 is sufficient to prefer intuition over statistics.
 In addition to Kahneman,
 it is worth pointing out others whose work Taleb cites to make a point but who,
 if you actually looked at what they are doing, would contradict Taleb.
 For example, Taleb says he admires the mathematician Edward Thorp,
 who developed a mathematically sound basis for card counting in blackjack in the 1960s.
 Now, if the objective of card counting was to predict every hand,
 even the most extraordinarily rare combinations as Taleb would seem to require,
 then Ed Thorp's method certainly fails.
 But Ed Thorp's method works---that's why the casinos quit letting him play---
 because his system resulted in better bets on average after a large number of hands.
 Taleb is also a fan of the mathematician Benoit Mandelbrot,
 who used the mathematics of *fractals* to model financial markets.
 Similar to Thorp and Taleb,
 Mandelbrot was equally unable to predict specific extraordinary events exactly,
 but his models are preferred by some
 because they seem to generate more realistic patterns
 that look like they *could* be from real data.
 If anecdotal evidence were sufficient to compare model performance,
 one could simply point out that Taleb's investment firm, Empirica Capital LLC,
 closed in 2004 after several years of mediocre returns.[^09-13]
 He had one very good year in 2000 (a 60 percent return)
 because while everyone else was betting on dot-com, he bet on *dot-bomb*.
 But the returns the following years were far enough below the market average
 that the good times couldn't outweigh the bad for his fund.
 Similar to the news pundits rejecting Nate Silver's findings
 or the sportscasters rejecting the methods used by the Oakland A's,
 Taleb merely shows that it is possible to find an error in a model
 if one looks hard enough.
 Again, the question is not whether to model (intuition is a model, too)
 or whether one model is imperfect (both models are imperfect)
 but which measurably outperforms the other
 and does so in many trials not just single anecdotes.
 Finally, Taleb makes the error of presuming what methods
 were actually being used when he blames them for an event.
 He argues, for example,
 that the downfall of long-term capital management (LTCM) disproves options theory.
 Recall that options theory won the Nobel Prize for Robert Merton and Myron Scholes,
 both of whom were on the board of directors for LTCM.
 The theory was presumably the basis of the trading strategy of the firm.
 But an analysis of the failure of LTCM shows that a big reason for its downfall
 was the excessive use of leverage in trades---an issue that isn't even part of options theory.
 That appeared to be based on intuition.
 Taleb also states that the crash of 1987 disproved modern portfolio theory (MPT),
 which would seem to presume
 that at least some significant proportion of fund managers used the method.
 I find fund managers to be tight-lipped about their specific methods,
 but one fund manager did tell me how
 "learning the theory is important as a foundation
 but 'real-world' decisions have to be based on practical experience, too."
 In fact, I found no fund managers who didn't rely partly, if not mostly, on intuition.
 Finally, if we are looking for explanations of the mortgage crisis,
 neither MPT nor options theory had anything to do with the practice
 of giving out mortgages to large numbers of people lacking the ability to pay them.
 That was more of a function of a system
 that incentivized banks to give risky loans without actually accepting the risk.
 Finally, Taleb seems to make a variety of other points
 that, similar to the previous points, seem so inconsistent
 he ends up undermining the point he makes.
 For example, explaining the outcomes
 in terms of the narrative fallacy committed by others
 is sometimes itself a narrative fallacy.
 Arguing that "experts" don't know so much is not supported by quoting other experts.
 He argues that rare events defy quantitative models,
 but then gives specific examples of computing rare events with quantitative models
 (he shows the odds of getting the same result in a coin flip many times in a row
 and argues the benefits of Mandelbrot's mathematical models
 in the analysis of market fluctuations).
 Taleb criticizes the use of historical data in forecasts
 but apparently sees no irony in his argument.
 He looks at several examples in which history was a poor predictor.
 In other words, he is assessing the validity of using historical examples
 by using *historical examples*.
 What Taleb and others prove with such examples
 is merely that what I will call a *naive* historical analysis can be very misleading.
 Taleb demonstrates his point by using the example of a turkey.
 The turkey had a great life right up until Thanksgiving.
 So, for that turkey, history was a poor indicator.
 So how is Taleb able to see this problem?
 He simply looks at the larger history of turkeys.
 All he is doing is using what we may call a *history of histories*,
 or *meta-historical analysis*, to show how wrong naive historical analysis can be.
 The error in historical analysis in a stock price, for example,
 is to look only at the history of *that* stock and only for recent history.
 If we look at all historical analysis for a very long period of time,
 we find how often naive historical analysis can be wrong.
 Taleb's own "experience," as extensive as it might be (at least in finance),
 is also just a historical analysis---just a very informal type
 with lots of errors in both recall and analysis, as shown in [chapter 7].
 No thinking person can ever honestly claim
 to have formed any idea totally independent of previous observations.
 It just doesn't happen.
 Even Taleb's ludic fallacy seems to be a fallacy itself.
 Sam Savage calls it the "ludic fallacy-fallacy."
 As Savage describes it, we cannot rationally address real-world problems of uncertainty
 "*without* first understanding the simple arithmetic of dice, cards, and spinners."
 Of course, Taleb is right when he says we shouldn't *assume*
 that we have defined any problem perfectly.
 That certainly would be an error, and if that were Taleb's point, that would be valid.
 But, again, whether a particular model is perfect is not the right question.
 The most relevant question is whether a probabilistic model---even a simple one---
 outperforms the alternative model, such as intuition.
 #### Major Mathematical Misconceptions
 #### We're Special: The Belief That Risk Analysis Might Work, But Not Here
@@ -182,6 +433,14 @@ For commentary see the companion
 #### Using Data For Initial Benchmarks
 ##### It's Been Measured Before
 ##### You Have More Data Than You Think
 ##### You Need Less Data Than You Think
 ##### A Reference Class Error: Revisiting the Turkey
 #### Checking The Substitution
 #### Simple Risk Management
@@ -11,7 +11,33 @@ title: Lighting Controls
 ---
 # Lighting Controls
-## Technologies
+## Occupancy/Vacancy Sensor Technologies
 * Passive Infrared (PIR)
 * Ultrasonic
 * "Dual Tech" (PIR and ultrasonic)
 ## Switching/Communication
 * Occupancy
 * Vacancy
 * Daylight
 ### Line Voltage
 120--347VAC
 ### Low Voltage
 24V Class 2 control circuit
 ### Digital
 Digital Light Management (DLM)
 [[twisted-pair-cable]]
 ## Dimming Technologies
 * Triac (Line voltage dim)
 * Analog (0-10V dim)
@@ -30,7 +30,8 @@ title: Multifamily Dwellings
 > but this term is ambiguously understood.
 > 
 > The question _does not_ apply
-> to hotels with three or more suites with full kitchens.
+> to hotels with three or more guestrooms with full kitchens
 > (often called "suites").
 > Such hotels are always multifamily dwellings.
 Whether hotel rooms are dwelling units
@@ -105,6 +105,16 @@ equal to the mean squared error of their forecasts.
 ### Luck Looks Like Skill
 Individually improbable events
 become likely with increased sampling.
 This the unstated other half of the **law of large numbers**
 > How many success stories
 > are simply cases of winning a coin flipping tournament?
 #### Red Baron Effect
 > [!cite] Chapter 7 p.154 (pp.)
 > Hubbard describes a study which concluded that,
 > given the number of German pilots and their overall victory/defeat figures,
@@ -116,12 +126,6 @@ to overvalue competence and undervalue luck
 in the role of achieving improbable accomplishments
 as the "Red Baron effect".
 This the unstated other half of the **law of large numbers**:
 improbable events become likely with increased sampling.
 > How many success stories
 > are simply cases of winning a coin flipping tournament?
 ### There's Always Enough Data
 > [!quote] Voltaire