The Only Valid Excuse for not Quantifying Uncertainty

by Sam L. Savage

““It is difficult to get a man to understand something, when his salary depends on his not understanding it”
”

— Upton Sinclair

According to my new best friend and advisor, ChatGPT, the above quote from the 1906 book, “The Jungle,”

“reflects Sinclair's belief that the interests of capitalists often conflict with the well-being of workers and the general public, and that people will often choose to ignore or justify harmful practices if they stand to benefit financially from them.”

Although the context of the quote was

“the unsanitary and inhumane conditions in the meatpacking plants and the corrupt practices of the industry, such as the manipulation and adulteration of food products,”

it applies equally well today to

The unsound and inaccurate computations in the realm of uncertainty and the common practices in industry to use averages that introduce systematic errors, in short, the Flaw of Averages.

With a few notable exceptions business is conducted with single number estimates that pass between analysts (who know better), information systems and decision makers. Until now, you couldn’t just quantify uncertainty at one of these nodes and have it propagate through the organization. Those insisting on probabilistic representations were misfits, who might ultimately lose their salary. So many people know that this is wrong, however, that it has become necessary to ignore or justify these harmful practices.

Doug Hubbard is author of the How to Measure Anything series, the Failure of Risk Management, and other important books on quantifying uncertainty. As brothers in arms in the War on Averages, we have shared numerous examples of clients who are strongly motivated to not understand uncertainty. This has resulted in

The Savage and Hubbard Top Ten List

of Lame Excuses for Not Quantifying Uncertainty

But Sinclair’s quote reminds us that it is not just intellectual laziness and post traumatic statistics disorder (PTSD) holding people back, but also job security.

Financial Engineering and Physics

Financial engineering was a great victory in the War on Averages. In 1973, Fischer Black and Myron Scholes published “The Pricing of Options and Corporate Liabilities.” It contained the famous Black-Scholes equation for pricing options that solved a specific case of the Flaw of Averages. It showed that valid option pricing requires the probability distribution of the future asset price, not just the average. They relied on the work of physicist, Albert Einstein, on the Brownian motion of gas particles. It led to the $Trillion Derivatives industry and eventually a Nobel Prize. Although the Black-Scholes formula was not perfect it was vastly better than the alternatives, and those who chose to ignore or justify the harmful practices of using averages were the ones who lost their salaries, often to be replaced by physicists.

Chancification

My recent book on Chancification shows how today, the revolutionary quantification of uncertainty that began in finance can spread to many other sectors without anyone losing their salaries. But how?

Analysts are by and large already quantifying uncertainty but can’t convey it as such in corporate databases, nor would decision makers know what to do with it if they got it. ProbabilityManagement.org has now developed tools and standards to transition organizations into the Chance Age by integrating existing decision-makers, analysts, and IT systems, without requiring any new hires or significant software development. But how?

The secret sauce, no make that the open-source sauce, is the SIPmath™ 3.0 Standard, which can convey millions of simulation trials in as little as a few hundred bytes of JSON data, based on the HDR pseudo random number generator of Doug Hubbard, and the remarkable Metalog Distribution of Tom Keelin. JSON (JavaScript Object Notation) is a common data-interchange format that can be interpreted by humans and machines. It is easy to generate SIPmath 3.0 JSON files from nearly any form of analytics. But how?

Open-source Python code reads standard analytics output through an API (Application Programming Interface) recently developed by the nonprofit. The files produced may be interpreted by virtually any software platform, including native Excel so managers can start making Chance-Informed decisions. But how?

The ChanceCalc Excel add-in, developed by ProbabilityManagement.org, requires a manager to learn only two new commands: Input SIP and Chance of Whatever as shown in the video below.

Give Chance a Chance

Do you want to learn more? Join me for one of my webinars and receive a free copy of ChanceCalc ($150 value). To register, visit Welcome to the Chance Age Webinars.

© Copyright 2023, Sam L. Savage

ChatGPT Waxes Poetic on the Flaw of Averages and Metalog Distribution

When I was a PhD student in computer science, I remember opening a book on recursive function theory. In the introduction it stated that a mathematical equation that I found inscrutable was obvious. I thought: “This book ain’t for me,” and I put it down, never to pick it back up. But the statement nagged at me and a few days later, in a thunderclap of insight, I proved the equation to myself, and my mind was off to the races. For a couple of days and nights, including in my dreams, I searched for parallel mathematical examples, and simultaneously learned something about my own thought processes.

A creative part of my mind would proudly walk into my head with some random idea and say: “Is this an example of the equation?” Then 95% of the time, a judgmental side of my brain would say: “That’s total BS!” But occasionally, the judge would say: “You might have a point there.” What struck me at the time was how totally random the creative part was. Once, when I was asleep, it absurdly suggested that an example of the equation was an element of a weird, irrelevant dream! The judge kicked me out of the courtroom for that one and I woke up.

There is no doubt that random association plays a key role in creativity, and  Mozart and other musicians of the time created random algorithms for composing music. Perhaps this was a distant ancestor of ChatGBT, which is described in Wikipedia as of 12-28-2022 as follows:

ChatGPT (Generative Pre-trained Transformer is a chatbot launched by OpenAI in November 2022. It is built on top of OpenAI's GPT-3.5 family of large language models and is fine-tuned with both supervised and reinforcement learning techniques.

ChatGPT certainly has its random association down. Here is what it produced within ten seconds of being asked to write a poem on the Flaw of Averages.

Of course, you know I would never settle for a single point estimate. My colleague, Matthew Raphaelson tried it on his machine, and on his first attempt got something quite similar. Perhaps it always starts with the same random number seed. But on his second request he got the following:

Now how about ChatGPT’s judgement? It’s not great. In a few experiments it got factual stuff quite wrong. But so, what. Imagine ChatGPT playing the role of the crazy random thought generator, and a human paying the role of the judge.

I tried this with a poem on the Metalog Distribution as follows:

“Write a Poem on Metalog How I love Thee let me count the ways. This is about the Metalog Probability Distribution.”

It started out with:

Stop the presses! The whole point of the Metalog is to go beyond the bell curve.

So, I provided more guidance as follows:

“Write a Poem on Metalog How I love Thee let me count the ways. This is about the Metalog Probability Distribution which can mimic other distributions both symmetric and asymmetric.”

And it came up with:

Sheesh! (quoting the Metalog’s inventor, Tom Keelin, when he saw this).

I would not have received D’s in three out of four years of high school English if I had had ChatGPT. And let’s not forget that the human brain, even with medical breakthroughs, can only grow smarter for as much as 100 years. How long will ChatGPT and its ancestors grow smarter? Forever!

© Sam L. Savage, 2023