From the R.M. Dolin book, “Truth and Trust in Crisis,” 2021
Chapter 5: Deduction versus Induction
Computer models never render correct answers, but we expect experts to at least be close in a crisis with so much at stake. For government and academia to be off by a factor of ten in their Armageddon estimates while our simple model’s essentially spot on demands explanation. For this, we consider the two primary schools of logic, deduction and induction. The Greek philosopher Aristotle is credited with developing the philosophy of logic, his focus is deduction, but he recognizes there’s a competing logic he calls induction.
Deductive reasoning goes from the general to the specific. In deductive logic, a conclusion is necessarily based on a set of premises assumed true. An example from Newton is “Gravity acts on all objects (general premise). An apple fell on my head due to gravity (specific conclusion).” Deduction is the basis of science for things that can be demonstrated or proven through experimentation.
Induction is where a set of premises support a conclusion but don’t necessitate it. Inductive logic moves from particulars to their generalizations. An example is “My son’s school starts at 8:00. My daughter’s school also starts at 8:00 (specific premises). Therefore, all schools start at 8:00 (general conclusion).” Induction is the basis for things that cannot be necessarily proven.
To demonstrate how deduction and induction can be applied to COVID, let’s assert that Brix/Fauci and their academic cohorts either knowingly mislead us about COVID projections or are insufficiently competent for their roles in this crisis. To work from this assertion to a conclusion, we’ll apply both deductive and inductive logic, but first, we state our hypotheses.
H1: Brix/Fauci, along with their academia cohorts, knowingly misrepresent data and mislead the public.
H2: Brix/Fauci, along with their academia cohorts, lack sufficient competence for their positions.
General Premises for Deduction:
- Unsubstantiated assertions put forward by Brix/Fauci and their academic cohorts led to government overreach and public panic.
- Flattening the curve is not intended to reduce infections or deaths, only spread them out.
- Natural viruses behave exponentially.
- Models should always be validated against first principles and peer reviewed.
- Expert predictions should be validated against observational evidence.
Deductive Hypothesis Testing – General to Specific
Brix/Fauci rely on the unsubstantiated UW model to assert mitigation measures are necessary to “flatten the curve” even though that model demonstrates no ability to simulate the impact of mitigation measures, and no evidence is offered suggesting mitigation measures are effective. In fact, a Johns Hopkins study finds mitigation measures are not effective. Brix/Fauci use academic models to assert that the reason their initial projections are so outrageously wrong is because their mitigation measures work. These two assertions contradict each other because if they know the mitigation measures flatten the curve their models should have confirmed it and not been outrageously wrong in the first place. The only plausible conclusion is either they are not competent enough to understand the models are insufficient items of evidence and more evidence is needed (H2), or we are intentionally being misled (H1).
Utilizing the UW model, state and federal agents enact draconian mitigation measures. They either know the models are wrong and don’t care or are not competent enough to realize the dire projections are unreasonable. What’s shocking is that serious peer review, other than ours, has yet to occur even though their validity has been proven to be seriously flawed. The only plausible explanation for how government and academic experts can be so wrong while we are able to accurately predict COVID behavior using first principles is that H1 and H2 are valid.
General Premises for Induction
Consider the following specific premises
- COVID costs are projected to exceeds $5 trillion, which means people are profiting from this crisis.
- Medical doctors are being incentivized to attribute non-COVID deaths to COVID.
- Medicare pays hospitals $39,000 to treat a COVID patient but $13,000 to treat influenza patients.
- Dr. Brix confirms the U.S. intentionally over-counts COVID deaths.
- New York officials admit to over-counting COVID deaths by 57%.
- Numerical models are always wrong, but first principal validation can bound expected outcomes.
Inductive Hypothesis Testing
During COVID’s expansion up to the apex, bureaucrats use model projections as a justification to close schools, businesses, and churches devastating our nation’s economic and social fabrics. Even if experts believe the academic models, they have a fiduciary duty to challenge the predictions and seek alternate validation.
Medical professionals are financially incentivized to over count COVID deaths. As a result, Doctor Brix admits CDC data, which is compiled from medical input, is untrustworthy. New York officials admit to over-counting COVID deaths by 57%. Science students are taught to be skeptical of data until validated; they are also trained not to rely solely on numerical models for decision making; models are one of many tools that assist decision making. Using one unvalidated model as a single source of policy planning is crisis management malfeasance.
Because numerical models are always wrong, no one expects experts to be spot on, but to be wrong by a factor of ten without contrition, defies intellectual standards. The government incentivized bad behavior within the medical community skewing CDC data toward their projections. It is reasonable to conclude New York is not the only state intentionally over-counting COVID deaths to exploit crisis narratives. It is equally likely some states, based on different narrative objectives, intentionally underreport. Brix/Fauci and their academic cohorts must know these things but intentionally mislead us anyway (H1). By mid-April, it’s clear the academic models are incapable of making rational or responsible projections yet no one in academia or government challenges the model or seeks alternative sources of validation (H2).
Epilogue: At the conclusion of his most famous play, Shakespeare laments, “For never was a story of more woe, than this of Juliet and her Romeo.” Sadly, it seems our crisis has equally devolved. As peasant citizens of a great nation, we’re taught to believe in the honor and integrity of our leaders, in the intellectual superiority of our academicians, now we find them misleading us and lacking sufficient competence for their positions, casting both truth and trust in crisis.
Note: This chapter is based on a series of 2020 essays submitted to major media outlets, including the NY Times, Washington Post, and Chicago Tribune. The essays can be found at: https://rmdolin.com/commentary/
Click here to read the entire “Truth and Trust in Crisis” book
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