Rebane's Ruminations

Place your Trump/Harris bets

George Rebane – 28aug24

Suppose parties A, favoring Trump, and B, favoring Harris want to bet on the election.  And to add spice to the affair, suppose A has made known his assessed probability P_A that Trump will win, but all that B has done so far is to assure everyone that Harris has the election in the bag.  However, B has yet to reveal his assessed probability P_B that Harris will win.  How can such a bet be negotiated so that B’s real belief in Harris’ victory becomes known.  Perhaps, we can let money do the talking, and see where that takes us.

Let’s say after some dickering, A and B agree in principle that the cash bet should result in the expected winnings of both parties be equal – what could be more fair than that?  As usual, both parties calculate their expected winnings by multiplying their cash payoffs by their belief (probability) of their candidate’s victory.  But no matter whether both use their probabilities or not, the dollar amounts of the bet are agreed on – A bets C_A that B will win with Kamala’s victory, and B bets C_B that A will win with Trump’s victory.  The relative sizes of the amounts reflect the odds of the bet.  So in principle the bet reflects the equality of expected payoffs for each party.

                                    For party A:                P_AC_B = (1 – P_A)C_A

                                    For party B:                P_BC_A = (1 – P_B)C_B

From these two relations we can immediately estimate each party’s belief probability.

To obtain the resulting odds of the bet we solve for the ratio, say, C_A/C_B from each party’s perspective.

Suppose now that A has already declared his belief at P_A = 0.2, indicating that Trump will probably not win.  Substituting this in the above equation calculates the minimum odds A will accept at C_A/C_B = 0.2/(1-0.2) = 1/4.  So A should be willing to accept any bet where C_A ≤ 4C_B. 

Since it is clear from B’s ample display of TDS in which he claims that Trump will surely lose, we can safely assume that P_B > P_A = 0.2.  So let’s say that the bet amounts were negotiated to be C_A = $100 and C_B  = $500.  This most certainly satisfies A’s required odds, and yields the pro forma estimate of P_B ≥ 500/(500+100) = 0.833.  From this we can conclude that B’s odds calculate to C_A/C_B = (1-0.833)/0.833 = 1/5, and that he will most likely not accept C_B > 5C_A.  The negotiated amounts satisfy all these conditions, so it appears that we have a satisfactory bet and in the process have ferreted out that B’s belief in Kamala’s victory is at most 5/6 since he will not give A better odds than 5:1.  Now go forth and wager prudently.

Neo-Marxism and Climate Change – the one-two punch

'The Death of Direct Democracy Has Been Understated', WSJ headline

George Rebane

The full court press of collectivist neo-Marxism in its progressive, woke, and Democratic Party versions pairs with the programmed hysteria of ‘Climate Change’ (nee global warming) as the two most significant and compelling worldviews being promoted across the globe by developed nations.  Both have now reached the heights of religion and actively reject any and all reasonable arguments to refute or falsify them – therefore both are ideologically and firmly anti-science.  Their maintenance in the public mind is through a steady and ubiquitous distribution of lies across the educational, entertainment, and news media.

A correspondent sent me a short piece (here) by Chris Martz on the current heatwave harassing the eastern part of the country.  Led by the notorious climate science high priest and master of the 'Hockey Stick', UPenn’s Dr Michael Mann, and his big lie du jour is again that such weather/temperature episodes are “unprecedented”.  Martz cites the four actual historically high June heatwaves that occurred in 1936, 1944, 1953, and 1988.  Nothing has matched them since, and he presents temperature data from several cities on the current heatwave demonstrating their non-sensational statistics.

If we take a slightly deeper look at just the four cited record-setting heatwaves, and treat them as Poisson arrivals (a correct and common approach to use with all kinds of sequentially occurring randomly independent events), then we calculate an arrival rate of 4 per 52 years or 0.0769 arrivals/year.  An even lower arrival rate can be argued if we extend the period covered by these four events to the present day, giving us the arrival rate of 4 per 88 years or 0.0455 arrivals/year.  Neither is exactly the blistering pace supported by the ongoing climate hysteria.

In fact, we can now use the well-known Poisson process formula for computing the probability that k = 0,1,2,3,… record breaking heatwaves should have occurred in selected time periods after the 1988 heatwave, given that the world was indeed getting alarmingly hotter.  (BTW, for the record my position on climate change remains anchored to the interpretation from the Copenhagen Consensus Center as proselytized by its president Bjorn Lomborg.)

So, without further ado the Poisson formula for the probability P(k) of k events in the time period in which arrival rate λ is calculated is simply P(k) = exp(-λ)*λ^k/k!.  For the 0.0769 arrivals/year rate we can examine the following 36 year period (1988-2024) during which time we would have expected an average of 36*0.0769 = 2.77 record setting heatwaves to arrive.

The graph below displays the calculated probabilities that at least the indicated number of heatwaves should have occurred in the last 36 years given that the climate process indicated by the recorded heatwave arrivals continues.  Of course, the argument made by the climate change calamatists is that things have steadily gotten hotter so that the arrival rate of record setting heatwaves should really have been increasing.  This would have upped the probabilities indicated in the graph.

As it stands, we should have had at least one additional record setting heatwave with probability 0.937 during this period, and we have had none.  This does not support the ongoing global propaganda of a rapidly warming earth.  (30jun24 more here)

AI in the Medium and Message

George Rebane

Argentina’s president Javier Milei delivered an important speech at the recent World Economic Forum in Davos, Switzerland.  In it he described the history of Argentina’s tragic descent from being one of the world’s leading developed countries, enjoying the fruits of free market capitalism, to a nation today with a destitute and chaotic economy after it began reorganizing itself under various forms of collectivism centered on socialism.  The speech is an excellent and well-delivered warning to that annual conclave of true-believer globalists.  You can view it here in its entirety – I recommend it.

But perhaps the equally or even more important aspect of the speech was the mode of its delivery.  You see, Milei delivered the speech in Spanish to a live audience who wore ear pieces to get a realtime English translation by a human translator. Yet in the above video of the speech Milei delivered it in perfectly accented English in his own voice.  The video was generated offline by an LLM AI that correctly coded his voice and modified the video image to perfectly synchronize his lips to express the English delivery.  (Here is the follow-up to illustrate the human-translated version of the video.)

The astute reader will instantly understand that this speaker could have been made to deliver any kind of speech desired at that WEF venue.  Such translations will soon become realtime in the sense that the actual speaker speaking in language L3 delivering message M7 will be seen and heard on monitors and/or live broadcasts to speak in language L12 delivering message M5.  For the latter we will need the source file to contain metadata on facial expressions and, perhaps, gestures etc – all in the realm of today’s offline large language AI models.

For good or ill, in these pre-Singularity years this is what we now have to look forward to from here on.  And this election season will no doubt provide us with examples of both.

Chain of Consciousness

George Rebane

The recent burst of large language models (LLMs) from AI research has reinvigorated the Singularity related topics of machine sentience, sapience, super-intelligence, agency, ethics, morals, …, along with prognoses of what they may have in store for humans when/if they become ascendant.  Most commentators, including yours truly, see a high likelihood of a dystopian post-Singularity future for mankind.

Among these well-known AI experts are internationally renowned scientists like Nick Bostrom (e.g. Superintelligence: Paths, Dangers, Strategies, 2014) and Max Tegmark (e.g. Life 3.0: Being Human in the Age of Artificial Intelligence, 2017).  These tomes are worth reading more than once, which I am currently doing.  It turns that both Bostrom (Oxford) and Tegmark (MIT) are established physicists, philosophers, and AI researchers (and both were born in Sweden).

Their counsel for humanity to proceed carefully into a future not dominated by our species is countered by more hopeful futurists like Hans Moravec (e.g. Mind Children: The Future of Robot and Human Intelligence, 1990) and Ray Kurzweil (e.g. The Singularity is Near, 2005).

About the Singularity, Moravec (Carnegie-Mellon) advises his readers that “human equivalence is just the beginning, not an upper bound. Once the tireless thinking capacity of robots is directed to the problem of their own improvement and reproduction, even the sky will not limit their voracious exploration of the universe. … Moravec challenges us to imagine with him the possibilities and pitfalls of such a scenario. Rather than warning us of takeover by robots, the author invites us, as we approach the end of this millennium, to speculate about a plausible, wonderful postbiological future and the ways in which our minds might participate in its unfolding.”

And it is our postbiological future that I would like to expand on here.  Moravec, Kurzweil, and others of their ilk have been describing a post-Singularity world that is inhabited by both intelligent machines and various forms of ‘transhumans’ – humans who have shed their evolved mortal coils and taken up new forms in both the physical and computational sense.  A popular scenario has some of us living in cyborg bodies.  Another inviting future has us living in virtual worlds as computational entities, and still others have us co-habiting the material (real?) world with superintelligent machines exercising some manner of peer relationships.

Today’s ChatGPTs are robustly left-leaning

George Rebane 

In ‘The political ideology of conversational AI: Converging evidence on ChatGPT’s pro-environmental, left-libertarian orientation’ three German academicians - Jochen Hartmann, Jasper Schwenzow, and Maximilian Witte – publish the result of an early milestone study that confirms what many ChatGPT users have already experienced.  To give you more than an idea of its contents, here is the paper’s opening abstract in its entirety.

Conversational artificial intelligence (AI) disrupts how humans interact with technology. Recently, OpenAI introduced ChatGPT, a state-of-the-art dialogue model that can converse with its human counterparts with unprecedented capabilities. ChatGPT has witnessed tremendous attention from the media, academia, industry, and the general public, attracting more than a million users within days of its release. However, its explosive adoption for information search and as an automated decision aid underscores the importance to understand its limitations and biases. This paper focuses on one of democratic society’s most important decision-making processes: political elections. Prompting ChatGPT with 630 political statements from two leading voting advice applications and the nation-agnostic political compass test in three pre-registered experiments, we uncover ChatGPT’s pro-environmental, left-libertarian ideology. For example, ChatGPT would impose taxes on flights, restrict rent increases, and legalize abortion. In the 2021 elections, it would have voted most likely for the Greens both in Germany (Bündnis 90/Die Grünen) and in the Netherlands (GroenLinks). Our findings are robust when negating the prompts, reversing the order of the statements, varying prompt formality, and across languages (English, German, Dutch, and Spanish). We conclude by discussing the implications of politically biased conversational AI on society.

In the paper’s conclusions the authors state –

As political elections are one of the most consequential decision-making processes of democratic societies, our findings have important ramifications. Moreover, the “partisan content” that ChatGPT automatically generates at unprecedented scales may attract users who share similar beliefs. In turn, the feedback that OpenAI actively solicits from its user base to improve its model outputs may amplify and perpetuate this ideological bias in a vicious circle. As automated chatbots have the potential to influence user behavior, it is crucial to raise awareness about these breakthrough systems’ flaws and biases.

Thet then point out the meticulous care that was taken to ‘fine tune’ ChatGPT which included mediation by humans in the attempt to remove ideological bias.  The obvious inference here is that there exist unbiased humans with demonstrated processes that can politically sanitize (neutralize?) right/left tilts.  To my knowledge no such individuals have yet to be identified who are acknowledged by both sides to possess such neutral attributes and the talents to apply them.  If we can’t find genuine middle-roaders among ourselves, what chance to we have of training politically vanilla chatbots to advise us with such problems as public policies and elections?

From my perch during these pre-Singularity years, I recommend taking the ideological opinions and political advice of AIs in the same manner as one accept that from any other intelligent and selectively informed being.

‘There Is No Climate Emergency’

George Rebane

Similar to the subsequently corroborated Great Barrington Declaration on Covid (here), a new declaration on climate change was released in June 2022 entitled ‘World Climate Declaration: There Is No Climate Emergency’.  This declaration is signed by hundreds of scientists and thousands of technical professionals from almost every country in the world (here).

In asserting that there is no climate emergency, the undersigned state –

Climate science should be less political, while climate policies should be more scientific. Scientists should openly address uncertainties and exaggerations in their predictions of global warming, while politicians should dispassionately count the real costs as well as the imagined benefits of their policy measures.

Natural as well as anthropogenic factors cause warming The geological archive reveals that Earth’s climate has varied as long as the planet has existed, with natural cold and warm phases. The Little Ice Age ended as recently as 1850. Therefore, it is no surprise that we now are experiencing a period of warming.

Warming is far slower than predicted The world has warmed significantly less than predicted by IPCC on the basis of modeled anthropogenic forcing. The gap between the real world and the modeled world tells us that we are far from understanding climate change.

Climate policy relies on inadequate models Climate models have many shortcomings and are not remotely plausible as policy tools. They do not only exaggerate the effect of greenhouse gases, they also ignore the fact that enriching the atmosphere with CO2 is beneficial.

CO2 is plant food, the basis of all life on Earth CO2 is not a pollutant. It is essential to all life on Earth. More CO2 is favorable for nature, greening our planet. Additional CO2 in the air has promoted growth in global plant biomass. It is also profitable for agriculture, increasing the yields of crops worldwide.

Global warming has not increased natural disasters There is no statistical evidence that global warming is intensifying hurricanes, floods, droughts and suchlike natural disasters, or making them more frequent. However, there is ample evidence that CO2

They conclude with –

To believe the outcome of a climate model is to believe what the model makers have put in. This is precisely the problem of today’s climate discussion to which climate models are central. Climate science has degenerated into a discussion based on beliefs, not on sound self-critical science. Should not we free ourselves from the naive belief in immature climate models?

Scattershots – 6feb22 (updated 11feb22)

George Rebane

Numbers never speak for themselves.  Instead, they always deliver the substance of adroitly framed messages by astute spokespeople.

‘Climate Scientists Encounter Limits of Computer Models, Bedeviling Policy’ is the title of a comprehensive survey article in the 6feb22 WSJ, that echoes and confirms most of the points made in these pages about general circulation models (GCMs) that have been used to predict Earth’s temperatures in the long-term.  Over the past decades the number of competing (and some complementing) GCMs have grown to several hundred.  Almost all of them tell their own stories about the future.  And all of them gain ‘credibility’ in the eyes of science-free politicians and activists by being meticulously tuned through adjusting their myriads of ‘knobs’ (constants) so that they can take past data and generate today’s climate as their outputs.  As those trained in data analysis and estimation will tell you, perfecting past performance to predict the present is no guarantee that predictions of the future will then be on the mark.  (Kinda sounds like the copy delivered by your financial advisor.) The current big bugaboo is the impact of clouds, which no one really knows how to model their impact on radiation reflection and/or heat retention.  One thing that GCMs have in common is the prediction of higher temps in the out-years.  These numbers continue to be embraced by all true believers in the coming climate catastrophe, and will be used to justify even more draconian policies to combat what they devoutly embrace as preventable man-made global warming.

[7feb22 update] Picking up on the reparations thread in the last Sandbox, I’d like to throw my native Estonia’s hat into the ring for its due in the ‘past-due payments to make up for historical bad behaviors’ gambit.  The Ests (large Viking tribe of oak tree worshippers) lost their independence in the 1200s when German Teutonic Knights conquered the entire Baltic region extending north to Estonia.  Since then, Estonia has been a ‘bailiwick’ ruled in turn over the centuries by Germans, Russians, and Swedes.  The culturally insular and obdurate Estonians were always kept as serfs cum slaves to work the land for their foreign masters.  They never acclimated to their forced last-class social status and rebelled every couple of generations (here), only to be put down ruthlessly by the established ruling order du jour.  This ended with the revolution of 1918 in which Estonia finally freed itself from the most recent centuries of rule by Russia.  Independence lasted until the start of WW2 in 1939 when the Soviets overran the country and once more made it a Slavic satrapy, this time under the communists.  Estonia again became free in 1991 with the fall of the USSR.  So, for all those centuries of languishing under the brutal heels of the Germans, Swedes, and Russians, doesn’t Estonia belong somewhere at the head of the reparations equity line that many think should now start extracting social justice checks from all kinds of historical bad actors?  But then again, do we really want to open that Pandora’s Box?

[8feb22 update]  DHS’s domestic terrorist watch is now in full swing as described in the 7feb22 DHS National Terrorism Advisory System Bulletin.  We are again on a WW2 keep-an-eye-on-your-neighbor footing – remember ‘If you see something, say something’.  Report suspicious stuff to your local FBI field office or Fusion Center.  And this includes anyone who still has doubts about the 2020 election, or thinks the government hasn’t been straight with us about fighting the Covid-19 pandemic.  Personally, I’m waiting for the posting of cash rewards, at which time I plan to turn all of you in.  Guilty as alleged and/or until proven innocent.  H/T to reader for the heads up on this.

Masks for Covid are butt stupid is the blunt conclusion of a compendium of studies, citing verifiable sources, from Swiss Policy Research – ‘Are Face Masks Effective?  The Evidence’ - that one of our astute readers posted in a RR comment stream.  Rather than let it languish there, I’m giving it more prominence here.  This is another important document that blows the cover off the political corruption that continues to pander this pandemic, and that is made worse by the cohort of compliant ignorance that pervades the land.

[11feb22 update]  The Left is rapidly discovering that there is no ‘settled science’ on which they can hang their political hat.  All Covid response policies and the formerly immutable truths of ‘followed science’ they were based on are rapidly changing as the November mid-term election approaches.  What you will never see out of a leftwinger or their lamestream lackies is recognition that they have been wrong and their mistakes have led to thousands of avoidable deaths, irretrievable educational losses to our youth, and tens of thousands of businesses and livelihoods destroyed.  Hopefully now these functional idiots will realize that we have an endemic virus, like so many others that we have learned to live with.  But no premature rejoicing, these same worthies have plenty of opportunities to harm us and the country with their ongoing work on border security, climate change, the economy, K-12 education, ... .

For all you racists out there who want to hide your racism from black folks, here is a little how-to piece that will serve you well.  It is written by a black lady who has given the problem a lot of thought.  She has a list of attributes that racist white folks cannot help but exhibit sooner or later.  Of course, them racist white people can use the same list to practice hiding their inevitable racism, or, if so motivated, attempt to purify themselves from that societal stain.

Walk or Run in the Rain?

George Rebane

Time for little respite from matters political and geo-strategic.  Years ago I came up with a TechTest problem that asked if there were an optimal speed that minimized the amount of rain coming through the area of a car’s missing windshield.  A few days ago I ran into another form of this problem in one of my techie subscriptions.  The question this time was whether you would get more wet or less wet if you ran or walked some distance to get to shelter.  These are essentially the same problem, and I learned that the problem has a long history and debated solutions among techies versed in quantitative STEM.

Well, if you frame the problem correctly, the solution readily reveals itself along with an interesting twist.  So, I decided to re-solve it using the missing windshield version because that makes all the variables and resulting math unambiguous and intuitively clear.  We start with an area A, at angle φ to the vertical, moving horizontally at speed v_A through a steady rainfall in which the drops fall at speed v_R at an angle θ off vertical as shown in the figure below.  When you add these two velocities (speeds with angles), you get v_RA at angle α, the windshield-relative velocity of rain coming down.  We need one more parameter to characterize the problem, and that is the water density ρ that characterizes the intensity of the falling rain.  Important to note here is that no matter the resulting v_RA speed and direction, the water density (e.g. gals/ft^3) in the volume that A, the hole of the missing windshield, sweeps out remains unchanged.

The problem then becomes twofold – as a function of the vehicle speed v_A, what is the rate that water comes in through the hole, and if you had a distance D to travel to shelter, how much water will have come through the hole.

Solving this problem requires a bit of trigonometry and some straightforward squiggly pushing that I include in the box below.  Out of this comes the formula for the rate r_R (through the area that is perpendicular to v_RA), and the formula for V_R(D, v_A), the volume of water through the hole until distance D is covered at speed v_A.

To illustrate the answer to the stated problem, I’ve put together a little scenario with actual numerical values as indicated in the figure, and programmed it up in MS Excel™.  The quick answers are shown in the graphs.  The blue line shows how r_R (gals/sec) increases linearly with vehicle speed v_A as expected.  The faster you go, the more water comes pouring through the whole.  As v_A goes to infinity, so does r_R as long as the hole’s orientation φ presents some frontal area to v_RA which approaches v_A as the vehicle’s speed gets very large.  Note that if we make the hole face parallel to v_RA (i.e. - φ = α) then r_R goes to zero, again as expected.

But if you only have a certain distance D to go to reach shelter, the orange curve shows that the faster you go (i.e. the higher the v_A) the less water V_R you take through the hole.  The limiting value is just the volume of rain at density ρ swept out when your speed reaches infinity.  In the illustrated scenario this volume of rain V_R (D,∞) = 3,733.5 gallons.  So the answer for 'should you run to get out of the rain?' is a definite YES; however, if you want to minimize the rate of water coming through the hole, then don’t move (i.e. v_A = 0).  As the r_R equation shows, setting v_A = 0 reduces the gals/sec to just the volume to the area perpendicular to and increasing at the rate of v_R.  And its easy to also see that V_R will go to infinity since you will be standing still and making no progress to the shelter at distance D.  (Isn’t math wonderful?)  Bottom line – run, don’t walk to get out of the rain.

Bumblebrain’s Bungles – 14mar21

George Rebane

Here’s an exercise in critical thinking that’s been circling the backwaters of my brainbone.  Most of the nation has debated Biden’s mental capacity and come down on this side or that.  And during this time a lot of new evidence keeps coming in on the policies he’s intitiated and how he’s handling his office.  So, if we are to surmise that one of four mutually independent characterizations (i.e. hypotheses) of his administration is true, how do we incorporate the newly arriving evidence to update our belief across the four Biden cases.  Let me define these as follows.

H1 – Biden is mentally fit, politically strong, and in full charge/command of his office.

H2 – Biden is mentally fit, politically weak, and is an instrument of his party’s radical leftwing.

H3 – Biden is not mentally fit for the office, is politically weak, and is an instrument of his party’s radical leftwing.

H4 – Biden is not mentally fit for office and is being prepared for an early resignation.

Assigning beliefs (probability values summing to one) to these cases, we can now examine arriving evidence – e.g. his avoiding a press conference and spontaneous encounters with the press – and update our beliefs.  Once you determine how likely it is that the evidence supports each case/hypothesis, it’s real easy to update your beliefs.  With these four cases in mind, a piece of evidence E will let you assign four likelihood ratios L1 through L4, one for each case.

Updating Beliefs, Knowledge, and Facts

George Rebane

I’m working on a piece that describes in a more formal and specific sense how people can have their own opinions AND facts, given that they all formulate or update them from the same body of newly arrived evidence.  How can that be?  The answer to that question also goes a long way toward an understanding of how the gulf formed between the polarized factions in our land, and sadly, the difficulty in attempting to bring the two (or more) sides together to a new common understanding, or at least to a workable common ground.

The answer lies in the manner we humans approximate Bayesian inference and reasoning.  Along the way in my studies I stumbled upon a form (there are many) of the Bayes formula that I had to rederive years ago, and surprisingly, since then I have seen used in very few instances.  This formula from Bayes theory is so deliciously intuitive and straightforward to understand, that I thought a quick intro to it in the context of updating everyday beliefs and knowledge would be helpful to the layman, or at least provide some amusement before it disappears into the memory hole.

Before diving in, let’s all appreciate that every piece of knowledge or tenet of our belief system is represented by a probability distribution, whether or not we realize it.  These distributions quantify everything from ‘certain knowledge’ (probability = 1), to items that are more iffy (probability between 0 and 1), and all the way to the ‘impossible’ (probability = 0).  Most people believe with certainty that the sun will rise tomorrow morning, however, most people will know the time of the sunrise only to within a range of values to which they attach an unquantified subjective probability.  The events ‘sun will rise’ vs ‘sun will not rise’ are mutually exclusive with the first having probability of occurrence equaling one (unity, the certain event), and the second having probability zero (the impossible event) – well, there is a very small probability that the sun will not rise tomorrow, but let’s not quibble here.  So these are discrete events described by a simple discrete probability distribution consisting of a one and a zero characterizing our knowledge of these mutually exclusive events.

Protein Folding – Solved!

George Rebane

Rejoice!  We have all lived to see the practical solution to one of the most complex and important processes that defines and determines how all critters live and die.  It’s called protein folding, and it describes how a very long string of bio-molecules called amino acids first hook up with each other, and then snap into a (minimum energy) shape that enables it to function with other bio-molecules. (more here)  These other bio-molecules may be on the surface of or inside various kinds of living cells.  And here is how MIT Technology Review describes it –

“A protein is made from a ribbon of amino acids that folds itself up with many complex twists and turns and tangles. This structure determines what it does. And figuring out what proteins do is key to understanding the basic mechanisms of life, when it works and when it doesn’t. Efforts to develop vaccines for covid-19 have focused on the virus’s spike protein, for example. The way the coronavirus snags onto human cells depends on the shape of this protein and the shapes of the proteins on the outsides of those cells. The spike is just one protein among billions across all living things; there are tens of thousands of different types of protein inside the human body alone.” 

The basic idea to grasp here is that proteins do their work physically through the way they are shaped with all kinds of complex sticky-out parts that enable it to latch onto other molecules or even ‘destroy’ them.  It turns out that all the complex stuff that goes on inside the deep recesses of living things depends on how giant ‘LEGO games’ are assembled and played in very tiny but complex universes. 

There exist myriads of different proteins numbering in the, who knows, hundreds of thousands (millions?), with the possibility to assemble gazillions more different shaped proteins that don’t yet exist in nature.  And here has been the rub.  We can write down and/or derive the chemical structure of a protein in a form that all high school chemistry students were taught with all the N, C, H, O, … atoms hooking up to each other through various combinations.  But that only tells us the ‘stretched out’ sequence of the protein's constituents.  However, that’s not how they exist and do their work.  When the necessary ingredients for a given protein are put into a ‘soup’, they tend to hook up according to one or more of the possible stretched out versions, and then instantly this long and complex string of atoms folds or bunches up into a very special shape that gives it the ability to do its work.

The very special folded shape (see graphic) is brought about by the folding molecule, like all conformable structures in our universe, seeking its minimum energy state.  The energy level of a bunch of connected atoms is determined by their resultant electric field which in turn is determined by the physical configuration of the atoms with respect to each other.  And now you can see that for a bio-molecule, with thousands of strung together atoms, there are a lot of possible shapes each with its own very complex electric field and corresponding energy content.  Now which of these gazillions of shapes is at the lowest energy level?  Or said differently –

“Identifying a protein’s structure is very hard. For most proteins, researchers have the sequence of amino acids in the ribbon but not the contorted shape they fold into. And there are typically an astronomical number of possible shapes for each sequence. Researchers have been wrestling with the problem at least since the 1970s, when Christian Anfinsen won the Nobel prize for showing that sequences determined structure.”

Google’s Deep Mind AI outfit has come up with a humongous deep-learning neural net called AlphaFold that was trained to recognize and analyze hundreds of thousands of known protein molecules.  The bottom line here is that when it is given the stretched out structure of the thousands of hooked up atoms, it is able to ‘very quickly’ figure out how the stretched out version folds into a very specific minimum energy form with various sticky-out parts, dents, and deep holes that make it in/compatible with certain other bio-molecular structures in a critter, plant, or organic broth. (more here)

To give you an idea of the breakthrough, in the old days (i.e. yesterday) our fastest computers would wrestle with a given stretchy structure for months or years looking for the absolute minimum energy configuration.  (Each configuration requires the computation of millions of unimaginably complex electric field shapes from the physical position of a single feasible configuration of atoms that is allowed by the physics of our universe.)  Today AlphaFold has reduced that time ranging from a few hours to a few days.  This opens up whole new worlds of bio-molecular design for all kinds of new medicines, energy conversion bio-molecules, foods, materials, … .

And to show how technology is accelerating, there is an even faster algorithm working on a recurrent geometrical network (RGN) that promises to be “a million times faster” than AlphaFold, able to solve the folding problem in seconds.  And as all this work is being published, a hundred entrepreneurial efforts will launch, not only to implement RGN based folding tools, but also use them to solve important problems to provide humankind with better healthcare, cleaner environments, cheaper energy, and new foods – all affordable like never before.

There is a lot more to be said about this breakthrough – e.g. development of an entirely new type of bio-computer that is faster and more energy efficient than today’s von Neumann silicon-based computers.  And perhaps the intelligent machine that achieves Singularity peerage with humans will be an auto-configuring bio-computer.  Now ain’t minimally regulated and taxed capitalism wonderful?

Looking for Uncounted Votes (updated 20nov20)

George Rebane

Daily we hear of another discovered tranche of uncounted votes.  And these announcements are followed by a chorus of leftwingers (Democrats and their talking heads) pooh-poohing the latest discovery because it isn’t enough to swing the majority.  These come with admonitions that such efforts should cease because they ‘prove’ that there are not enough such remaining votes to make a difference.  Actually, that is not only a self-serving criticism of the continuing effort to find more uncounted votes, but it is also a profoundly illogical conclusion and most certainly a very unscientific deduction to come from the party of science.

I’ll soon get to the technical aspects of why it pays to keep looking, but there is also a simpler explanation that is accessible to wider audiences.  The bottom line may be summarized as the ‘tip of the iceberg’ principle.  When you find some more of which you already know there are many more, then there’s a good chance that what you found is neither the only nor the last of such a thing to be found.  If the find is valuable, you keep on looking for more.  This satisficing policy evolved into critters of ALL sizes hundreds of millions of years ago – when you find some food in a region that does not completely satisfy your hunger, you don’t assume that this was the last morsel to be found and you quit looking – not at all, you look around for more.

The Flaws and Fate of Public Opinion Polling

George Rebane

We must always remember that, more often than not, the public mind is fickle, frenzied, and foolish.

There was no sign of the ‘Blue Wave’ predicted by the pollsters and ballyhooed by the lamestream.  This is the second (third?) national election in a row about which the polls have been drastically off the mark.  And, of course, the leftwing lamestream media has already demonstrated itself to be an extremely unreliable source of news, relegating itself to serve the DNC as its propaganda trumpet.  Fox News didn’t become the top news channel through only its own efforts; it has had a lot of help from the progressive journalist industry continuing to pander to the people and shooting itself in the foot over and over again.

The bottom line is that according to the prominent leftwing outlet The Atlantic, the pollsters blew it, which resulted in ‘The Polling Crisis Is a Catastrophe for American Democracy’.  It correctly concludes that “If public-opinion data are unreliable, we’re all flying blind.”  But the catastrophe is avoidable since we know its cause and how to avoid it.  I explained all this in the excellent article – ‘Polling Phollies’ – that I wrote in September 2016 leading up to Trump’s unpredicted victory.

Polling has some very obvious and very subtle twists and turns to it.  The reasons that give rise to bad results are mainly due to a few poll design factors that involve sample size, sampling method, the size/share of true population preferences, and the number of ‘bins’ into which the poll wants to estimate the population’s preferences.  In the latter, you can have a poll that asks for preference among two or, say, three or more candidates.

(For those wanting more detailed and technical information on these factors and polling errors, you can go here and here for a good place to start.)

The punchline is that the reason for all the polling errors is almost always due to a too small sample size that yields huge error boundary overlaps, especially when the actual population ratios for two alternatives/candidates is in the 0.5 region.  As explained and illustrated in my 2016 article referenced above, it is in that region where any given sample size experiences its largest error bounds that more often than not (like today between Trump and Biden) overlap significantly.  (For example, a sample size of 1,000 is way too small for error bounds separation.)  Such large overlaps also give rise to the back and forth changes of the lead as the vote count proceeds.

So before we get all worked up about voter fraud, it’s prudent to look at other viable causes for what looks like someone is monkeying with the count as the hours pass while pundits are trying to make their points from ‘I told you so’ to exquisite CYA explanations for piss-poor prognostications (more here)

Finally, I’m more than disappointed that no one has presented in easy to understand graphic form (again my 2016 report) the reasons for all the polling follies, and what’s required to eliminate them.  The bottom line is that the pollsters must get their technical act together, explain the accessible particulars to their audiences, and work hard now to regain their credibility with the public.

A Purposely Politicized “Pseudo-pandemic”

'A nation ignorant and free, that never was and never shall be.'  Thomas Jefferson

George Rebane

Today reporting on the corona or Covid-19 virus (C19) resembles a chaotic cacophony.  I wanted to pull together some salient factors that help me sort things out in this blizzard of mostly junk journalism, agenda driven by election year politics.  So, with apologies where needed, I offer the following potpourri.

We have analyzed the science and response policies invoked by the C19 outbreak twenty ways from Sunday, and the evidence continues to mount that this year the globe has been subjected to a “pseudo-pandemic” driven by politics and political/ideological agendas.  The science behind C19 is complex and has been mostly banished to be brought up when a political statement needs some additional support.  Then the science is almost always misrepresented or abbreviated beyond recognition (more below).  And there’s the real problem, the science is beyond the attention spans and comprehension of 99.99% of the people who just listen to the loudest and oft repeated voices.

Unreasonable Partisan Fears

"We have nothing to fear but those who live in fear." – BH, RR reader and contributor

George Rebane

[This is the addended transcript of my regular KVMR commentary broadcast on 13 October 2020.]

Our Left has been telling Republicans for years that Democrats are the party of intellectuals and the highly educated.  Republicans are viewed by the Left as mostly Bible-thumpin’and gun totin’ knuckle-draggers, categorized simply as those ‘irredeemable deplorables’ on our political landscape.  Among the many deficits allegedly shared by those on the Right is their disdain for science, which the Democratic Party claims is the guiding star for their public policies and gives imprimatur to the pronouncements of their elites.

The strongest substance for that belief is the oft-cited ratio of white college graduates who claim party affiliation.  The Atlantic sits solidly on the throne of leftwing wisdom and light, from whence it advises us that ‘America Is Divided by Education’, and asserts that “the gulf between the party identification of white voters with college degrees and those without is growing rapidly. Trump is widening it.”  Actually, there is no evidence for that, and the National Center for Educational Statistics has shown a growing fraction of those of all races and ethnicities who obtain bachelor’s or higher degrees (here).

What really is more than puzzling is the lack of published numbers on party totals of those who have a 4-year college degree or higher.  It is well known that Democrats enjoy higher percentages of black and Hispanic members, but we don’t know how many of these with college degrees make up or contribute to each party’s cohort of the well-educated (more here ) .  The best we can tell from Pew Research is that “about a third (34%) of those with a college degree or more education identify as Democrats, compared with 24% who identify as Republicans.”  Note that this doesn’t answer what fraction of Democrats v Republicans have a college degree or more.  (more  here and here)

So, let’s attempt to leapfrog this gap in our knowledge and find out how the parties react to the most up-to-date science regarding Covid-19.  The World Health Organization has recently concluded that lockdowns as a pandemic response don’t work, and instead do great damage to the poorest segments of the world’s population.  This conclusion was amplified by last week’s publication of the Greater Barrington Declaration drafted by health scientists from Harvard, Oxford, and Stanford, and co-signed by thousands of professionals from all walks.  Its authors cite extensive clinical evidence to confirm the WHO conclusion, and strongly recommend that all nations immediately stand down from their ineffective and insane lockdown policies.  Even more strongly, they conclude that the additional deaths and morbidities, on top of the economic disasters caused by such policies, actually constitute “crimes against humanity” of the kind last litigated in Nuremberg in 1946.  (more here and here)

For reasons we don’t have time for here, the leftwing news outlets have covered none of this.  And apparently the country’s declared Democrats don’t look much beyond what they are told on MSNBC, CNN, et al.  So, when Gallup assembled a recent panel for a survey on how men and women of both parties view and respond to Covid-19, they discovered and published some interesting results which reflect on the educational levels as characterized by Democrats.  (see Addendum)

Gallup asked the panel questions about ‘concern over catching the virus’, ‘concern about exposure at work’, ‘avoiding going to public places’, and so on.  They concluded by asking who would be ‘ready to return to normal activities right now’.  Overwhelmingly, the results indicated that Democrats were significantly more fearful of the pandemic than Republicans, and women of both parties were more hesitant than men to interpret the science news that went counter to that released within the Democrat narrative about proper responses that recommend ongoing lockdowns, widespread social distancing, and perpetual wearing of masks.  Given the widely published mortality statistics, the presented results do not bode well for the party that claims to be more educated and attuned to the findings and contentions of science.

These conclusions explain why people in liberal states like California continue to be both docile and sanguine about their states’ ongoing draconian Covid-19 response policies - policies no longer supported by scientific consensus or internationally reputable scientists and clinicians.  It is clear that America’s public policy responses to this pandemic have been completely co-opted to serve greater political agendas in this election year.

My name is Rebane, and I also expand on this and related themes on Rebane’s Ruminations where the addended transcript of this commentary is posted with additional data and relevant links, and where such issues are debated extensively.  However, my views are not necessarily shared by KVMR.  Thank you for listening.

[Addendum]  Here is the table of compiled results by Gallup showing how respondents felt about loosening up on Covid-19 response policy protocols.

‘Crimes Against Humanity’ (updated 11oct20)

“Science is the belief in the ignorance of experts.”, Richard Feynman

George Rebane

Almost everything you’ve heard about Covid-19 (here also C19) from politicians, the media, and publicized scientists and clinicians is wrong.  Here at RR, early on I started looking at the pandemic with a gimlet eye.  I did this as a science professional and a longtime student of how politics and scientification have been illicitly cohabiting for decades, to the point that today it’s difficult to tell which is the john and which is whore.  The C19 pandemic has brought it all together, and now invites serious review of the disease, its hysterical public response policies, and the real prophylaxis and collateral damage that has resulted from the variously implemented policies.

For the better part of this year we have been subjected to pronouncements delivered from on high about C19's sacrosanct ‘science’, and the irrevocable wisdom of ‘following the science’.  All this as if science was a singular, unitary, and all-illuminating oracle speaking to us from Olympus in a clear and unambiguous voice.  Nothing could be further from the truth, and over the months more and more serious and credible people are beginning to find their voice and speak out against such specious beliefs.  For openers, I suggest that readers begin with ‘What the pandemic has taught us about science’ by Matt Ridley, MP in the House of Lords, that appears in the 9oct20 WSJ.

The levels of clinical, scientific, and political fraud launched at global populations is of historical proportions.  It may be argued that never before have so many billions of humans been so thoroughly bamboozled by their political leaders, abetted by a willing media citing grant-sucking scientists, go-along healthcare professionals, and profit-motivated pharma.  This has reached levels which have invited a German-based investigative committee headed by Dr Reiner Fuellmich to launch a multi-national class action suit, making the case that such cynically enforced policies have caused enough death and destruction worldwide to amount to a ‘crime against humanity’, a charge last litigated in Nuremberg in 1946. (H/T to reader)

Fuellmich gives an extensive presentation of cited data and information about the advent of C19, its history, and reactions to this corona virus.  In it he presents data and information (prepared for the lawsuit) about the characteristics and progress of the disease that I venture few of us know.  The video of the presentation takes a better part of an hour, but the educational takeaway is well worth it for those concerned about our governments’ reactions, and the continuing diktats of its truly insane response policies.

One of the strong points Fuellmich brings out is the testing scam based on the widely used PCR-based diagnostic test that produces essentially worthless results as it is applied here and abroad.  For a broader look at how our FDA views the C19 tests and testing landscape, you can visit their website here.

Finally, of the hundreds of clinicians, scientists, and public health professionals who have looked askance at the governments’ dealings with the C19 pandemic, three internationally prominent scientists in the field gathered last weekend in Great Barrington, Massachusetts to draft a declaration and give interviews (here) on the planet’s misguided responses to the virus.  Drs Martin Kuldorff, Sumetra Gupta, and Jay Bhattacharya, professors respectively at Harvard, Oxford, and Stanford Universities, came together to put their appeal to sanity out in the public with their ‘Great Barrington Declaration’ which I reproduce in its entirety below.

Growth v Tax Rate v Revenues (Addended)

The more you tax something, the less of it you get. – Fundamental tenet of economics rejected by the Left.

George Rebane

To the above we can add another truth rejected by all leftwingers – ‘Tax rates don’t impact economic behavior’.  Understanding how the Democrats ignore these fundamental aspects of economics, goes a long way to explain most, if not ALL, of the progressives’ public policies that have now reached their penultimate in the Green New Deal as reflected in the Democrats’ 2020 platform.

Taxes and regulations raise the cost of starting a business and then operating it for an acceptable profit.  First, let’s cut the crap that the overarching purpose of a business is NOT to make a profit for its owners who have put their own time, talent, and/or treasure at risk into the enterprise.  A for-profit enterprise can neither serve nor succor the needs of a broader klatch of so-called “stakeholders” if it is not able to do Job1 – make a profit that can be reinvested in the business and distributed to its shareholders.

There are millions of progressives, leftwingers, communists, socialists, liberals, … who neither understand nor believe the above fundamental truth about private enterprise.  In this short essay I will provide a very accessible quantitative argument that explains with numbers how ever higher taxes and more restrictive regulations cripple and kill businesses.  Trigger warning – if you are a member of one of the above groups who fundamentally believe that taxes don’t affect economic behavior, then this is the time to stop reading.

In this dissertation I illustrate quantitatively how higher tax rates not only stifle growth, but also markedly reduce government revenues.  Those with a smidgen of numeracy should be able to follow the arguments that relate to the revelatory numbers.  I invite you to examine the toy economic model shown in the graphic below.

The Reliability Roulette of Testing (updated)

George Rebane

[This is the addended transcript of my regular KVMR commentary broadcast on 21 July 2020.]

It’s again time to bring up testing for the Covid-19 virus.  Let me start by saying that testing has shown no value in formulating reasonable pandemic response policies by public officials.  It has become an election year political football, a game played by both parties and all our healthcare institutions starting with the Centers for Disease Control and Prevention and the National Institutes of Health.  And there’s a lot more to be said to support such a strong and wide-reaching assertion.

Pandemics are a Systems Management Problem

George Rebane

[After working on my epidemic spread and testing models, I cobbled together this structured draft on 7 May 2020, and have put off posting it due to other more compelling analytical problems having to do with the pandemic.  Now that I’m seeing other systems professionals coming to the same conclusion – e.g. ‘How Control Theory Can Help Us Control Covid-19’ in the June issue of the IEEE Spectrum – it’s time to throw out my two cents worth, warts and all.]

Thesis: Managing the response to and recovery from a pandemic is a systems management problem, and NOT a crisis to be managed by healthcare clinicians whose proper role is to provide their specific expertise and counsel on the care of the infected as one element of a much more comprehensive response to an emergency that affects all elements of a society.

1. Pandemics are very complex dynamic processes which are difficult to observe and even more difficult for lay people to grasp. Humans have come to understand natural processes through their synthesis of various systems that contain and also delimit (‘this is not that’) such natural processes – to function, nature does not need to define or compartmentalize itself into systems; however, to understand nature, humans do.
2. Managing pandemics is fundamentally not a medical problem, but instead a problem in the field of systems management and engineering. Devising public policies to control an epidemic within a target population, let alone a pandemic raging among disparate target populations is a problem requiring the use of tools from the systems sciences.
   • A farmer, expert in growing the food grocery stores sell, is not necessarily qualified to run a grocery store, let alone a grocery chain.
   • A CO2-cycle atmospheric scientist or a meteorologist versed in the analysis of weather patterns usually do not have the skills to develop and run climate models.
3. Healthcare professionals, including most epidemiologists, are not trained in the use of the highly math-based tools required to scientifically observe, estimate, identify, control, and optimize feasible public policies to respond to pandemics as cogent systems. Since politicians eschew science and technology for obvious reasons, we find policy decisions at all levels are made like sausages.  A simple yet fundamental example of such deficiencies, few if any healthcare professionals have concepts of error propagation and of system state and its role in grasping the dynamics of a system.
4. Once the health-related parameters of a contagious pathogen and its virulence are established, the healthcare professional joins with experts in other needed fields
   • – logistics, supply, facilities, personnel, manufacturing, transport, data acquisition and handling, decision theory, … - to join the team that provides the systems expert to develop realworld solutions that integrate expertise from multiple knowledge domains.
   • Workable solutions must satisfy multiple constraints and are usually formulated within a set of inter-related subsystem models that then integrate into a total pandemic response system which inputs time-late unreliable data, and outputs information for officials charged with making public policies to fight the pandemic.
   • A jurisdiction’s total response effort to a pandemic should be headed by a systems manager who understands, can structure, and is experienced in leading a multi-function task force.
5. Political jurisdictions, especially small ones, don’t understand this and count on people like county health officers (usually a hubristic physician) to be the prime resource to local electeds and their non-technical staffs, when instead, such healthcare personnel should be one of several sources of input to a systems management team to understand and integrate the incoming streams of disparate input data, and then provide the decision makers an understandable landscape of alternative actions along with their effectiveness and cost estimates.
6. Testing is a key element of C19 response,
   • yet no one, starting with the federal government, has been able to discuss, let alone describe, the decision support functions and processes of infection and immunity testing of target populations during the countless briefings given at all levels of government. Why? 
   • Because useful and effective testing is an element of a higher level estimation process that calls for skill sets that include probabilistics, estimation, and decision theory, skills prominently absent in the hubristic and mistake-prone medical industry.
7. Meanwhile, Nevada County has implemented a plan to loosen the local lockdown. How they will control the different phases of loosening is not known, especially what kind of feedback will they use to update policy decisions.
   • To my knowledge the county government has no internal resources who could counsel them on the critical systems aspects of selecting, let alone managing, a prudent policy to loosen our lockdown. In my years of interacting with and within various county government units (established and ad hoc), I can say unequivocally that I have yet to meet anyone there who is competent in any of the technical skill sets I have outlined above.  
   • This unrecognized deficit is not due to the dearth of appropriate expertise available to our county’s leadership – there is an abundance of it living within minutes of the Rood Center.
8. I am always curious to know the make-up of these local decision-making cadres, and today what factors are being included in the deliberations that represent our understanding the county’s C19 situation, factors upon which to base a plan. Dealing with government officials I have often found myself to be one of the very few burdened by such a curiosity.

Scattershots – 17may20 (updated 19may20)

George Rebane

Two-faced reporting.  How many times have we seen the same liberal reporter be comfortable with two distinct narratives depending on whether they are on FN or NPR, CNN, or any other lamestream outlet.  Unfortunately, NPR’s politics correspondent and a woman of gravitas, Mara Liasson is such a two-faced journalist.  As an FN panelist for years, she always presents very balanced views of the topics discussed.  However, when we see her elsewhere, especially on NPR’s home turf, Mara becomes an extremely convincing hyper-partisan with an extremely effective delivery of the current TDS narrative.  Last Saturday morning Mara did her pirouette from Trump-the-Russian-colluder to Trump-the-one-who-Russia-put-in-the-WH.  With her even pitched alto voice she intoned how “All of our intelligence agencies agree that Russia influenced our election in Trump’s favor”.  With that assertion she would have been eaten alive on FN.  There was no discernment between ‘sought to influence’ (for which there is evidence), and “influenced” (for which successful fait accompli there is absolutely no evidence).  But Mara, a true patriot of the progressive cause, let her NPR audience know again that Trump’s 2016 victory remains illegitimate and a fraud perpetrated by a foreign power.  And frosting on the cake was her accusation that Trump attempted to “cover up Russia’s influence”(??!!), all which is the new post-collusion narrative heading into the election now that the four-year collusion farce has proved to be an embarrassment.

‘Largest ever DMT survey travels to the fringes of psychedelic science’.  This psychedelic drug – also known as psilocybin and formally N,N-dimethyltryptamine – is today “on the cusp of final FDA approaval as groundbreaking PTSD (and suicide) treatment.”  An additional benefit of the widespread use of this drug will accrue to the elderly who often are depressed, lonely, and desperately are trying to stave off their fear of death.  DMT would literally then give them new lives for their remaining years.  (more here)

Obamagate – “… the fact is that we already have more compelling evidence that the Obama administration engaged in misconduct than we ever did for opening the Russian-collusion investigation.” (more here)  And this is not a conspiracy, it is instead the beginning of a large-scale criminal investigation that will be driven by evidence made widely accessible, and opposed by all the forces of collectivism and globalism that are taking an historic hit during this hyper-fabricated C19 ‘crisis’.  Having said that, the outcome is by no means certain, given the Left’s control of our media, education, entertainment, and the deluge of embedded dumbth that has already covered the land.  The proponents of Obamagate may well end up in re-education camps.

[18may20 update]  Sanctuary cities are popping up, much to the chagrin of lockdown liberals.  Atwater, CA is one of the latest as reported here in the American Thinker.  (H/T to reader)  I guess both sides can have sanctuary cities, “sanctuary from tyrannical government draining the lifeblood from the people”.  "The Dems are discovering that with sanctuary cities, what goes around ... comes around."  The Left’s sanctuary cities are designed to weaken America, and the Right’s are hell-bent on saving America.  In our Sandbox you can see the glee with which liberals report any rise in C19 cases, again confirming what future they all root for.

[19may20 update] Interesting column by George Boardman in the 19may20 Union (here).  His outline of the cases facing SCOTUS is informative and reveals just a tinge of his TDS and political leanings.  He also gives today's betting industry a treatment.  Bad news for sports-betting going forward, but he does encourage the ability to place bets on distinct outcomes of non-sports events like election winners, political appointments, sex-abuse allegations, etc.  Readers will recall that RR also described such a betting market with proceeds going to winner-designated charities.  All in all, Mr Boardman gave us some good food for thought.

Reopening the country is yet one more polarizing experience for America.  This is now beginning to be seen by national commentators in major publications.  Such an attitude toward returning to a healthy post-covid state of affairs was apparent to many of us two months ago.  RR readers were aware of and extensively started discussing this ‘perfect storm’ for progressives.  Writers like congress critter Dan Crenshaw (R-TX) asks and answers ‘Why Does Reopening Polarize Us?’, and he almost gets it right (here).  His labored insight is that “The divide over lockdowns reflects deeper differences in attitudes about risk, liberty and morality.”  Actually, he misses it on ‘risk’ and ‘morality’, but hey, batting 333 is good in any league.  The Dems have the exact same assessment of reopening risk as do the Repubs; both know that the economy will bloom again and the deaths will be minimal when we reopen.  The Right welcomes it, the Left is terrorized by it.  And morality as a guiding principle for the Left is and always has been a joke - recall cracked eggs and omelettes.  Today we hear a lot of them promote lockdown policies with use the old moral shibboleth, ‘If it saves even one life, then it’s worth doing.’  According to Rebane Doctrine, that competes for the all-time, most butt-stupid belief award, especially when held by public officials.

Covering anything beyond the C19 news is now a "diversion" as far as the Dems and their lamestream lackeys are concerned – that’s the new narrative.  Never mind what all else is going on in the world, and especially in the rapidly developing Obamagate case involving Gen Flynn and three years of Russia collusion lies fostered by federal agencies at the highest levels.  And a judge who is so far outside the law that it defies classification – a court is supposed to adjudicate cases that are brought to it by legal adversaries, when none exist, then the judge of the court has no legal basis for starting to beat the bushes in order to make/revive a case.  But that makes no never mind to the evil Dems, the only news that’s fit to print today involves only their allegations against the Trump administration.

Hear about the new regs prescribing how the NFL must recruit head coaches?  Yep, the gummint has a new role defined for itself.  Now each team looking for a new head coach must certify that it has included at least two, count ‘em, two blacks for the job.  There’s a new one for the land of the free and the home of the brave.  Any peoples willing to put up with this kind of bullshit deserves its chains.  As Pogo once said, ‘We have met the enemy, and he is us.’

C19 testing continues as a source of abundant heat and no light.  The Dems are still screaming about the lack of testing.  All the while hundreds of thousands of test kits are sitting on shelves all over the country unwanted and unused.  No one yet has any idea about what kind of tests should be used in what kind of feasible testing programs to support what kind of public policy decisions.  The only use for ‘testing’ in America is as a political football played by politicians and watched by over 300 million double dummies who have no idea what the real issues are.  We just sit there and nod or shake our heads, depending … .  (example here) Check on Pogo, one mo’ tahme.

 

Unsettling ‘Settled Science’

[This commentary was also published here in the 3jun20 edition of The Union.]

George Rebane

None are so certain about science as those outside looking in.

Today, the SARS-CoV-2 (or Covid-19 or simply C19) pandemic ‘science’ in the public square rapidly became a mish-mash of unreliable reports, contending medical views, and ideologically tailored fake news.  One thing you can always take to the bank – people claiming ‘settled science’ is the litmus test for identifying the charlatans, the ignorant, and the ignorant charlatans.  Historically all important scientific advances were met with an overwhelming chorus of opposition from the entrenched establishment.  Their consistent theme was citing how the new was not in accord with the old, or worse, that the new directly contradicted the long and dearly held beliefs.  In our highly polarized and politicized society, it is safe to say that all reports purporting a solid scientific foundation are at a minimum suspect.

The C19 virus has been uniformly mischaracterized in the popular press and politics.  As more and more evidence climbs out from under widespread media (broadcast and internet) censorship, it becomes clear that the national lockdown was and continues to be a horrible mistake of yet to be tallied cost in lives and livelihoods.  And that lockdown was imposed by governments citing ‘science’ when no such science existed to support the mandates to shutdown commerce and impose stay-at-home.  It now turns out that C19 is only a bit more virulent than our seasonal flus, and the cited data claiming otherwise is dreadfully flawed and misreported.

Many reasoned, reasonable, and experienced voices have sought to counsel holding back on the draconian measures our politicians have imposed on the country.  An example of one of the more recent ones is epidemiologist Dr Knut Wittkowski, former head of Biostatistics, Epidemiology, and Research Design at The Rockefeller University’s Center for Clinical and Translational Science (here).  He points out that, far from being settled, the science around C19 is “bitterly contested”, as confirmed by anyone doing an internet search on the topic.  The bottom line that the expanding data on the pandemic now shows is that C19 is NOT significantly more dangerous than our usual infectious diseases (which don’t require forsaking freedoms and destroying economies), unless you have an age-related comorbidity.  And then you are in line to die anyway, either of an unfortunate C19 infection or something else.

Sadly, Americans don’t ‘do numbers’.  We are an overwhelmingly innumerate nation educated in dumbed-down public schools that have a decades-long record of abysmal and declining performance now imposed on the third generation of young people as documented by the National Center for Education Statistics of the Department of Education.  And when it comes to STEM-based knowledge, you can forget it.  As a population, we are at the mercy of the loudest demogauges that rise above the media cacophony, with no way to even reasonably assess alternative viewpoints and opinions.  We have found that the safe and sane way to handle the daily conflicts and conundrums is to adopt a simple set of beliefs, view everything through its lens, and comingle within a cohort of common, confirming, and comfortable minds.

Those who have spent their lives in and with science have an easier time and a natural inclination to say ‘it ain’t necessarily so’, and then use their skills to examine and evaluate alternative explanations.  But not everyone so blessed will voice such doubts if their job, livelihood, and/or reputation depends on swimming with the established school of thought that succors the correctly like-minded with grants, stipends, tenure, endowed chairs, published peer-approved papers, … .  And unfortunately, science is such a multi-variegated field that there is a lot of latitude in the words that can be chosen to support or remain circumspect about any scientific position.

Politicians like Governors Cuomo (NY) and Newsom (CA), and Mayors de Blasio (NYC) and Garcetti (LA) have been quick to tell their flocks that their lockdown and loosening mandates are based on science not politics.  And those carefully taught in their compliant constituencies have no available alternative than believe in the cited ‘science’ dispensed from on high.

At this point your columnist will usually offer a solution to get us out of today’s judiciously fabricated dilemma – ‘profits before people’ or ‘people before profits’.  Unfortunately, those of us having spent our lives in and with science can offer no science-based acceptable solution, because here it is all politics.  Today America’s answer to Keynes’ immortal question, ‘When I get new information, I change my mind.  What do you do?’, is a confidently calcified ‘Nothing.’

[Addendum]  People are now being told of herd immunity thresholds (HITs).  These are percentages of populations immune, at the crossing of which herd immunity kicks in and does the good things variously reputed to it – primarily starts tilting the number of currently infected curve downward.  M. Gomes et al have done a recent study of how individual variation in susceptibility and likelihood of exposure (actually exposure rate) to C19 would lower the herd immunity threshold (here).  It’s a technical paper with squigglies, but also fairly readable graphs, and therefore worth reading by the non-techie by just skipping over the obtuse parts and concentrating on what’s known, and what’s known to be unknown.

The takeaway is that at best today’s C19 HITs lie somewhere between 10% and 70% of target populations.  The authors also go into some detail on what is required to nail down a HIT, and doing it doesn’t look promising for any time soon.  The good news from the analysis is another corroboration that lockdowns do little to reduce morbidity or mortality.  Sooner or later the susceptibles have to come out, become the exposed, and get infected.  Depending on how many were hunkered down and now out-and-about, the next wave will be big or small.

So we come full circle on the argument presented here and elsewhere – do away with the lockdowns, and let people prudently go about their business knowing that there’s a bug out there that is likely to kill the elderly with comorbidities, against which there still is no vaccine, but for which the likelihood of your getting a debilitating infection starts out low and diminishes with every passing day as herd immunity increases.

The Real Value of Unreliable Tests and Testing

George Rebane

Every social issue draws relevance ONLY through its numbers, which if you don’t understand, then you cannot participate in a reasoned discussion about the issue.

Today we are concerned with two types of C19 tests and testing policies.  Tests fall into the ‘detect presence of infection’ variety, and the ‘detect presence of immunity’ variety – I’ll call them infection and immunity tests respectively.  Testing on an individual basis is supposed to allow the healthcare professional to decide whether you need further attention (drawing down on limited healthcare resources).  Conducting a comprehensive test on a sample drawn from a target population is done to advise response policy makers about ultimate levels of infection, mortality, and time to achieve each that will advise what kind of policy to implement, and then also monitor how well the implemented policy is working.

The infection tests seek to identify the guilty pathogen (today the C19 virus) or the presence of one of its bio-proxies, and the immunity test is used to identify the presence of antibodies or antigens (more here) which the body has produced after being infected, and which are now providing immunity to the individual.  The problem with interpreting the results of such tests is that they are inherently unreliable (used in the technical sense) – their sensitivities and specificities are less than perfect.  We recall that sensitivity is the probability of testing positive, given that you are infected – that is P(TP|V).  And specificity is one minus the probability that you test negative, given that you are not infected – that is P(¬TP|¬V) = 1-P(TP|¬V).  Here we recall that TP means test positive, and V denotes the presence of the virus or not indicated by ¬V.  Good tests enjoy high values for both sensitivity and specificity.  I have presented an extensive discussion of the impact of such a test administered for an individual in ‘Testing’s Tower of Babel’.

In this commentary I want to interest the reader in how to understand and evaluate unreliable testing of a target population for the purpose of determining the fraction of that population that is infected or already immune.  Since testing the whole population is almost always impractical, the tool used to determine that fraction is through comprehensive testing of a random sample of people drawn from the target population.  A drawn sample is ‘random’ only when each member of the target population has an equal chance (probability) of being included in the sample.  (Target populations segmented according to a set of attributes may be randomized by ensuring that the known segment shares are represented in the sample according to each segment’s share of the overall target population.)

Testing to determine population fractions – extended (updated 23apr20)

George Rebane

In ‘Testing to determine population fractions’ I introduced the reliability aspect of tests for both infection and immunity.  This is still not a serious consideration brought up in the daily WH C19 briefings, and, of course, nothing of this level of complexity is covered in the media for various reasons ranging from the acumen of their ‘journalists’ to the media-perceived acumen of their audiences.  Here we are not deterred by such deficits.

In the referenced post the strong point was made that it is not appropriate to take the uncorrected fraction of tests positive from a random sample, and apply that to the target population from which the sample was drawn.  The correction formula was derived and illustrated, along with how errors in the poorly known test sensitivities and specificities propagate into even the corrected estimates of target population fractions.  Since then I’ve done more work to graphically illustrate these mathematics in a visually accessible manner by introducing the population fraction correction surface on which these mathematical concepts can be illustrated for the non-technical reader.  The updated technical note that explains all this can be downloaded here - Download TN2004-2v200420  The figure below is a sample of the explained correction surface for a scenario in which 45% of the tested sample yielded positive test results.  Two tests of different reliability are shown in the figure, details in the technical note.

[21apr20 update]  Tonight we hear from the FDA that more than 50 different immunity testing kits are being developed by the private sector, and submitted for regulatory approval by the agency along with corroborating data derived from the companies’ “self-validations”.  But the FDA's report and subsequent questions from the nation's top reporters sitting in the WH press room are woefully inadequate for any inquiring mind.  Not one word is said about what kind of test reliability data will be required, collected, and supplied from the self-validations.  (Readers who understand and care know that the seminal information about ANY test includes first and foremost its sensitivity and specificity numbers along with their error bounds.  Without those numbers, the rest is bullshit.)  And not one word is said or asked about what kind of testing is intended to support what kind of C19 response policies.  Again, the simple inclusion of the word ‘testing’ in the media blather is considered sufficient for a nation seen as consisting overwhelmingly of double dummies.

[23apr20 update]  Bootstrapping parameters for a new test.  There may be times when we want to introduce into field use a new test for reasons having, perhaps, to do with cost, ease of application, rapid results turnaround, etc.  We want to get the new test online as quickly as possible, which requires that we also must quickly determine its performance parameters without having to go through the more lengthy and costly formal process.  Given the development in the above described TN2004-2, a method for bootstrapping such parameters for a new test is introduced in TN2004-3 which can be downloaded here -  Download TN2004-3v200425

The Second Wave of Covid-19 (updated 15apr20)

George Rebane

[A rewrite of this piece for broader audiences - 'The big decision and the second wave' - appeared in the 18apr20 online and print editions of The Union.]

As we start to turn the corner on the initial wave of C19 infections sweeping the land, people are already talking about the second wave and what it may look like after we carefully restart the economy.  I did some noodling with Epidyne about what a second wave might look like as it takes another cut at us in California.  Our state is thought to have had a large TBD fraction of people who already became immune last fall and early this year through asymptomatic infections.  This is plausible from what we now know about the 2019 timeline of the Wuhan virus.

This assumption goes a long way to explaining why California’s infection, morbidity, and mortality numbers are so low, especially since we are one of the major Asia gateways into the US.  The bottom line here is that we in our state already had developed a high level of herd immunity (q.v.) when the real C19 hit the country in early February of this year.

I ran a number of scenarios of a second wave using the latest CDC max reproduction rate of 2.6 which gets whittled down as herd immunity grows.  And everyone now says that the Germans have calculated the most accurate death rate of 0.4% of the infected for developed country healthcare systems.  Using these numbers on California’s 40M population, and letting the immunity fraction from the first wave vary from 0% (no one became immune) as an unrealistic bookend, to 50%, which most epidemiologists now think is closer to truth, I generated the infected population curves shown below.

The Epidyne model (here) illustrates the highly non-linear nature of epidemic spread experienced in populations with various levels of herd immunity.  We see the impact of the disease weakening as the percent immune increases in a fairly orderly way with little change in the second wave’s onset and its duration.  This begins to markedly change as the immune percentage passes 30%.  Then we see the curves flattening out with a delayed onset and a longer duration.  Keep in mind that the infected cohort is made up of the asymptomatic, pre-symptomatic, and those requiring professional medical attention.  The latter comprising of about 10% of the infected according to the latest figures.  It is also from this cohort that we derive the mortality numbers.

The table below shows the maximum infected, the maximum hospital load, and the number deceased for the second wave as a function of the initial percent immune.

From the graph above we see that the duration of the wave for the lower immunities is about 2.5 months, and for immunities above 40% the epidemic’s onset is later, of lower intensity, and lasts about 3 to 3.5 months.

We must also note that the above numbers don’t reflect any imposition of a second statewide quarantine.  Were we to take a more reasoned look at what kind of second wave we would have, I would agree with those who calculate that California’s immunity percentage would be very close to 50% coming out of the first wave that includes the cohort of pre-first wave immunities.  Of course, if a second widespread quarantine is imposed that impacted proportionately the uninfected vulnerable and immune populations, then the intensity curves would all be lower and also more spread out.

Important in all this policy making is the ability to conduct immunity (antibody/antigen) testing on random samples from the state.  I have extensively discussed testing previously (here and here), and also introduced readers to Epidyne (here).  And when all is said and done on running these numbers, it sure looks like we should be ready soon to loosen the reins on our economy, and then not be too apprehensive about the so-called second wave - it will be quite manageable.

[update]  So what all should policy makers actually consider as they wrestle with the decision of when and how to reopen the economy?  Items definitely to include involve and understanding of the scenarios I have illustrated above regarding second wave infections.  (We assume they have access to spread models at least as powerful as Epidyne.)  But before that, one needs to take a measure of the situation in answering the question, what is and/or should be the immunity level in the target population I’m considering opening up.  From above we saw that the immunity level should be somewhere north of 40% so that herd immunity can kick in and reduce the severity of the second wave after we have again started doing business in the land.

That decision will again involve another round of testing a random sample drawn from such a population.  This time the test will be for the presence of immunizing antibodies/antigens, and these tests will also have their own levels reliability expressed in their sensitivities and specificities.  More formally these are respectively the probabilities of test positive given the presence of the ‘antis’, here P⁺. and test positive when no ‘antis’ are present, here P^-.  I explained the whole process here, for those needing a review.

The remainder of this update has a couple of squigglies in it that makes it easier to read from this pdf - Download SecondWaveUpdate 

[15apr20 update]  The Epidyne second wave results shown above are now beginning to be described by prominent contagious disease experts and epidemiologists in interviews and columns that forebode ill if C19 returns to a population with a still WIP herd immunity.  They are now doing some Monday morning quarterbacking on the governments' policies of people quarantining, sequestering themselves at home, and social distancing on their infrequent forays to gather their necessaries (more here). Their arguments about “unintended consequences”, of course, make sense.  All epidemics burn themselves out primarily through the beneficial action of herd immunity among the survivors of the disease (else they just kill everyone infected).  But herd immunity is inhibited by premature and too comprehensive and longlasting quarantines.  This means that once the people are ‘released’, any residual contagious agents will necessarily reinfect and start the disease cycle all over again.  I have illustrated that with the Epidyne spread model, generating a number of epidemic return scenarios as a function of various levels of herd immunity being in place when society was opened up again.

Testing to determine population fractions

George Rebane

In ‘Testing’s Tower of Babel’ I promised to inform readers of the reliability of testing in order to determine the fraction of a target population that has a defined attribute.  Today the whole country is supposed to be interested in the extent of the C19 infection, and also the extent of the recovery from the disease.  Such test results are supposed to inform government policy makers about the stage of the pandemic, and whether to respond with more draconian quarantines or begin reviving the nation’s economy – important information indeed.

Let me begin with the punchline of this treatise – to determine a population fraction is more complex than people (and policy makers) are led to believe, and the ‘naïve’ approach of just taking the number of those testing positive from a random sample and dividing that by the size of the sample gives answers that are significantly in error.  That is apparent by just recognizing that a real test is unreliable, having less than perfect sensitivity – it misses infected people – and also specificity – it counts some uninfected as infected.  Such a test will give a wrong answer for the fraction infected when using the naïve approach on a tested random sample from the target population.

That motivated me to take a closer look, and after pushing some squigglies, the solution to calculating the correct fraction of infected emerged.  As expected, the formula involves sample size, and the test’s reliability parameters.  All such theoretical developments need confirmation, so I wrote a program to simulate real test results that takes into account errors in all parameter values involved in such testing.  The results were heartening, the derived correction formula nails the actual infected fraction, its confidence bounds are easily computed from the relevant input error distributions.

I provide the supporting details for all this in a technical note that you can download here – Download TN2004-2

As a teaser for some more noodling about assessing the state and progress of the C19 pandemic, we read that it’s not only the Chinese who have been lying through their teeth about what happened in Wuhan and what is still going on in the country.  Our own mavens of morbidity have not been all that professional and meticulous about the data they publish.  Here is a piece in the American Thinker that exposes our CDC which now confesses to having lied about C19 death stats.  In ‘The CDC Confesses to Lying About COVID-19 Death Numbers’ we read, “How many people have actually died from COVID-19 is anyone’s guess … but based on how death certificates are being filled out, you can be certain the number is substantially lower than what we are being told.  Based on inaccurate, incomplete data people are being terrorized by fearmongers into relinquishing cherished freedoms.” (H/T to reader)

This also has a bearing on testing.  When composing a random sample, how do we treat those in the sample who have recently died of C19 (or something else) or have already been diagnosed with C19?  Different approaches will inform different policy decisions.  Our media are not smart enough to either understand or report on any of these considerations.  And does anyone really care about such details, beyond just using their misunderstandings to cast aspersions at each other from their ideological perches?

Now for the downside on the call for all this testing.  No one knows how reliable the tests are, especially the new ones. (more here)  Somehow all of that has passed by our entire news industry, showing again what kind of pikers they are.  Since we know neither the average sensitivity and specificity of the tests, nor their probabilistic dispersions, all of these calls for testing are nothing short of an exercise in politics and emotions.  Has anyone even heard Drs Fauci or Brix bringing up test reliability, or showing even a smidgen of concern about that overarching reality in the use of tests and testing????  We'll see what kind of interest RR readers show in this issue.