Chapter 5 in the R.M. Dolin book, “Truth and Trust in Crisis.”
In computer modeling, one never gets the correct answer, however, we expect experts to at least be close in a crisis with so much at stake. For trusted experts to be off by a factor of ten in their Armageddon estimates while our simple statistical model was 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 was 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 that are assumed to be true. An example attributed to 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 and testing.
Induction is a method of reasoning 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 demonstrated or 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 COVID data and mislead the public.
H2: Brix/Fauci, along with their academia cohorts, are not competent enough for their leadership and academic positions.
General Premises for Deduction:
- Wildly 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.
- The growth rate for viruses usually follows exponential patterns.
- Models should always be validated against first principles assessments and be peer reviewed.
- Expert predictions should be validated against empirical evidence.
Deductive Hypothesis Testing – General to Specific
Deduction can be used to determine if one or more hypotheses are true; let’s start with H1. Initially doctors Brix and Fauci rely on the University of Washington model to assert mitigation measures are necessary to “flatten the curve” even though that model demonstrated no ability to simulate the impact of mitigation measures, and no evidence is offered suggesting their mitigation measures are effective. They 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 academic models are insufficient items of evidence and more evidence is needed (H2), or we are intentionally being misled (H1).
State officials relying on the same model projections enacted draconian mitigation measures. They either know the models are wrong and don’t care or are not competent enough to realize the unrealistically high projections need to be challenged. What’s shocking is that serious peer review of COVID models (other than ours), has yet to occur even though COVID crisis estimates not only were, but continue to be, seriously flawed. The only plausible explanation for how government and academic experts can be so wrong while not challenging absurd assertions while we are able to accurately predict COVID performance using first principles is that H1 and H2 are valid.
Next, we consider H1 and H2 from an inductive perspective. The specific premises for our induction are
- COVID costs are projected to be over $5 trillion, which means people are profiting from this crisis.
- NIH and CDC directed doctors to attribute non-COVID deaths to COVID.
- Medicare pays hospitals $39,000 to treat a COVID patient but only $13,000 to treat an influenza patient.
- Doctor Brix confirmed the U.S. intentionally over-counts COVID deaths.
- Government officials in New York admit to over-counting COVID deaths by 57%.
- Numerical models are always wrong, which is why first principal validation is needed to bound expected outcomes.
Inductive Hypothesis Testing
During COVID’s expansion up to the government declared apex, bureaucrats use model projections as a justification to close schools, businesses, and churches devastating our nation’s economic and societal fabrics. Even if these experts believe in the academic models, they have a fiduciary duty to challenge the predictions and seek alternate means of confirmation/validation.
The government incentivizes medical professionals to over count COVID deaths by offering substantial rewards each time they declare a patient has COVID. As a result, Doctor Brix recently admits CDC data, which is compiled from medical claims, is untrustworthy. Also, New York officials have admitted to over-counting COVID deaths. Science students are taught to not rely on numerical models for decision making; models are just one of many resources that should be utilized in deliberations because their results are always wrong. Using unvalidated models as the sole source of extreme policy making is unconscionable.
Since 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, which resulted in CDC data being skewed toward their projections and still they were off. It is reasonable to conclude that New York is not the only state that intentionally over-counts COVID deaths to exploit their crisis narratives. It is equally likely some states, based on different narrative objectives, are intentionally underreporting. Brix/Fauci and their academic cohorts must know these things but intentionally mislead us anyway (H1). It is painfully obvious by mid-April 2020, that the academic models are incapable of making rational or reasonable COVID projections yet no one in academia or government does anything to challenge the model or seek alternative sources of validation (H2).
Epilogue: At the conclusion of his play, Shakespeare writes, “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 are taught to believe in the honor and integrity of our leaders, in the intellectual superiority of our academicians, now we find them willingly misleading us and are not competent enough for the task at hand, casting both truth and trust in crisis.
