Chapter 1 from the R.M. Dolin book, “Truth and Trust in Crisis,” 2021
When the history of the COVID crisis is eventually told, long after we’ve learned to filter through media sensationalism, political distortions, medical malpractice, and our emotional investment in hysteria, the crisis won’t be remembered for its death toll, it won’t be remembered for the economic chaos caused, the personal turmoil, or even the irrational logic of facemasks, placing infected people in nursing homes, or quarantining healthy populations; COVID will be remembered as the first crisis of the digital age and how data was weaponized into prescribed narratives and used to manipulate populations into Pavlovian subterfuge.
Before historians have their say though, the dwindling demographic of serious men and women of science who do not abdicated responsibilities during crisis need to perform postmortems to separate fact from fallacy, fiction from science, and immoral malice from systemic incompetence. I am in a unique position to contribute to this assessment because I’m one of the few scientists who didn’t abdicate responsibilities as the world spun into inconceivable chaos. While most of my fellow engineers and scientists sat silent as their professions were hijacked, I applied mathematics to real data to demonstrate how the political, medical, academic and media communities obfuscated “science,” to fulfill the needs of their agenda-driven narratives. Throughout the crisis I challenged colleagues to step up and risk both cancellation and persecution, to act as our courageous predecessors like Copernicus, Galileo, and Averroes had done. I implored my peers to host on-line reviews of narrative assertions, to hold politicians and crisis managers accountable, only to be met with the silence of abdication.
When historians take their turn, convoluted attempts will be made to obscure the profound ways this virus unraveled society. Footnotes will appear here and there commenting on how science, which for centuries saved the planet from calamities and ushered in the technical wonders of the digital age, failed the world in ways that are as incontrovertible as they are inconceivable. There will of course be the cliché finger-pointing and scapegoating by pundits and those trying to conceal their culpability, but I trust in your ability to ferret out the truth and believe you are already more than familiar with the list of leading contenders for COVID’s most nefarious villains.
A prominent piece of propaganda historians will undoubtedly promote in post-mortems is that this crisis brought out the very best of people and society; you’ll be told you were not only heroic, but acted in accordance with the information provided, as did illustrious politicians, wise academics, dedicated medical professionals, well-intentioned bureaucrats, and benevolent media moguls. The overarching theme will be no one in the moment could have understood what was happening since there were no lights to guide us through the darkness, so everyone gets pardoned; politicians are re-elected, bureaucrats are promoted, and doctors and moguls are further enriched. Only, that’s not what the causal evidence suggests and together, our collaboration will diffuse such assertions by shedding light on the COVID crisis and demonstrating a fundamental truth of the digital age that those in charge were ignorantly unaware of; namely, when trusted data is properly utilized, it doesn’t lie, mislead, or obfuscate, it leads to truth.
Using simple math, we’ll demonstrate how those responsible for mitigating the crisis exploited our trust by weaponizing data to legitimize dramatically exaggerated model projections to feed false-flag hysteria and recklessly regulate remedies meant to calm our fears. Together we’ll expose government, academia, media, and medical professional culpability as we work through the role you played. Yes, you were intentionally misled and even likely lied to at times, but you, and you alone, allowed yourself to be manipulated, often to the point of doing things contradictory to self-interest and personal health. In the digital age, you have a responsibility to ferret through false-flags and disinformation and must come to terms with why you chose to not be vigilant.
Our collaboration through the COVID crisis requires you and I to be scientists, which gives you the freedom to disagree and the responsibility to challenge my hypotheses, calculations, and conclusions, because in science everything should be openly challenged and vigorously debated. Nothing should ever be accepted in the name of consensus; that’s the domain of pseudo-science charlatans. As men and women of science, you and I have an obligation to engage in open and unbiased dialog and at the same time, in deference to how social media and political pundits have conditioned society in the digital age, we’re obligated to hear all sides of a fact-based argument while driving toward logically framed conclusions that allow others the freedom and appreciation to conclude differently.
We will at times assume the role of crisis managers, which requires merging quantitative facts with qualitative conjecture to make judgement-based decisions. For example, diagnosing a patient with cancer is a quantitative undertaking; you run tests, analyze the results, and make a diagnosis based on the data. How treat is prescribed is based on qualitative criteria, such as where the doctor trained, what pharmaceutical company sponsors their treatment drugs, and even sadly, how much money the patient has. As scientists, we’ll strive to stay in our quantitative swim lane while pointing out when nonscientists cross into our lane and what consequences that causes.
When we take up the topic of facemask efficacy, we’ll use science and logic to assess their effectiveness at stopping the spread of the COVID virus. However, that assessment can only inform the public policy issue regarding facemask mandates. Similarly, we’ll use available evidence to subdivide populations into demographics based on risk of illness, but information that can only be used to inform government officials regarding quarantine mandates. The intersection between quantitative and qualitative, fact and opinion, science and policy, is one we must continually navigate; as scientists, we strive to avoid opinion-based conjecture as much as possible.
Another delicate balance we’ll strive to maintain is providing you with sufficiently detailed derivations of our mathematics to assure your confidence in our conclusions without overwhelming you with formulas and figures. This will, however, be a challenge at times since it’s hard not to get lost in the language of math when topics become complex. My goal is to present sufficient details to allow you to reproduce results on your own while demonstrating the necessity in the digital age to approach all news and government information with timid skepticism and decide for yourself what is truthful and trustworthy. I’ll demonstrate multiple ways of filtering through misinformation to find the truth, often in seemingly unconventional ways; for example, using funeral home statistics to validate CDC death data.
Once historians are done and postmortems neatly filed away, the search for culprits begins. If the past provides our pretense, the culpable will avoid scrutiny, which leaves the fingers of blame pointing at you; your naive gullibility, your inherent trust in those responsible for your care, your docile willingness to do what you’re told even when it flies in the face of self-interest. My goal is to prevent what happened to you during COVID from happening again by arming you with logically based, mathematically consistent arguments that can incredulously challenge entrenched narratives using the unfettered power of real science.
Like most scientific inquiries, we’ll begin with a presentation of credentials. I’m a blue-collar PhD who left high school early to apprentice as a plumber. The money’s good and a hard life suite me; after a year and a half though, a curiosity about how the world works awakens putting me on the path toward a Mechanical Engineering degree and a career at the prestigious Los Alamos National Laboratory, home of the infamous Manhattan Project[1]. With my itch unscratched, I earn a master’s degree in numerical methods while working before Los Alamos sends me to Purdue University for a year and a half to obtain a PhD in Design Optimization. In 1999, Los Alamos offers me an opportunity for a one-year sabbatical at the University of California at Berkeley to pursue research in Decision Theory and Bayesian Probability Analysis. Upon my return, I support nuclear stockpile reliability, serve as Assistant Director of the Los Alamos Center for Homeland Security, manage a hundred-million-dollar nuclear production portfolio, and lead the Manufacturing Modernization Project at the Los Alamos plutonium facility.
In between earning my PhD at Purdue and sabbatical at Berkeley, In addition to working at Los Alamos, I teach graduate courses in the mathematics department at the University of New Mexico. I retire from Los Alamos in 2018 but come back to pursue research in trust; the science behind how trust in data, processes, and products is assured in the absence of evidence. A companion aspect of my research involves truth; specifically, how one ferrets out truth in the digital age when inundated with copious data, much of which is suspect.
The Oxford Dictionary[2] defines trust as “a firm belief in the reliability, truth, ability, or strength of someone or something.” For example, we trust things that consistently behave as intended. Trust can be defined in terms of confidence in an outcome based on specific input. Trust is also a function of risk, that is assured when all the risks involved have been identified and mitigated to an acceptable threshold. We’ll utilize these aspects of trust to understand why certain policies and decisions were implemented during COVID, and whether we can or should trust what we’re told by “benevolent experts.”
Micheal Kosfeld[3], points out that, “trust is very much a biologically-based part of the human condition. It is, in fact, one of the distinguishing features of the human species. An element of trust characterizes almost all human interactions.” While we’re all wired to trust, we nonetheless require items of evidence. A dichotomy of trust exists between items that can be measured and thus quantified, and equally significant elements that need to be qualitatively accepted. This dichotomy plays an important role in our review of how COVID data was derived, conveyed, and used to support prescribed narratives.
Perspectives on trust vary from person to person and even within a person depending on context. For example, as a passenger on an aircraft, you implicitly trust the plane to take-off, fly, and land safely each time you board without knowledge of the aircraft’s manufacturing or maintenance history. As a pilot of the same aircraft, you’re far more skeptical, which leads you to perform pre-flight tests, check maintenance data, and review airworthiness bulletins. According to the principle of the Saint Petersburg Paradox[4], both pilot and passenger have the same utility function, namely a desire to survive the trip, even though their aversions to risk, and thus interpretation of utility, are diametric. Because trust can be thought of in terms of risk aversion, we introduce the concept of “zero trust,” which states that anywhere trust must be assured, the starting point is one of skepticism. Stephen March[5], first formalized the idea of zero trust in 1994, by asserting that trust can be quantified purely in mathematical terms, devoid of qualitative measures such as morality, judgment, and human perception.
How the COVID crisis was promoted and managed from a zero-trust perspective requires we be skeptical of any data and any data provider, and rely on quantifiable evidence, which is then necessary and sufficient, to assure the data, or inferences made from the data, can be trusted. Likewise, when assessing assertions made and conclusions drawn by politicians, bureaucrats, medical professionals, and the media, we require the addition of quantifiable evidence to prove or disprove the trustworthiness of their assertions. This is a scientific way of stating that we don’t rely on opinions, political dogma, or narratives to bias us toward trusting or not trusting either the data itself, or what we are told the data conveys. There will be times when this simple principle is difficult to maintain because it requires you to logically follow a data stream to its most likely conclusion that may run counter to your beliefs or perceptions.
It’s naive to suppose something as foundational as assuring trust does not involve elements of judgment and belief, so, rather than latch onto sexy slogans like, “follow the science,” to promote a narrative, we’ll rigorously follow quantitative science, even when it yields surprising or unintended consequences. This then returns us to our trust dichotomy; quantitative trust relates to things that can be measured, while qualitative trust is based on unmeasurable beliefs or opinions. For example, I own an old Dodge diesel pickup that I quantitatively trust to start each morning because I maintain proper fuel and fluid levels, and ensure the batteries are charged. Qualitatively, I believe the truck’s Cummins diesel is the finest engine ever built, which gives me unmeasurable confidence my truck always performs as expected.
An example of qualitative trust involves money; most of us believe our money is safe in banks even though during a financial crisis we implicitly understand there’s not enough cash to cover massive withdrawals. We trust the banking system even though evidence indicates that trust is dubious. Qualitative trust can evaporate based on a singular event or in some cases, with no detrimental evidence at all – just a growing concern something is amiss. Diminishing trust can be biased from a combination of pessimism, conservatism, or an inherent aversion to risk. At times we’ll challenge your qualitative trust boundaries as we provide evidence for how the world fell victim to some disastrous decisions during COVID.
While qualitative trust cannot be measured, its importance cannot be disregarded. When qualitative trust is diminished, its impact can be far reaching. For example, if you lose confidence in a beverage company’s ability to make one soft drink consistently, you tend to lose trust in their ability to make any soft drink well, and if they’re a multi-national conglomerate, their ability to make any high-quality product. In our zero-trust approach, we’ll rely on quantifiable evidence to assuage our inherent skepticism, but qualitative impressions will play a role. This is a cornerstone of science, making assessments based on measurable information but allowing perception to cautiously influence our conclusions. We’ll often begin our review of data from nonscientific origins, such as data from medical or governmental sources. Once we’ve obtained their data, we can parse out what fact is (i.e., quantitative), and what is opinion (i.e., qualitative), and apply additional evidence from trusted sources to arrive at quantifiable assessments.
The next step in our inquiry is defining nomenclature, or words used to carry specific meaning. Mathematics is rich with taxonomy, but we don’t have too many terms to define. During COVID, important terms were often misused, topping our list is “data,” which is often conflated with “information” when used by politicians, bureaucrats, medical professionals, and members of the media, even though these two words have different meanings.
Data is a set of facts having no context that by itself is not useful until a value-added analysis utilizes the data to organize, make assertions, or draw conclusions. Data is both singular and plural, but when plural each data item needs to contain the same item of fact gathered in the same manner. For example, if we weigh bananas, then each measurement represents a data point in the set and each banana must be measured in precisely the same manner, say for example, with the stem cut to equal lengths using the same calibrated scale.
Information is knowledge obtained by ordering, analyzing, and interpreting data. Information is the understanding gained from data within a specific context. For example, diagnosing a patient with cancer is a matter of fact – a measurement was made, and the results reported. This fact, by itself, does not necessarily convey knowledge, which is only achieved when this data is combined within some context. For example, to parse data representing an entire population of people diagnosed with cancer into demographics based on age, gender, ethnicity, or location provides useful information or knowledge that can then be used to prescribe treatment.
During COVID, epic fails resulted from bureaucrats and medical professionals misusing data to generate flawed information stemming from their disregard for fundamental principles of sound science, for example, by failing to distinguish between those who died from COVID from those who died with COVID, or double counting one death at both flu and COVID. We’ll cite instances where attending physicians listed COVID as the cause of death when a patient was hit by a bus or killed in a motorcycle crash. While their motive was greed, their rational was along the lines of, “had it not been for COVID, the patient would have been at work and not out joy riding,” which is both absurd and illogical. We’ll highlight examples causing COVID data to be corrupted to the point it becomes difficult to decipher real data (i.e., facts) from fictional fabrication (e.g., medical fabrications).
TTo demonstrate how badly medical professionals failed the country in crisis you need to understand that the Centers for Disease Control (CDC) conflates deaths from pneumonia, influenza and COVID (PIC) into a single number. The CDC announced in January of 2022 that while prescribed narratives for PIC deaths, based on medical reporting, approached one million, the actual number was 94% less, or ~30,000 per year.[6] To put this in perspective, during an average flu season there are 36,000 deaths; in 2017, there were 61,000 deaths from flu. In other words, after two years of COVID hysteria, the PIC death count is less than that of a normal flu season. If you think that’s outrageous, I invite you to hold onto your thinking caps because together we’re going to prove this assertion from multiple, and sometimes unconventional, avenues as we address truth and trust in the COVID crisis.
Another aspect of our lexicon is the distinction between causation and correlation. In mathematics, causation and correlation relate to how statistical evidence inter-relates. Cause-and-effect relations are everywhere, but it’s more common for relationships to be correlated rather than causal. For example, if you observe that lightning storms cause the power to go out, which causes your freezer to stop working, the relationship between your freezer not working and lightning storms is not causal because the power can go out for other reasons. There is, however, a correlation between lightning storms and your freezer failing to operate. If your freezer only occasionally goes out during a lightning storm, we say the correlation is weak, however, if the freezer is almost certain to go out every time there’s lightning, the correlation is strong. The distinction between causation and correlation becomes relevant in many COVID areas, such as the mask mandates that will be discussed later.
Causation and correlation can co-exist, but of the two, causation implies a more direct relationship. For causation to exist, action A must cause B. For example, cutting electrical power causes the freezer to stop working. For causation to exist, you need a dependent and independent variable. In our example, electric power is an independent variable, because it does not depend on the freezer, however, the freezer is a dependent variable since its operation depends on having electricity. For causation to exist, changes to the independent variable cause changes to the dependent variable.
Correlations exist when two variables change together, or covary. Mathematically, correlations between two variables range from zero to one, with zero correlation implying no relationship, and one implying perfect correlation. For example, a correlation might exist between the number of hours you spend each day surfing the Internet and the size of your annual salary increase. If your increase varies slightly from expectation, you can conclude the correlation is weak (near zero). However, if you observe your raise, along with others who routinely surf the Internet, is far below those of more dedicated employees, you might conclude the correlation is strong (near one). Based on the strength of your correlation, you have information to help you modify your performance parameters.
Our journey through COVID begins around February 29, 2020, the day America reports its first COVID casualty. We’ll walk through our various analyzes using data available on each respective analysis date. It’s important to note that the analyzes presented were posted on my website and submitted to major media outlets, such as the New York Times, Wall Street Journal, Washington Post, and Chicago Tribune on the analysis date. In other words, if we review an analysis that asserts in April that the crisis will end in June, that projection was made in April without the benefit of postmortem clairvoyance (i.e., performed after the fact).
Along the way you’ll come to appreciate that truth and trust in the digital age, can change to fit prescribed narratives and agendas and a challenge we’ll be forced to overcome is how to ferret through data that cannot be trusted to find the truth of what’s really happening. I can’t promise you the ride won’t be bumpy, but it will be interesting and informative.
The dubious dichotomy between truth and propaganda can be assessed to address an elephant in the COVID room; namely, is it possible we were lied to and if so, were the lies intentionally malevolent or the result of hubris and incompetence? Evidence suggests data was misused and power abused during COVID, it appears political, medical, and media responses followed prescribed narratives but for different motives.
On a recent visit to the free state of South Dakota, I overheard cowboys talking about legislation from 1945 that prohibited the government from manipulating its citizens through propaganda but was modernized in 2012 to reverse course and permit the use of government sanctioned propaganda. At the conclusion of World War II a South Dakota congressman, named Karl Mundt, recognizes the effective way German Nazis’ used propaganda to brainwash an entire nation into believing it’s imperative they mass murder innocent men women and children. Congressman Mundt wanted to ensure that such nefarious propaganda can never be used on Americans so he introduces legislation called the “U.S. Information and Educational Exchange Act,”[7] which becomes known as the “Smith-Mundt Act of 1948.” Keep in mind this is at the same time U.S. hysteria is being whipped into frenzy regarding the communist plot to infiltrate our country, which seems a lot like an anti-communism propaganda campaign, but I digress.
In 2012, with the internet’s wild frontier maturing and the digital age blossoming, the Smith-Mundt Act is modernized leaving some scholars convinced it now gives the government carte blanch to legally use propaganda to promote narratives within the U.S. Fast forward to COVID where government, media, and medical narratives emerge with anyone questioning their canonical absoluteness systematically canceled and you begin to understand why formally trusted institutions like academia and national laboratories abdicate their responsibilities to the world. Could it be they understand the imperative to go along with prescribed narrative or face dire consequences or is it that they collectively believe in the complete righteousness of each narrative?
In 2006 the Los Alamos National Laboratory becomes the last institute within the vast nuclear weapons complex to become a for-profit corporation run by defense contractors. Setting aside the absurdity of having the nation’s nuclear weapons research and production run as a for-profit enterprise, at the height of COVID, when the nation desperately needs its best and brightest minds working on crisis-mitigation strategies, Los Alamos fires over four hundred scientists who dare question prescribed COVID narratives or call for open peer review and the remaining Los Alamos scientists, having got the message, quickly and quietly fell in line. This same fate awaits anyone in the medical, media, or academic communities who also dare challenge prescribed COVID narratives.
At the same time countless people get banned on social media, removed from websites, publicly ridiculed, ostracized, and in some cases even imprisoned for challenging approved narratives or questioning the efficacy of things like quarantines, mask mandates, and untested vaccines. The response to prescribed narrative opposition seems like exactly the kind of organized propaganda campaign the 1948 Smith-Mundt Act sought to outlaw, and the 2012 Smith-Mundt Modernization Act legalized. Our journey through the COVID crisis will not seek solutions to the narrative opposition issue, what we will do, however, is expose the misrepresentation of data that allowed prescribed narratives to be legitimized, such as the President’s “Follow the Science,” propaganda. Anything beyond that is left for you to decide because I trust in your ability to make well-reasoned judgments once you’re armed with the truth.
[1] https://www.atomicheritage.org/history/manhattan-project
[2] Oxford English Dictionary, OED Online. Oxford University Press, December 2019.
[3] Kosfeld, M. 2008, “Brain Trust,” Greater Good Magazine, University of California Berkeley.
[4] Peterson, Martin, “The St. Petersburg Paradox”, The Stanford Encyclopedia of Philosophy (Fall 2020 Edition), Edward N. Zalta (ed.).
[5] March, S.P. “Formalising Trust As A Computational Concept,” PhD Dissertation, University of Stirling, 1994.
[6] CDC Finds that 94% of US COVID-19 Deaths Include Comorbid Factors – SWFI (swfinstitute.org)
[7] https://en.wikipedia.org/wiki/Smith-Mundt_Act
