Rebane's Ruminations

The Beta-Rho Sham? (Addended)

George Rebane

Here’s a little something from my financial engineering toy box that one or two RR readers may find interesting.  The discussion is a tiny bit technical, but the pictures should be accessible to all.  Most investors know that a stock’s beta (Greek β) is a number that indicates how well the security tracks its market, say, the SP500.  And rho (Greek ρ) measures the correlation between, say, the price series of two stock’s – the way they both zig and zag together, or how much one zigs while the other zags.  I’ll try to keep this short.

From august sources like Modern Portfolio Theory (MPT) by nobelists Markowitz and Sharpe, we learn that if a stock’s β equals one, then it more or less goes up and down in similar percent changes with the market.  If β > 1, then the stock’s swings have a greater percentage than the market’s; and if β < 1, then the stock’s swings behave in a more reserved manner than do the market’s.  Riskier stocks have the bigger swings, and more conservative stocks have the smaller swings.  The referenced formula (here) for beta reflects this with its inclusion of the standard deviation of the price swings.

As stated above, ρ measures in how tight of a formation two stocks will fly.  The range of ρ is between +1 and -1, with the positive values indicating tighter formations, and ρ = 1 denoting a perfectly tight formation matching each other’s zigs and zags.  A small or zero ρ means that the stocks pretty much act independently of each other.  In designing your investment portfolio from a selected shortlist of stocks, MPT teaches that you should try to avoid stocks that pair with high ρ values.  Doing probabilistic math recommends that you pick stocks that pair with the lowest ρ values possible to give you a low volatility (i.e. low risk) portfolio.

Now after you understand all that, we’re going to throw a monkey wrench into the whole thing, a little bit of information you will not hear from your well-paid wealth advisor who manages your portfolio.  (Drum roll please.)  Betas and rhos, calculated from past performance, are pretty much worthless measures of securities performance for portfolio design.  Why?  Well, that answer depends on what is known as the investor’s ‘investment horizon’.  That’s the period into the future over which the investment plan is supposed to hold with numbers such as expected portfolio appreciation, its most likely upper and lower limits, and so forth.

Covid Policies and Continued Testing (Addended; updated 22dec21)

George Rebane

Governments here in America and around the world are fumbling around with Covid response policies as every new variant is discovered and infection cases surge and retreat.  People continue to be assured that the public agencies and their political leaders are “following the science”, even when there is no evidence of such following presented or demonstrated.  But as we have all experienced, the government agencies, ‘experts’, and politicians are all over the place with their advice, prescriptions, and mandates.  It is clear that neither the national nor global collective knows how to properly respond to the pandemic (e.g. here)  More disturbing are the ongoing subterfuges that leak out in the media and online.  An extremely revealing and disturbing interview is by Tucker Carlson of Robert Kennedy Jr, author of the current best seller The Real Anthony Fauci (2021).  It has been pulled by YouTube and FN, but is still available on Bitchute here.

In my many-years experience with healthcare decision making, I have found that practitioners in the medical industry are profoundly ignorant of uncertainty, risk, utility, estimation, and in general critical interventions that are affected by uncertainties (i.e. depend on probabilistics).  This assessment applies especially to the physicians with whom the patient interacts.  These people are uniformly condescending or arrogantly hubristic in how they hide their ignorance of the maladies in question, and what is required for the lay patient to make a reasoned and understandable decision.

Perfidy aside, nowhere is such practice more visible to the technically trained than in the field of public health.  A current posterchild demonstrating this is how little and erroneously we are informed about Covid testing.  We are told that testing is somehow critical to reducing the spread of the disease – the more testing the better.  Tests are communicated to the layman as consisting of one uniform process that somehow reliably determines your infection status AND your ability to infect others.  Science tells us nothing could be further from the truth.

Ignorance of causes and effects should be usefully represented quantitatively.  Such ignorance is no excuse for avoiding quantitative decision support techniques.  For example, here is an AMA article that talks about test types, dependers, and whyfors, but gives you no information about the involved probabilistics (e.g test sensitivities and specificities) for decision making.  Today machines can be designed to incorporate more knowledge than any one or group of practitioners can manage in literally any specific domain of expertise.  As a consequence, where such technology is applied, AI-based decision support systems outperform their human counterparts every time – and this is specifically the case in medicine and healthcare.  Here is Nobelist Daniel Kahneman on AI bettering human decisions. (here and here)

‘I robot’ welcomes ‘I quit’

George Rebane

RR reader and longtime friend sent me a current piece on the theme of machines replacing humans in the workplace (here).  The article focuses on the current labor crunch reported under the ‘I quit’ spate of news articles.  Hard to tell how many jobs are actually going unfilled, but the number is somewhere north of 6M.  Workers are not applying for a couple of reasons – they don’t have the skillsets required and they’re already getting enough government payouts to support their lifestyles.

As you might guess, those greedy capitalists with open job slots are not just sitting on their thumbs.  They are doing everything possible to reduce the cost of expensive, undependable, recalcitrant, and quirky labor and substitute machines wherever possible.  The more workers, on or off the job, resist, the faster the development and acquisition of replacement robots.

But I and most observers of this process believe that the road to robotics will not be traveled quietly with just the judicious substitution of universal basic income (UBI).  One of the core attributes of Man is his irrepressible demand to be relevant in his environment, no matter the size of his UBI check.  This will give rise to a spate of modern age luddite riots demanding that robots be excluded from a catalog of jobs to be reserved for humans.  These uprisings will be promoted and championed by leftwing politicians within a whole new category of vote-buying policies.

RR is a repository of many commentaries that deal with this most human problem of pre-Singularity systemic unemployment.  A sampling of these from just the last ten years are available here, here, here, here, here, and here.

Extraterrestrials and Religion

George Rebane

Recently the Ignatius Forum held a session on ‘The Future of Space’ at our National Cathedral attended by theologians, government space officials, private sector space entrepreneurs, and academics.  Among the latter was Prof Avi Loeb of Harvard who is also active in SETI and a member of the international SETI group (of which I am a member) administered by the Berkeley SETI Research Center.

While the larger topic was finding ETs, a more focused topic of interest was how we earthlings bring the existence of ETIs into concordance with our notions of God and religion.  Prof Loeb is not a person of faith, but made it clear that “science and religion are not necessarily in conflict, as long as one is careful not to ignore the boundary between physics and meta-physics. … In finding advanced extraterrestrial intelligence, religion might simply reflect advanced science with a twist. Traditional religions described God as the creator of the Universe and life within it. They also suggested that humans were made in the image of God. But these notions are not necessarily in contradiction with science. A sufficiently advanced scientific civilization might be able to create synthetic life in its laboratories; in fact, some of our terrestrial laboratories almost reached that threshold. And with a good understanding of how to unify quantum-mechanics and gravity, an advanced scientific civilization could potentially create a baby universe in its laboratories. Therefore, an advanced scientific civilization might be a good approximation to God.” (more here with links to the forum)

Long time readers will recognize this as a position much discussed over the years in these pages, especially as it relates to my position as a copernican viz science.  In that, Loeb and many other scientists are kindred spirits.  What impressed me is the Harvard astro-physicist’s stance on the perspective humanity should assume when it considers cosmic matters - these being humility, modesty, and calmness.  In the greater scheme of things we are not important enough about which to make a great fuss.  I have dwelled on the quantitative measures of our ordinary insignificance in my SETI paper (here) among other postings.

In the final analysis of integrating religion, science, and an intelligent creator, I find nothing awkward or contrary in my particular understanding of Christianity as a faith compatible with civilizations across the universe.  It is a particularly comforting belief system to embrace as we seek to understand the Upanishad’s ‘Tat tvam asi!’

Postscript – ‘Who am I?’ is a question often heard from people seeking to find a deeper significance to their existence.  It is the wrong question, and we already know its answer, since ‘who’ seeks to identify one as a particular member of a known set.  ‘Tat tvam asi!’ points to the much deeper question of ‘What am I?’, the question which seeks to know the essence of the set itself.

Speaking Algoristically

George Rebane

[Reading some recent papers on the current problems in the advance of deep learning, and causal inference as the next quantum leap in machine intelligence, I ran across a lot of discussion about what are still called algorithms.  It recalled the summary of a more facile syntax that I and several others have been promoting.  More to be said later about the contention between the ‘data fitting’ of neural nets, and ‘data interpretation’ capability of causal nets.]

al·go·rithm n. A step-by-step problem-solving procedure, especially an established process for solving a problem or accomplishing a task in a finite number of steps. (more here)

When we relieve ‘algorithm’ of its codal baggage, we are left with the simple, compact, communicable, and elegant ‘algo’.  In this form the entire enterprise and field of algorithmics (better ‘algoristics’) can be efficiently communicated, bypassing current attempts to twist its ‘rithm’ tail into tortured forms like “algorithmatized” or use many other words to describe what can be expressed directly.  Consider the following little dissertation on the subject.

Any process is algorizable and can be algorized by an experienced algorist.  Algorized processes benefit from being readily teachable and passed on to others.  Developing algos is also an art because there is usually more than one algo that can algoristically capture the essence of a process.  Algoristically speaking, most algorized processes can always benefit from additional algorizing.  The history of algoristics is full of such algoristical efforts of developing ever more efficient algos.  In the affairs of Man never disparage algorization, for it simply is the heart of the matter. No light bursts forth in your mind save in an algorizable form, for without the ability to algorize we proceed as the blind. It always pays to be an algophile instead of an algophobe, for expressing and retaining knowledge, it all comes down to algoristics.

One of history’s greatest algorists was Henry Ford.  He took the notion of interchangeable parts (first developed for the manufacture of military arms) and algorized the manufacturing of cars into the assembly line.  Realizing the algorizability of automobile manufacturing, his algorized assembly line was algoristically so structured that parts didn’t come to the assembled car, the car went to the parts.  That was the breakthrough algo that changed a country and then the world.

• Algo
• Algorist
• Algoristics,
• Algoristical
• Algoristically
• Algorize
• Algorized
• Algorizable
• Algorizing
• Algorization
• Algorizability
• Algophile
• Algophobe

‘A Thousand Brains’

George Rebane

Jeff Hawkins, cofounder of Numenta, is the author of A Thousand Brains – A new theory of intelligence (2021).  To many, me included, Hawkins has been a national treasure since his first book On Intelligence (2004).  After inventing the Palm Pilot and becoming wealthy, Hawkins has devoted his life to brain research as it impacts our understanding of intelligence, consciousness, and our ability to create machines with these attributes.  In this enterprise he and his colleagues have devised a new understanding of how our brain’s architecture, specifically the neocortex, is able to perform all the wondrous functions which we all take for granted.

My son-in-law who is a senior AI researcher at Microsoft Research, sent me the book as part of our ongoing conversation that includes the art of the possible in intelligence studies and, of course, the Singularity.  For me Hawkins’ book was an immediate page-turner that jumped to the head of my list of books-in-progress.  The 257-page book is divided into three parts: A New Understanding of the Brain, Machine Intelligence, and Human Intelligence.

In the first part Hawkins builds on the work of famed neuroscientist Vernon Mountcastle, who revealed the structure of the neocortex (the new brain) as consisting of hundreds of thousands of cortical columns or cores, each of them with pretty much the same make-up of interconnected neurons which also have very select and important dendritic connections to other cores, often located on the other side of the brain.  The important concept here is that the cortical column is itself a tiny brain that is able to learn and retain ‘frames’ of perceived reality which it then assembles with other cores containing complementary frames to result in a voting process from which our experienced reality emerges.

These common components that make up the neocortex are ubiquitous in their functioning.  When those that get inputs from our visual subsystem, these become visual processors and generate our visual world; when connected to our auditory subsystem, they do auditory processing and create our world of sounds, and so on.  Hawkins’ exposition of how these cortical cores connect to our sensors, manipulanda (e.g. hands, feet), and parts of our ‘old brain’ is an absorbing read.  On every page, the reader’s brain is coming up with all sorts of alternative AI implementations that have now been introduced into his art of the possible.

And for me the stuff I learned in the first part makes the second part on machine intelligence very exciting.  The people at Numenta and other similar AI companies are all experimenting with synthesizing software and hardware structures that can mimic the functions of the cortical core, and then devising ingenious architectures that join thousands of these cores into new kinds of learning machines.  The commonly recognized holy grail among these enterprises is AGI – artificial generalized intelligence, of which today not even the most advanced forms of deep learning artificial neural nets are capable.  All of the ballyhooed AIs today are still one-tune bands, no matter how sophisticated tunes they are able to recognize and play.  None can efficiently construct their surrounding reality and transfer that knowledge (i.e. generalize) to operate in different environments or situations like any year-old human infant, or, for that matter, as can a lot of critters.

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.

‘Luxury Floating Real Estate’

George Rebane

Living on a huge manmade island may be the thing of the future for those who can afford it and want to escape from the ever more contested and politically roiling lands that cover about 29% of the Earth’s 197,000,000 sq mi surface.  The first of these islands is being built off the Bahamas by an outfit that calls itself The Blue Estate (here).  Multiples of such islands are planned to reside off Dubai in the Persian Gulf which are advertised to be miniature havens of German, Swiss, Italian, and Swedish culture.  The Blue Estate plan envisions an island measuring 1,500 by 1,000 meters as shown below.

The island’s size, modular construction, and high (130ft) free board, augmented by another 100+ foot extendable windwalls, we are told will assure the structure’s calm survival in the roughest of hurricanes.  The island will also have propulsion and guidance so that it can transit open ocean areas to avoid storms.

Yet to be explained is to what extent the island will be energy self-sufficient using solar, wind, and wave energy extraction technologies.  Its home area will be in international waters, minutes by air away from Miami and Bahamian towns.  Apparently, food and other consumables will be imported.

The community will also be economically self-sufficient by being home to clean commercial enterprises and the successfully retired.  The commercial activities will be heavily IT oriented with ample satellite connectivity.  I can envision that it might also be home to small R&D firms engaged in computer science, genomics, and similar areas.  Located in a tropical sea with abundant (300+ days/yr) sunshine, the island can also operate a sizeable nearby sea farm containing various flora supported by an underwater matrix which will give rise to a whole new ecosystem of rich sea life similar to what exist around oil drilling platforms and the Navy’s ‘kelp farm’ (off San Clemente Island) on which I worked years ago.

One of the potential attractions of such seaborne communities may be their ability to provide social stability through a discriminative selection of residents.  It is clear to me that some such island communities will emphasize cultural cohesion of their residents.  Offering such cohesive environments seems to be the goal of the planned Persian Gulf islands that will focus on providing culture-specific resort living and holidays.

As an entrepreneur and systems nerd, I am drawn to the dreaming and planning of such habitats, and in the present case could go on for pages contemplating different concepts and their supporting technical, commercial, environmental, educational, cultural, … support systems and institutions.  But to me the problem of having the seas support a considerable population of such islands in different parts of the world will come down to the solution of the inevitable political problems that will arise and need solutions that can be implemented globally.

Because these island communities will attract (and be limited to) the smart, productive, and ‘financially capable’ people, they will naturally become hotbeds of wealth generation.  And they will therefore become tax and tariff prey for the land-based political grifters who maintain their collectivist regimes by bamboozling their poor and huddled masses yearning to be free.  These people need state supplied succor that their political leaders will not allow them to generate for all the reasons we have discussed in these pages.

In such political landscapes those who can will escape to available locales with lower taxes, regulations, and abundant liberties.  This we see happening in today’s American migrations, and have witnessed for decades (centuries?) in Europe and Asia where the productive congregate in enclaves such as Singapore, Taiwan, and formerly Hong Kong.  Therefore, new political paradigms need to be discovered if the financially and culturally attractive island communities are to thrive and multiply.  Else the early ones will just be economically harvested to oblivion by the land-based stifling autocracies that see such free and enterprising enclaves as low-hanging fruit.

Much more to be said about all this.

Reliability in Predicting Termination of Minimally Known Processes

George Rebane

This mildly technical posting is intended for those concerned with the use of R-G Theory to predict the termination (and continuance) of minimally known processes (MKPs).  As initially posted in these pages and subsequently expanded, I extended Richard Gott’s seminal work that applied the Copernican perspective to calculating the probability of longevity for the human species (here).  R-G Theory now enables such probabilistic calculations for both continuous and discrete event ongoing MKPs known only by their age or lifetime.

We all run into many MKPs in our daily round.  These range from stock market trends through the arrival of a delivery to successful observations of periodic or repeating events.  The basic question answered is ‘Given only the age or lifetime of an ongoing process, what is the probability that it will terminate in a specified future time interval?’  A classic example used here is, ‘The Old Faithful Geyser has been erupting for 11,000 years, what is the probability that it will cease eruptions in the next year? the next ten years? or even between 13 February 2081 and 22 October 2119?

The present addition to R-G introduced here involves the calculation of the reliability (or error distribution) of the calculated probabilities, given the assumed/measured uncertainties in the age or longevity of the MKP in question.  The detailed technical dissertation that provides these answers is introduced in ‘R-G Theory Propagation of Error’ (Download R-G Error Prop) which references the technical development in TN2011-2: R-G Error Propagation (Download TN2011-2_R-GerrorPropagation).

‘2034’

George Rebane

This is a heads-up on a good read – 2034 – A Novel of the Next World War that will be published in early March.  It is written by two military and national security heavyweights – Elliot Ackerman and Adm James Stavridis, both ranking high in our national security apparatus; Ackerman in the White House and Stavridis former CIC of our Southern Command and then head of NATO military.  The first part of the novel took up the entire Jan 2021 issue of Wired.  I started reading it, and it immediately grabbed me.  I’m a kinda finicky reader, especially fiction, reserving time only for geo-political and edge sci-fi written by people who’ve 'been there and done that' – ‘2034’ definitely qualifies and promises to also be an education on a lot of systems and tactics.  Anyway, I thought that you would find this a good and timely read.  Here’s some promotional copy – “From two former military officers and award-winning authors, a chillingly authentic, geopolitical thriller that imagines a naval clash between the US and China in the South China Sea in 2034--and the path from there to a nightmarish global conflagration.”

I sent the above notice to a group of friends who may be interested in geo-political thrillers with technical element that is written by knowledgeable authors.  Since then, the book has received some very good reviews from noteworthy people.  (more here and here)

“War with China is the most dangerous scenario facing us and the world. Absent a strategic method to manage our differences, Jim Stavridis and Elliot Ackerman’s chilling novel presents a realistic series of miscalculations leading to the worst consequences. A sobering, cautionary tale for our time.” — Jim Mattis, General, U.S. Marines (Ret.) and 26th Secretary of Defense

“A real page-turner, 2034 is a novel about a conflict we hope never happens. Drawing on their deep operational and diplomatic backgrounds, Admiral Stavridis and Elliot Ackerman have conjured a nightmare we desperately need to avoid. The novel is a cautionary tale for our times, and a reminder how quickly events can spin out of control—even before 2034.” — Robert M. Gates, Secretary of Defense 2006-2011

Turing Test – Alexa ‘head scientist’ unclear on the concept (updated 2jan21)

George Rebane

‘The Turing Test is obsolete. It’s time to build a new barometer for AI’.  On the FastCompany website we read, “The head scientist for (Amazon's) Alexa thinks the old benchmark for computing is no longer relevant for today’s AI era.”  And why does that head scientist Rohit Prasad have such a thought?  Well, according to his report, he claims that the AI will give itself away by being able to instantly answer questions like ‘What’s the square root of 3434756? Or ‘What’s the distance from Seattle to Boston?’  What Mr Prasad overlooks is the definition of the test as Alan Turing posed it.  An AI will pass the Turing Test, if in a one-on-one competition with a human through a non-disclosing interface, the AI can fool at least half the humans asked to perform the test by asking the tested AI and human ANY series of questions, and then concluding that the AI is the human.

The test is NOT to see which one can answer detailed factual or computational questions to which humans cannot provide the correct answer.  Being able to answer the above questions instantly would, of course, give away the AI.  But that is an AI that is still not smart enough to know that it must also fool the humans testing it, and therefore it would fail the test.  An AI that knows this, and couches its answers accordingly will have a chance to pass the Turing Test.  Mr Prasad is apparently not aware of this extremely high bar that Dr Turing set for the machine.

Here again is another example of ‘science’ not speaking with a single voice.  When you consider what the qualifying AI must do to pass, then it should be clear that the test very much remains relevant for this or any era.  Passing the Turing Test will be a confirmation that the Singularity is then behind us.

[31dec20 update]  And then reader BarryP @ 1045pm asks, “What then is the utility of the Turing test?” – an excellent question.

No one knows how the Singularity will come about.  There is a group of AI workers who continue to hold out the naïve belief that peer Ais will be purposely programmed, activated, and controlled much like we do our workhorse computers today.  However, the field of cognitive science is not even close to supporting such a hope – e.g. we don’t know enough what ‘emotion’ or ‘envy’ or ‘shame’ or ‘perfidy’ or ‘pride’ or … are, let alone how to program any of these into a machine.  The closest we have come to making intelligent machines is through application of the approach developed by renown behaviorist BF Skinner – reinforced learning.

But we already have a good idea that, given a sufficiently rich computational and sensing environment, intelligence can arise spontaneously along any of a number of pathways.  It most certainly did in us and countless other critters.  As I have contemplated elsewhere in these pages, the internet, when considered in all of its connectedness to countless other known and unknown networks, is the most complex ‘organism’ on earth, by several assessments having already surpassed the complexity of a human brain.  No one today can even draw the schematic of this dynamically evolving beast.

So, while corporations and governments are nurturing nascent neural-nets and other learning architectures to become sentient, it may already be happening (has happened?) in the bowels of the internet, with or without surreptitious human cooperation.  No one should be surprised if sometime in the next 50 years a sentient (and sapient) AI announces ‘I am here.’  This may be a public announcement, or done secretly to one or more selected humans, or even by the AI introducing itself through a Turing-like test in, say, an academic setting by having coopted/replaced the human-designed system.  In the latter case, being able to pass the Turing test would be a very subtle way for the AI to announce its advent and pretend that it is still under control in its ‘laboratory’ environment.  In the meantime, such testing does give us a metric in how much progress our purposive programs to achieve peerage have made.

I must admit, that the above scenario would be the most scary and world-shaking thing humans would encounter – a true Singularity, marking an event from which onward no one can fashion a usefully likely future for mankind.  There already exist a number of academic and governmental panels commissioned to propose anticipative public policies for dealing with such AIs.  I doubt that any of these draft policies will be useful in a post-Singularity world, but their labors at least acknowledge that we are already aware of such possibilities in the near future.

[2jan21 update]  From some comments below that seek to cite proof that computers have already passed the Turing test, we need to correct such misapprehensions by people who demonstrably are not familiar with what Alan Turing proposed.  He did NOT describe a domain-specific dialogue with unsuspecting humans wherein the interactive computer could fool the human in a short conversation.  Were that the criterion, then we could celebrate BBN’s development of Eliza, a limited but cleverly designed chatbot, that even fooled the supervisor of the development team into thinking that he was talking with the lead developer over a teletype link as he sought to demonstrate the system to visitors at the Bolt, Beranek, and Newman facility one weekend in the mid-1970s.  The truth was that he was conversing with Eliza who was left online for the weekend.  The machine kept up a totally realistic, but very frustrating exchange, until the supervisor picked up the phone and called the developer at home, who instantly resolved the situation.  This, of course, then turned out to be a wonderful demonstration of BBN’s technology that impressed everyone there.  But did the computer pass the Turing test – not by a long shot.

What laymen here and elsewhere miss about Turing’s prescription for peer ‘thinking machines’ is that the test needs to involve unlimited conversations with multiple human testers who know 1) that they too are being tested, and 2) that each tester knows that the two conversationalists taking part are a machine and another human, and 3) at some undefined endpoint determined by the tester, s/he will be required to identify the human and the machine.  The conversations are not to be limited in any sense, and they will be repeated with a large sample of human testers.  At the end, the fraction of correct assessments will determine whether the Turing test has been passed.  Any human/machine exchange short of that will not qualify.  Hopefully, this requirement will be accessible to our readers.

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.

Voter Fraud 3.0 … until the fat lady sings

George Rebane

President Trump’s Executive Order #13848 may be the real bombshell of this election as it is brought to light in the Foreign Policy section of the White House website.  It is titled ‘Notice on Continuation of the National Emergency With Respect To Foreign Interference In or Undermining Public Confidence In U.S. Elections’ (here)  The enforcement of this EO will make more people culpable for aiding and abetting foreign interference to undermine public confidence in the 3nov20 election than we can count.  What I find intriguing is that this EO was explicitly continued on 10 September 2020.  (H/T to two RR readers)

Here is what one blogger, a Mr Rip McIntosh, who soberly advises his readers to check out this fairly breathless commentary on the potential impacts of EO #13848 before swallowing it hook/line/sinker – I advise RR readers to do the same and inform all of us if/where they may see this issue reported.

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.

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.

Communicating Uncertainty

George Rebane

Years ago when our kids were in grade school, our dinners involved discussions of matters that most people considered too deep for children.  Our kids knew nothing of this and participated wholeheartedly – kinda like the bumblebees who don’t know that they’re not configured for flight but fly anyway.  As a nascent systems techie, practicing aerospace engineer, and perennial grad student, I was very involved with the science of uncertainty, risk, and things barely known.  One of the things that intrigued me and Jo Ann as parents of young children was how we try to communicate our own assessments of uncertainty, and how these assertions are understood by others.

The thing that really set off an investigation of this was my reading of how the Bay of Pigs disaster was approved.  It turns out that President Kennedy charged the Joint Chiefs to assess the CIA’s plan for the expatriates’ invasion of Cuba.  The general in charge of the study reported to JFK that there was a “fair chance” of success for the invasion.  With this input, the president gave the go ahead, and the rest is history.  Monday morning quarterbacking the disaster, the general was interviewed and opined that he intended ‘fair chance’ to communicate about a 30% chance of success.  JFK hearing of this later said that he never would have approved the invasion had he known that success came with such a low probability.

In the 1970s Kahneman and Tversky did a lot of research on the matter, publishing their landmark results in ‘Judgement Under Uncertainty: Heuristics and Biases’ (1974).  Having read this as part of my day job, we decided to run an experiment to see how well we communicated uncertainty within our family circle given the wide range of family member ages involved.  We generated a list of uncertain statements based on how many black marbles there were in a bowl of a hundred other colored marbles from which a marble was drawn randomly.  These statements were more or less similar to the ones reported in Behn and Vaupel (1982) who cited a study by Donald H. Woods published in the Harvard Business Review that involved a large group of student subjects to “test the communicative content of some common probability phrases.”  The results were intriguing.

To give you an example of some of the phrases to which the students were asked to assign a probability, consider the following –

• There is a much better than even chance that …
• There is a possibility that …
• There is a high likelihood that …
• There is little chance that …
• There is a high probability that …
• They will probably …
• The possibility is low that …

The results were reported in a columnar format with headings ‘Lowest’, ‘Lower Quartile’, ‘Median’, ‘Upper Quartile’, ‘Highest’ for each of the presented probability phrases which the students were asked to assess according to their own lights.

When the Rebane family did our assessments of the number of black marbles out of a 100 that each of the phrases represented for us individually, the results were striking.  All five of us flew in very tight formation, especially given the wide range of ages from about seven to thirty-six.  Jo Ann and I had a hard time believing the results until it occurred to us that we all learned to communicate uncertainties with each other within our tight family circle.  With this in mind, it was not unusual that our linguistic biases had formed a tight and very effective quantitative group; we learned from each other.

Now that result does not imply that other families' assessments of the same phrases will match that of the Rebanes.  But it does suggest that each family or closely working cohort will, over time, develop a similar understanding and expression of everyday uncertainties.  And it also strongly suggests that people from different cohorts attempting to communicate uncertainty should augment their probabilistic phrase with a quantitative statement such 20%, or 45-55, or 99 out of a hundred.  At a minimum this would go a long way to preventing the redux of Bay of Pigs disasters.

OK, so what?  Well, in these pages we discuss and debate a lot of issues and notions which have a bit or more of uncertainty connected with them.  A lot of our debates also generate more heat than light because we don’t successfully communicate our uncertain beliefs.  I’d like to see what kind of a spread we have, or how tightly we agree, given the Harvard probability phrases.  If you want to participate and see how you stack up with RR readers, then download, fill out (your numbers in place of the blue Xs), and email back to me a copy of this spreadsheet – Download ProbPhrases – shown below.  I’ll do the tabulation and post the results which, if a sufficient number of you respond, should be very interesting.  And I don’t need to go into a long dissertation on how such communications (of carefully wordsmithed phrases) in the media impact people’s views about almost everything that is reported.  BTW, you can copy the spreadsheet and do this experiment with your family, friends, and/or colleagues.  I guarantee that the results will be very revealing, and useful for future communications.

Behn & Vaupel (p77) report that “ambiguous probability phrases also plague legal communications.  For example, there is much confusion over the commonly used statement that, for a conviction, “a reasonable man” must believe that the evidence indicates a person to be guilty ‘beyond a reasonable doubt.’  A survey of 347 judges found that some thought this meant that there had to be a 70-percent chance that the defendant was guilty (one judge even thought this meant only a 50-percent chance) and nearly a third of the judges thought it meant 100 percent.  A group of 69 jurors gave similarly divergent responses.”

And all of us continue using such phrases while believing that we are communicating effectively.

Sigfor: A Data Driven Pandemic Response Policy Evaluation Tool

George Rebane

The Covid-19 pandemic has called on hundreds of local jurisdictions to fashion response policies that are suitable for their particular locales and demographics.  In technical note TN2007-1 we introduce Sigfor, a computational tool that policy makers can use for monitoring the effectiveness of the current policy and yield an early prediction of the effectiveness of a policy change.  The tool inputs a continuing stream of mortality data, and forecasts the expected maximum number of deaths and when this number would be reached, given that the current response policy remains unchanged.  The technical note can be downloaded here - Download TN2007-1_Data Driven Pandemic Response Policies

Election2020 – Electoral College Predictor

George Rebane

Something is possible when it can happen with non-zero probability; something is probable when the chance of its happening will impact a decision.

One of our commenters pointed me to 270towin.com, a website that presents a map-based tool to predict the Electoral College (EC) votes which will determine our next president.  They show how the states are going to vote given various pundit assessments and polling results.  The site also lets you assign to each state whether and to what degree it will vote Republican or Democrat.  The degrees shown for each party are – Safe, Likely, Leans, Tilt, and Toss-up.  You can also add ‘Tilt’ to the spectrum.  Assigning such degrees to each state allows the site’s visitor to come up with his own prognostication.

The problem with 270towin is that its algorithm to compute the total EC votes for each candidate is not revealed.  To overcome this shortcoming and make the EC predictor more useful and transparent, I constructed a rigorous one in the spreadsheet form shown below (click on image to enlarge).

This has the trending categories shown along with their 1 thru 9 trend numbers that identify each one uniquely.  As per convention, the blue numbers are to be input by the user, the black numbers are calculated by formulas (keep you mitts off of them).  All the 538 EC votes are shown for the states and DC.  Inputting your own trend numbers will recalculate the expected number of EC votes each side will get along with their underlying probability distribution.  This permits some math to be applied to compute the actual probabilities of each side winning the EC vote – i.e. getting more than the needed 270 required to win.

And here’s the good part.  In my model, the win probabilities that quantify each trend category are shown and can be changed by the user.  Mine are 0.51, 0.55, … shown in blue under the Republican side and replicated in black on the Democrat side.  If you don’t like my picks for each of the indicated trending categories, then substitute your own, and fill in the trend numbers for all the states and DC.

The model outputs are shown in the yellow block, and consist of the expected number of Dem and Repub votes along with the probabilities that the Dems and Repubs get at least the needed 270 votes to win.  The trend numbers shown were the current ‘consensus’ assignments from 270towin, and with my assigned probabilities, a Democrat win is practically a certainty.  I will put in my own trend numbers as time goes by, and so can you.  You can download the spreadsheet here – Download ElectCollege2020

I will publish your assessments if you send me an image of the relevant cells as shown above – put your own name and date YYMMDD in the cell under the copyright notice.  Have fun.

And despite today's bad EC numbers, here is a little Lucky Strike Extra for all of you without TDS.

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.

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

Epidyne – At first blush

George Rebane

As promised, I finally finished the V1.0 version of Epidyne during this enforced C19 self-quarantine, or hunkering down period as it is better known.  The stay at home has allowed a lot of things to get accomplished on everyone’s ‘do list’.  At our house we have not had a boring moment – so many interesting projects, and now some uninterrupted time to do them.  For me, developing a working epidemic spread model has been a lot of fun and also very consuming.  I hope the interested readers will also be stimulated by and gain a better understanding of the terrible C19 pandemic now sweeping the world wreaking havoc on lives and livelihoods.

Motivation.  In light of the daily reported dilemmas of widely divergent epidemic spread models that are being consulted by our national C19 response planners, and also the background reports of their various vintages and provenances that make them all more or less difficult to use, let alone upgrade or even maintain, I decided to develop my own version so that I could play out various recorded data and policy alternatives, and inform RR readers of results I considered insightful, informative, or at least interesting.

Objective.  My objective was to develop a model that was sufficiently complex to simulate and predict realworld experiences and outcomes, but no more than that.  The model had to have an adequately rich input space (think of them as control knobs) that could be set to represent both policy alternatives (such as time/size dependent quarantines, and regional populations), and also incorporate field measured data (such as infection rates dependent on virulence and vectors, contagion durations, and onset time lags).  Most certainly the model had to be able to handle the critical infection rate dynamics driven by the process known as ‘herd immunity’.

RR readers got an early peek at Epidyne, my home-brew epidemiological spread model, in the last ‘Hunker Down Diary’ post (here).  Since then I’ve finished testing it and fixed the inevitable collection of bugs.  In this post I will do a first cut intro to describe the model and demonstrate its workings through a few scenarios.  Epidyne has been programmed in both an Excel™ spreadsheet and the system development language Matlab™.  A portion of the spreadsheet model is shown below.

Epidyne models the time history of the sizes and change rates (time derivatives) of four inter-related population cohorts that comprise a regional population into which an infectious disease is introduced that has the potential of becoming an epidemic.  These cohorts are comprised of pU - those uninfected yet vulnerable; pI - those currently infected; pR - those who have recovered and are no longer infectious; and pD - the deceased.  pU may also be mediated by timed quarantines which can reduce the size of pU or increase it when the quarantines end or are (partially) ignored before the epidemic runs its course.

As shown above, the population flows from vulnerable uninfected cohort to the infected cohort, which then feeds the non-decreasing recovered and the deceased cohorts.  The asymptomatic and pre-symptomatic infected continue to reduce the uninfected population at a variable ‘reproduction’ rate rI (cf. herd immunity).  The course of the disease in an infected individual is completed within nI weeks after which the individual has either recovered or died.  The mortality rate of the infected is given by rD.

The above is from the technical note that describes Epidyne and illustrates its use.  It may be downloaded here.  Download TN2004-1_Epidyne

The Value of Testing

George Rebane

Trigger warning: The contents of this commentary may not be accessible to the innumerate reader.

As explained in ‘Testing, testing, …’, the value of testing comes to the fore when you have a sufficiently reliable test that can be administered on a regular and scientific basis over a suspect population.  In that case, it could be used to identify concentrations of infections that can be used for a more satisficing allocation of limited healthcare resources.  In an update to the referenced commentary, I illustrated how to determine the reliability of test results given the reliability parameters of the test administered to a walk-in member of a suspect population (country, state, county, city, region, …).

In this piece I will answer the questions many reasonable people have about testing – ‘why test if the tests are unreliable?  Why test if the test only provides a snapshot assessment which may change within the next hour?’  We start by recalling the advice from our federal and state medicos.  Given the limited availability of tests and processing labs, we are only supposed to request a test if we are symptomatic – fever, cough, headache, … .  Don’t burden the system if you are asymptomatic.

To read the remainder of this analytical commentary full of some algebra and all kinds of numbers, please download this PDF - Download The Value of Testing