Trouble on the Way from Notion to Inference

As a child, my favorite bedtime story was Dr. Seuss’ I Had Trouble in Getting to Solla Sollew.1 I never forgot the story line segment wherein the hero is involuntarily conscripted inside an army, in order to confront the ‘Perilous Poozer of Pomplemoose Pass’. The erstwhile army ends up bailing on the hero and he is left alone, surrounded, and without a real weapon, to fight not one, but many Perilous Poozers.

During a severe market recession a couple decades ago, I was a junior partner in a firm whose principals and owners all bailed on the business and absconded in short order with most all the accounts, clients, and assets. These partners secretly knew that one major client was about to go bankrupt, while another was being acquired and merged. This left me alone to rescue the enterprise, and during a severe recession no less. We were abandoned with a mere two months of backlogged sales, while employees fretted over what was to happen with their jobs, families, and lives. We faced a monthly payroll that was alone twice the size of all backlogged sales. It was a dark time.2

I was quite happy and lived by the ocean
Not far from a place called the Valley of Notion
Where nothing, not anything ever was wrong
Until… well, one day I was walking along…

And I learned there are troubles of more than one kind
Some come from ahead and some come from behind
There I was, all completely surrounded by trouble,
When a chap rumbled up in a One-Wheeler Wubble

“Young Fellow,” he said, “what has happened to you
has happened to me and to other folks, too
What I’ve decided to do is to think in more sense…
So I’m off to the City of True Inference

I was able leverage my house and retirement accounts, borrow money and time, change our market message and approach, and through an intense road campaign, raise new business to replace the old – and not let a single employee down through forfeiture of their job. We even brought the company back to equal its heights of record business – selling the business at a premium nine times earnings years later. I also ensured that the employees who stuck with the business were rewarded well in that sale. Such experience and willingness to stand in the gap, is essential to the life of the true philosopher. The stark challenge to think without coercion, and under differing goal structures. Such lessons are not learned in academia nor government, and yet are also critically essential to good science.

In the end, the hero of the Dr. Seuss story turns back to confront his troubles, and becomes trouble to them instead. When making the journey from notion to inference, there exists a cast of standard nefarious pretenders – characters who have never done a thing with their life, and for whatever reason, are angry at you over this reality. They will attempt to make the journey confusing and ineffective. These are the Perilous Poozers one must face down, in order to discern sound science or public policy.

The Perilous Poseurs of Pompelmoose Pass

Fallacy Falcons

They don’t actually ever create anything. They hide inside the lack of accountability automatically afforded denial and critique. They never get into the mix, but rather fly high above it, merely to swoop down and point out the informal fallacy you have committed. The problem with garden variety fallacies is, they lend a false confidence into the mind of this form of poseur skeptic. The notion that, because they have filtered out disliked ideas by means of informal violations, they have therefore increased the likelihood that their own ideas are correct. But you will also notice that they never expose their own ideas to critique and never show their hand at actual logical calculus built into an argument or refutation – this is part of the massive ego complex they conceal. In the end, their debunking only constitutes a form of punishing those who disagree and has nothing to do with any form of inference, rationality, or scientific ‘likelihood’.

It is commonly claimed that giving a fallacy a name and studying it will help the student identify the fallacy in the future and will steer them away from using the fallacy in their own reasoning. Fallacy theory is criticized by some teachers of informal reasoning for its over-emphasis on poor reasoning rather than good reasoning. Do colleges teach the Calculus by emphasizing all the ways one can make mathematical mistakes?

~ Internet Encyclopedia of Philosophy: Fallacies: 3. Pedagogy

Bayesian Bullies

Bayes Theorem is founded upon scientific estimations of probability, which are confirmed and then updated by series inductive tests. However, poseurs therein are often not aware of when such a process does and does not bear utility. These poseurs will constantly sea-lion for ‘studies’, ‘recitations’, ‘proof’, knowing that most subjects are not easily reduced much less resolved by Bayesian induction under confidence. They use linear induction and abductive reasoning, in place of deduction, consilience, and falsification. They elect to be scientists when an investigator is needed most, and then become technicians when they need to be scientists. Shrinking from the true prosecution of ideas. They intimidate by means of unjustifiable levels of precisely framed outcome, or precision as a substitute for accuracy. They frame a complete guess, by means of boastfully confident (hedging) error bands. They resolve the answer before determining the right question. They forecast the future before defining correctly the present, hoping to be lucky rather than good. They harden their model to inaccurate outcomes, failing to realize its incumbent brittleness.

Bayesian methods are presented as an automatic inference engine, and this raises suspicion in any-one with applied experience… such methods being oversold as an all-purpose statistical solution to genuinely hard problems. Compared to classical inference, which focuses on how to extract the information available in data, Bayesian methods seem to quickly move to elaborate computation rather than the deeper questions of inference.

~ quoted and condensed from Andrew Gelman, Objections to Bayesian Statistics, 2008, Colombia University

Parable Probabilizers

Since all knowledge is uncertain, therefore knowledge can be gained by merely establishing a scant level of likelihood regarding it. Such probabilizers exploit the cache of obviousness as evidence that the ‘simple is more likely’. They stack up comfortable and understandable parables, asking you to ignore the risk, and just focus on the ‘explain it in simple terms’ answer they have crafted. They contend that ‘until God gets here and establishes truth for us, I will explain that which is more likely in his stead’. Yes, this is a claim to being God. They ‘results gauge’, or produce answers which are at face value simple, conforming or understandable (concealed complication in reality) as opposed to answers which are complex, informative, challenging or push our development envelope. They fail to understand Ockham’s Razor, and thus crafted this mutated version called Occam’s Razor, affording one permission to wrap up all epistemological loose ends as ‘finished science’ in one fell swoop of fatal logic. They ignore the riddle of Lindy:

The fact that an opinion has been widely held is no evidence whatever that it is not utterly absurd; indeed in view of the silliness of the majority of mankind, a widely spread belief is more likely to be foolish than sensible.

~ Bertrand Russell, Marriage and Morals

Process Ponzi Schemers

One key method of pretend science is to borrow assumptions from early in the scientific method, and apply them later as pretend held assets, asking one to invest belief in such process of science. This is at its heart a Ponzi Scheme. Paying off scientific answers by means of borrowed assumptions, premature questions, and gravitas that are not real owned assets. They ‘ask a question’ before conducting any kind of intelligence development or establishment of necessity. They promote a mere notion to the vaulted office of hypothesis, and then prove it by its ‘simplicity’ alone. They fail to ask ‘What do we not know?’ or ‘Can this lack of knowledge cause harm?’ They use the process of reduction and linear analysis to affirm what they already ‘knew’ (sciebam), rather than seek to challenge and falsify (science). They declare (scientific claim) something ‘supernatural’ or ‘pseudoscience’ and not approachable by science, so that it does not have to be studied in the first place and therefore can never become science either. They use accidental absences of data in a discovery protocol, to stand as evidence of absence. They start with the answer, and finish in the very next step by means of the awesome insistence of meta-analysis. They view science as a bludgeon to conclusion, and not as a feedback cycle.

When I give a thesis to students, most of the time the problem I give for a thesis is not solved. It’s not solved because the solution of the question, most of the time, is not in solving the question, it’s in questioning the question itself. It’s realizing that in the way the problem was formulated there was some implicit prejudice or assumption that should be dropped.

~ Carlo Rovelli, The New Republic: Science Is Not About Certainty, 11 Jul 2014

At times, it is indeed the job of the ethical skeptic to stand in the gap on behalf of the innocent. To make life hell, for those who choose to be abusive troubles rather than thoughtful contributors.

The Ethical Skeptic, “Trouble on the Way from Notion to Inference”; The Ethical Skeptic, WordPress, 17 Feb 2022; Web, https://theethicalskeptic.com/?p=50006