We discuss common errors and fallacies when using naive “evidence based” empiricism and point forecasts for fat-tailed variables, as well as the insufficiency of using naive first-order scientific methods for tail risk management.
We use the COVID-19 pandemic as the background for the discussion and as an example of a phenomenon characterized by a multiplicative nature, and what mitigating policies must result from the statistical properties and associated risks. In doing so, we also respond to the points raised by Ioannidis et al. (2020).
About two years before the recent collapse, at a dinner, a then (slow thinking) member of the Lebanese parliament kept bugging me for an economic forecast. There was already some anxiety in the air. My answer was that we were facing imminent financial disaster, but that it was not necessarily bad news, long term. Why? Because such a total collapse could lead to natural responses that are better than the one we would have spontaneously, going from patching bad stuff to patching worse stuff. The lira was artificially kept too strong for any industry to survive and the financial system (the Ponzi) was sucking up all the money and destroying the economic substructure. But my point was that the (unavoidable) collapse would lead to an adaptation, the weaning from chronic foreign “loans” and, possibly, a huge bounce. De-financializing the country was a necessity, and people never do that spontaneously. Nothing was going to be fixed without a collapse. Was I optimistic? pessimistic? He was trying to figure out what I was saying and couldn’t get it as it did not fit his elementary static classification.
Incompetence and Errors in Reasoning Around Face Covering
SIX ERRORS: 1) missing the compounding effects of masks, 2) missing the nonlinearity of the probability of infection to viral exposures, 3) missing absence of evidence (of benefits of mask wearing) for evidence of absence (of benefits of mask wearing), 4) missing the point that people do not need governments to produce facial covering: they can make their own, 5) missing the compounding effects of statistical signals, 6) ignoring the Non-Aggression Principle by pseudolibertarians (masks are also to protect others from you; it’s a multiplicative process: every person you infect will infect others).
In fact masks (and faceshields) supplemented with constraints of superspreader events can save us trillions of dollars in future lockdowns (and lawsuits) and be potentially sufficient (under adequate compliance) to stem the pandemic. Bureaucrats do not like simple solutions.
The COVID-19 pandemic has been a sobering reminder of the extensive damage brought about by epidemics, phenomena that play a vivid role in our collective memory, and that have long been identified as significant sources of risk for humanity. The use of increasingly sophisticated mathematical and computational models for the spreading and the implications of epidemics should, in principle, provide policy- and decision-makers with a greater situational awareness regarding their potential risk. Yet most of those models ignore the tail risk of contagious diseases, use point forecasts, and the reliability of their parameters is rarely questioned and incorporated in the projections. We argue that a natural and empirically correct framework for assessing (and managing) the real risk of pandemics is provided by extreme value theory (EVT), an approach that has historically been developed to treat phenomena in which extremes (maxima or minima) and not averages play the role of the protagonist, being the fundamental source of risk. By analysing data for pandemic outbreaks spanning over the past 2500 years, we show that the related distribution of fatalities is strongly fat-tailed, suggesting a tail risk that is unfortunately largely ignored in common epidemiological models. We use a dual distribution method, combined with EVT, to extract information from the data that is not immediately available to inspection. To check the robustness of our conclusions, we stress our data to account for the imprecision in historical reporting. We argue that our findings have significant implications, including on the extent to which compartmental epidemiological models and similar approaches can be relied upon for making policy decisions.
Nassim Nicholas Taleb∗, Pierre Zalloua, and Dan Platt ∗Corresponding author, email@example.com Dec 2019
We discuss the inadequacy of covariances/correlations and other measures in L-2 as relative distance metrics. We propose a computationally simple heuristic to transform a map based on standard principal component analysis (PCA) (when the variables are asymptotically Gaussian) into an entropy-based map where distances are based on mutual information.
Tail Risk of Contagious Diseases Pasquale Cirillo∗ and Nassim Nicholas Taleb† ∗Applied Probability Group, Delft University of Technology †Tandon School of Engineering, New York University Forthcoming, Nature Physics PDF Download Link: academia.edu/42307438/…
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When, along with applied systems scientist Dr Joe Norman, we first reacted to coronavirus on 25 January with the publication of an academic note urging caution, the virus had reportedly infected fewer than 2,000 people worldwide and fewer than 60 people were dead. That number need not have been so high.
The U.S. government is enacting measures to save the airlines, Boeing, and similarly affected corporations. While we clearly insist that these companies must be saved, there may be ethical, economic, and structural problems associated with the details of the execution. As a matter of fact, if you study the history of bailouts, there will be.