From the R.M. Dolin book, “Truth and Trust in Crisis,” 2021
Chapter 1: Truth and Trust
When the history of COVID is truthfully told, long after we’ve learned to filter through political distortions, academic malfeasance, medical malpractice, media sensationalism, and our emotional investment in hysteria, the crisis won’t be remembered for its death toll, the economic chaos caused, the personal turmoil, or even the irrational logic of facemasks, placing those infected in nursing homes, quarantining healthy people, or injecting entire populations with untested drugs; COVID will be remembered as the first crisis of the digital age and how data is weaponized into prescribed narratives and used to manipulate the masses into Pavlovian subterfuge.
Before historians have their say, the dwindling demographic of serious scientists who do not abdicate responsibilities during crisis need to perform postmortems to separate fact from fallacy, fiction from science, and immoral malice from systemic incompetence. I’m 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 scientists sat silent as their professions are hijacked, I apply data analysis to demonstrate how a political, medical, academic, and media cabal obfuscated data to fulfill prescribed narratives. As the crisis raged, I continually challenge colleagues to step up and risk both cancellation and persecution, to act with the same courage of conviction predecessors like Copernicus, Galileo, and Averroes had. I implore my peers to host on-line reviews of political assertions, academics projections, and crisis mismanagement, only to be met with the silence of abdication.
Historians will make convoluted attempts to obscure the profound ways the virus unravels society. Occasional footnotes will comment on how science, which for centuries protects the planet from calamities and ushers in the technical wonders of the digital age, fails in ways that are as incontrovertible as they are inconceivable. There will of course be 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.
Prominent placement of propaganda will promote how 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, doctors and media moguls are further enriched. The sticky wicket, however, is that’s not the portrait the evidence paints and together, you and I will diffuse such assertions by shedding light on the COVID crisis and demonstrating the fundamental truth of the digital age that the cabal was ignorantly unaware of; namely, when trusted data is properly utilized, it doesn’t lie, mislead, or obfuscate.
Using simple math, we’ll demonstrate how those we trusted to manage the crisis, weaponized data to legitimize dramatically exaggerated models that in turn fed false-flag hysteria and recklessly regulated remedies meant to calm our fears. Together we’ll expose government, academic, media, and medical professional culpability as we work through the role you played. Yes, you were intentionally misled but you, and you alone, allowed yourself to be manipulated, often to the point of doing things contradictory to your own self-interest and personal health. In the digital age, you have a responsibility to ferret through false-flags and disinformation, and you must come to terms with why you let others dictate your fate.
Our collaboration requires you to travel with me as a fellow scientist, this gives you the freedom to disagree and the responsibility to challenge my hypotheses, calculations, and conclusions, because in science things need to be openly and vigorously debated. I’ll confess up front that given what we’ll uncover, I want you to prove me wrong because if you can’t, our future fate is far more dire than you can conceive. As scientists, we’re not interested in consensus; that’s the domain of pseudo-science charlatans. You and I have an obligation to engage in unbiased dialog and at the same time, in deference to how social media and political pundits conditioned us in the digital age, we’re obligated to hear all sides of a fact-based argument as we drive toward logically framed conclusions while allowing others the freedom and appreciation to conclude differently.
As scientists, our swim-lane is quantifiable facts and observational evidence. Our goal is to stay within this lane but there are times we merge with qualitative conjecture. For example, diagnosing a patient with COVID is a quantitative undertaking; you run tests and make a diagnosis based on the data. However, treatment is based on qualitative criteria, such as where a doctor trains and patient access to money. As we strive to stay within our swim lane, we’ll point out instances when nonscientists cross into our lane and what calamities that causes. We’ll often combine logic with math to arrive at outcomes, like when we take up the topic of facemask efficacy or use available evidence to subdivide populations into demographics based on likelihood and seriousness of illness. The intersection of 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, but it sometimes can’t be avoided.
My goal is to provide sufficiently detailed derivations to assure your confidence in our conclusions without overwhelming you with formulas and figures. This will be a challenge at times since it’s hard not to get lost in the language of math when topics become complex. I’ll provide enough detail to allow you to reproduce results while demonstrating the necessity in the digital age to approach all news and government information with tepid skepticism and decide for yourself what is truthful and trustworthy. I’ll demonstrate ways to filter through misinformation; for example, using death industry statistics to validate CDC data.
After COVID postmortems are neatly filed away, the search for culprits begins. If the past is prologue, the culpable avoid scrutiny, which leaves the fingers of blame pointing at you; your naive gullibility, your inherent trust in leaders and institutions, your docile willingness to do what you’re told even when it flies in the face of self-interest. My goal is preventing what happened during COVID from happening again by arming you with tools using the unfettered power of science. As we journey through COVID’s crisis, we’ll solve the overriding riddle; why. Why was truth obfuscated? Why were we deliberately deceived?
I’m a blue-collar PhD who left high school early to apprentice as a plumber. Hard work suits me but after two years of commercial construction, a curiosity for how the world works puts 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 before, Los Alamos sends me to Purdue University to obtain a PhD in optimization. After several years in nuclear weapons design, I take a one year sabbatical at the University of California Berkeley to pursue research in Decision Theory and Bayesian Probability Analysis. Upon my return, I support stockpile reliability, serve as Assistant Director of the Los Alamos Center for Homeland Security, manage a hundred-million-dollar nuclear design and production portfolio, and lead the Manufacturing Modernization Project at the Los Alamos plutonium facility.
While working at Los Alamos, I also teach graduate mathematics at the University of New Mexico. I retire from the Lab in 2018 but come back to pursue research in truth and trust; specifically, how one assures truth in the digital age when inundated with copious data, much of which is suspect. Trust involves how data, processes, and products are assured in the absence of validating evidence. The Oxford Dictionary[2] defines trust as “a firm belief in the reliability, truth, ability, or strength of someone or something.” We trust things that consistently behave as intended and 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 acceptable thresholds. We’ll utilize these aspects of trust to understand policies implemented during COVID, and whether we can or should trust what we’re told in times of crisis.
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 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 is derived, conveyed, and used to inform policy.
Perspectives vary from person to person and even within a person depending on context. As a passenger on an aircraft, you implicitly trust the plane to take-off, fly, and land safely each time you board without evidence of the plane’s airworthiness. As the pilot you’re far more skeptical, causing 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, 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, 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.
Our journey through COVID is from a zero-trust perspective, which requires being skeptical of any data and any data provider, and likewise, of inferences made from data. When assessing assertions made by politicians, bureaucrats, academics, medical professionals, and the media, we require the addition of quantifiable evidence to prove or disprove their trustworthiness. As scientists we won’t rely on opinions, political dogma, or narratives to bias our analyses or stifle our intellectual curiosity. You will be challenged to separate yourself from what you’re told by people you trust from what the data reveals, which may run counter to tightly held opinions and 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,” we’ll rigorously follow math and data analysis, even when surprising or unintended consequences result. 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 that I quantitatively trust to start each morning because I maintain proper fluid levels and ensure the batteries are charged. Qualitatively, I believe my Cummins diesel is the finest engine ever built, which gives me unmeasurable confidence my truck always performs as expected.
Another example involves money; most of us believe our money is safe in banks even though we implicitly understand there’s not enough cash on hand 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’s amiss. You see this happen in the stock market where wild swings are caused by opinion-based beliefs. 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 quantitative evidence for how the world fell victim to disastrous decisions.
While qualitative trust cannot be measured, it can also, not 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 that company’s 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 observation but allowing perception to cautiously influence conclusions. We’ll often begin a review using data from nonscientific origins, such as medical or governmental sources. Once obtained, we’ll parse out fact from conjecture and apply validating evidence from trusted sources to filter through obfuscations.
We’ll utilize nomenclature, or words used to carry specific meaning. The term, “data,” is often conflated with “information” when used by politicians, bureaucrats, medical professionals, and members of the media, even though they have different meanings. Data is a set of facts having no context that by itself lacks meaning 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 a set and each banana must be measured in precisely the same manner, say 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 is made, and the results reported. This fact, by itself, does not necessarily convey knowledge, which is only achieved when the data is combined within some context, like parsing data from an entire population of people diagnosed with cancer into demographics based on age, gender, or ethnicity to be used to prescribe treatment.
During COVID bureaucrats and medical professionals misrepresent data that is then used to generate flawed information, for example, by failing to distinguish between those who die from COVID from those who die with COVID, or double counting one death as both flu and COVID. We’ll cite instances where attending physicians list COVID as the cause of death when a patient is hit by a bus or killed in a vehicle crash. While their motive is financial, their rational is along the lines of, “had it not been for COVID, the patient would have been at work and not out joy riding,” which is absurd, illogical, and nonscientific. We’ll highlight examples causing COVID data to be so corrupted it becomes difficult to decipher facts from fraudulent fiction.
To demonstrate the extent of medical malpractice during COVID you need to first know the Centers for Disease Control (CDC) reports deaths from pneumonia, influenza and COVID as a single PIC number. The CDC announces in January of 2022 that while PIC deaths, based on medical reporting, approach one million, the actual number is 94% less, or ~30,000.[6] To put this in perspective, during an average flu season ~36,000 pneumonia and influenza (PI) deaths occur. In 2017, there were 61,000 PI deaths. In other words, after a full year of COVID hysteria, the PIC death count is less than that of a normal PI season. If you think that’s outrageous, I invite you to embrace your skepticism because together we’re going to prove this assertion from multiple, and sometimes unconventional, avenues as we address truth and trust in the digital age.
Another dichotomy in our lexicon is the distinction between causation and correlation. In mathematics, causation and correlation define how statistical evidence inter-relates. Cause-and-effect relations are everywhere, but it’s more common for relationships to be correlated 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 efficacy of facemasks.
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 this example, electrical 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 direct 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 at work and the size of your annual salary increase. If your increase varies slightly from expectation, you can conclude the correlation is weak. However, if you observe your salary increase, along with others who routinely surf the Internet, is far below those of more dedicated employees, you might conclude the correlation is strong. Based on the strength of your correlation, you have information to help you modify your performance behaviors.
Our COVID journey begins on February 29, 2020, the day America reports its first COVID casualty. We’ll walk through multiple analyzes using data available on each respective analysis date. It’s important to note that our analyzes are based on essays posted on my website and submitted to major media outlets, such as the New York Times, Wall Street Journal, Washington Post, and Chicago Tribune, so a record exists validating when our analyses were performed.[7] When we assert in April 2020 the crisis will end in June, that projection is made in April without the benefit of postmortem clairvoyance.
As we proceed to COVID’s apex and subsequent remission, we’ll examine how truth and trust are manipulated to fit prescribed narratives. We’ll address how and why data is misused and power abused, why STEM professionals abdicate their responsibilities, why 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 COVID narratives or call for open peer review.
The Smith-Mundt Act of 1948 prohibited the U.S. government from manipulating citizens through propaganda,[8] but in 2012 the act is modernized to make it permissible. Fast forward to COVID where government, academic, media, and medical narratives emerge with anyone challenging their validity systematically canceled and you understand why countless people are banned on social media, removed from websites, publicly ridiculed, ostracized, and in some cases even imprisoned for challenging sanctioned narratives or questioning the efficacy of things like quarantines, mask mandates, and untested vaccines. Our journey through COVID will expose the misrepresentation of facts to fit prescribed narratives, such as government’s “Follow the Science,” campaign. My goal is to arm with the tools and information necessary to decide the real nature of the COVID crisis; I trust in your ability to make well-reasoned judgments once you’re armed with the truth.
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/
Link to entire Truth and Trust in Crisis book.
[1] History of the Manhattan Project: 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] COVID essays; https://rmdolin.com/commentary/
[8] U.S. Information and Educational Exchange Act: https://www.usagm.gov/who-we-are/oversight/legislation/smith-mundt/
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