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
As a younger man, I ventured out one afternoon with the thought in mind of buying a new car. My old Toyota had 185,000 miles on it, and despite all the love and care I had placed into that reliable vehicle, it was time to upgrade. ‘Timothy’ as I called my car, had served me well through years of Washington D.C.’s dreadfully monotonous 6:00 am Woodrow Wilson Bridge traffic, getting to Arlington, the Pentagon and the Capitol District, through to graduate school classes, and finally getting home nightly at 11:00 pm. My beloved car was just about worn out. So I selected the new model that I wanted and proceeded one Saturday to a local dealer. The salesperson and I struck a deal on my preferred model and color, with approval from the sales manager skulking remotely as the negotiator within some back office. Always take this as a warning sign: any time a person who is imbued with the power to strike a deal, will not sit with you face to face during the execution of that deal. This is a form of good-cop/bad-cop routine. However, being that this was only my second car purchase, I accepted this as normal and shook hands with the salesperson upon what was in reality, a very nice final price on my prospective new car.
What this used car sales comedy serves to elicit is a principle in philosophy called an ‘ignorance of the necessary epistemological domain’, or the domain of the known and unknown regarding one cohesive scientific topic or question. Understanding both the size of, as well as that portion of science’s competent grasp of such domain/unknown, is critical in assessing scientific risk – to wit: the chance that one might be bamboozled on a car contract because of a lack of full disclosure, or the chance that millions of people will be harmed through a premature rollout of a risky corporate technology which has ‘over-driven its headlights’ of domain competency, and is now defended by an illegitimate and corrupt form of ‘risk strategy’ as a result.
One way in which such matters are addressed in industry (when they are addressed – which is rarely), is to conduct a form of value chain strategy called a risk chain evaluation or ‘risk strategy’. Risk flows in similar fashion to a value or margin chain, it concatenates, snowballs and increases non-linearly. It is not a stand alone element unto itself, rather part of the fabric of the mission, product or channel of service being undertaken. A risk strategy is usually done as part of a value chain strategy.
A risk strategy is developed in industry by first conducting a piloting session, which kicks off two steps. The first tasks a team which is assigned to develop the value chain description (Question 1 below) of the entailed domain (the critical path of a product development horizon, a brand strategy, a legal argument, or an imports channel for example). A second step then involves development of epistemological risk and slack factors, measures and sensitivities which can be assigned to each node (action/decision) in the risk chain series mapped during the first step (Questions 2 – 7 below). These shortfalls in diligence are derived from the general categorizations defined (with links) under ‘Domain Epistemological Risk’ above. This does not actually take that long if the group is guided by an experienced professional. The groups who conducted the two steps above then reconvene and develop the answer to Question 8 as the final step.
Identify ‘hazards’ and assess their likelihood of causing harm, and call that ‘risk’
Smaller Risk Horizon (Lower State of Domain Epistemological Risk)
First when one moves from the context of the trivial ascertainment of knowledge and into an arena wherein a population of stakeholders is placed at risk; say for example as in the case of broadscale deployment of a pesticide or an energy emitting system – the level of rigor in epistemology required increases substantially. One can see this under the column ‘Objective Risk modus absens‘. Here the null hypothesis shifts to the assumed presence of risk, not its absence (the precautionary principle). In other words, in order to prove to the world that your product is safe, it is not sufficient to simply publish a couple Hempel’s Paradox inductive studies. The risk involved in a miscall is too high. Through the rapid deployment of technology, society can outrun our ability to competently use or maintain that technology safely – as might be evidenced by nuclear weapons or a large dam project in a third world nation which does not have the educational nor labor resources to support operation of the dam. When we as a corporate technology culture are moving so fast that our pace outdistances our headlights – risk concatenates or snowballs.
Extensive Risk Horizon (High State of Domain Epistemological Risk)
Second and moreover, this problem exacerbates when the topic suffers from a high state of epistemological domain risk. In other words, there exist a high number of critical paths of consideration, along with a high degree of sensitive and influencing factors – very few of which we have examined nor understand sufficiently. Inside this realm of deliberation, induction under the Popper Demarcation of Science not only will not prove out the safety of our product, but we run a high risk of not possessing enough knowledge to even know how to test our product adequately for its safety to begin with. The domain epistemological risk is high. When a corporate technology is pushed onto the public at large under such a circumstance, this can be indicative of greed, malice or oppression. Risk herein becomes exponential. A technology company facing this type of risk strategy challenge, needs to have its legal counsel present at its piloting and closing sessions.
Much to the chagrin of fake skeptics, certain phenomena and archetypes in the realm of human experience, will just not go away. Specific subjects they disdain are irritatingly bolstered by almost daily repeated observation on the part of the general public. Inside many of these topics the idea that such disdained phenomena constitute a mere figment of overzealous imaginations has been falsified over and over. But this will never satisfy the mind of a fake skeptic. They extrapolate a condition of difficulty in terms of classic inference, to therefore stand as basis for inferring the phenomenon’s absence as well (appeal to ignorance). They then invoke the name of science, as a USDA stamp of certification on such putrid products of ‘critical thinking’. To the ethical skeptic, such skeptical casuistry is folly.
There are certain subjects, wherein their modus absens (absence as an object or state) has been falsified. In other words, Ockham’s Razor plurality has been surpassed and ethical research now demands their investigation. These are the domains which are best researched by the intelligence specialist; that form of investigator who knows how to assemble prior art and chase a consilience of information, all of which have proved to be unlike much of what we have seen before. But such a researcher must understand, that what is forbidden, and the puzzle piece nubs which are cut off in order to make the pieces a better ‘fit’ inside the a priori puzzle, can also often be assembled into the truth. Such is a predictable foible of mankind.
For instance, dark matter is a one-idea-solves-all proposition which is raised as a result of cataloging a set of anomalous observations regarding universal/galactic motions in their relation to our understanding of gravity. Classic linear induction would dictate that we craft dark matter as the incremental element which would function to conserve general relativity and Lambda-CDM models as the null hypothesis in the face of such a growing set conflicting observations. The reader may be forgiven for confusing such activity with ‘belief’. An ethical skeptic understands that the null hypothesis should never enjoy the luxury of becoming a belief.