Posted on 04/02/2020 9:05:29 AM PDT by Willgamer
(my first vanity, retired profession data modeler)
The use of data modeling as the gold standard of informing our response to pandemics is literally killing us.
While modeling is used with helpful results that inform planning, projections, and computer applications in business, science and elsewhere, they are nearly useless for guidance about real world, biological phenomena.
So called global climate change is a prime example. The track record for the thousands of computer simulations, based on models, that have been created over the last 4 decades is 100% failure.
Models are useful when the inputs, variables, and math formulas are well known. So for business, physics, engineering, other hard science, et al. the model can be accurate and helpful.
However, in the natural world of living things, NONE of these thing are sufficiently understood to give reliable results. Not just "garbage in, garbage out", but garbage application of modeling; hence the modeling becomes pseudoscience.
The president would be well served by removing the academics seduced by psuedoscience modeling and replacing them with practicing army, family, and ER doctors.
Our best understanding of pandemic response was probably around previous decade with advanced medical protocols, but before the uncritical adoption of modeling.
Models are like polls, they serve as political tools.
I don’t think COVID-19 will kill 100,000 people in the US, but so far the response to it has killed over ten million jobs...
You have expressed what I have been saying here for weeks, but more eloquently. However, many here will discount your expertise because you question our medical deities (Imperial University which has been revealed as very inaccurate) and President for Life Fauci (I did not vote for him, did you?).
The model has no intelligence. It is as good as the algorithm devised by the designers. It’s not even a “Model” of the real world. It considers a limited number of factors, while the real world has a huge number of them. It divides reality into convenient segments, while reality does not come in convenient segments.
So a model is inherently incapable of predicting the future. Sometimes the output coincides with reality, and sometimes it doesn’t.
Lorentz: Chaos: When the present determines the future, but the approximate present does not approximately determine the future.
All models are wrong. Some are useful. George Box
These CV TV models are being treated as Gospel. Because math . . . is difficult.
Sacrificing our economy to blindly trust a model that has ZERO basis in reality was an extremely reckless decision.
Government/Medical tyranny coupled with Propagandists Extraordinaire can be very deadly.
Fat man, little boy, tiny germ ALERT.
They are not models. They are simply basic math multiplying numbers they don’t understand.
Models use multiple parameters, curve fitting (requires Calculus), linear equations, population models, statistical mathematics, data sources, etc. Modeling is much more complex than simply taking a value today and multiplying by 365 to come to an annual number. That’s not modeling.
I'm not a modeler but I work in an industry where models are used extensively for forecasts of future demand on public infrastructure. I have never once seen a model output that was 100% accurate, but in most cases they provide reasonable estimates based on prior trends and current known conditions.
Models for biological phenomena are probably very tricky simply because the "current known conditions" are often a large black hole, but "prior trends" in other comparable situations can at least provide some reasonable guidance on future conditions. Maybe the range of potential outcomes is wide, but in a rapidly-changing situation at least the model can be re-calibrated and updated constantly.
I see a COVID-19 model as not unlike a hurricane tracking model. It will never be totally accurate, but the forecasts are reasonably accurate for emergency preparedness purposes.
21st century Witch Doctors.
Good quote from Korzybski (1879-1950): “The map is not the territory.”
Great post.
Every time Dr. Birx mouths the word “models,” she appears to experience a subjective sense of exaltation.
The same thing happens whenever academics use the word “studies.”
It’s the religion of intellectualism.
What data are you going to “fit a curve to”? I don’t think you want to “fit a curve” to # new deaths so far?
“What data are you going to “fit a curve to”? I don’t think you want to “fit a curve” to # new deaths so far?”
Obviously, you don’t know analytics. As I said before, it ain’t about simple numbers.
Per your point, it sounds like the field you work in involves some kind of stochastic modeling, thus generating imperfect but nevertheless helpful outputs.
Biological models are far more problematic beyond the reasons you cited. Simply put- nature is complex beyond our wildest imaginations.
The reason global warming models will always fail is that it’s not just the physics of weather, but the biological effects of countless living plants and animals, the holistic effect of all nature, as it were, on the climate. The sheer number of variables, much less how to correctly weight them, is beyond our present scientific understanding.
So to me, the Wuhan models have far more dissimilarities than similarities to hurricane tracking models where biological agents are not significant.
“It’s the religion of intellectualism.”
Only to those that don’t know models, which is usually someone who says ‘models’.
Yezbut....models are inherently incoorect...limited true data...skewed collection of data...etc...big prob is that the talking “journalist” heads dont know enough to know which models are poor...the model info is repeated by the journos based on scientists opinion (widely incorrectly assumed to be true)...and you get the “blind leading the blind” outcome
modeling is a form of “imitation,” i.e., by definition not real.
“The result of this pseudoscientific imitation is to produce experts, which many of you are. [But] you teachers, who are really teaching children at the bottom of the heap, can maybe doubt the experts. As a matter of fact, I can also define science another way: Science is the belief in the ignorance of experts.”
‘When someone says, “Science teaches such and such,” he is using the word incorrectly. Science doesn’t teach anything; experience teaches it. If they say to you, “Science has shown such and such,” you might ask, “How does science show it? How did the scientists find out? How? What? Where?”’
—Dick Feynman speaking to a group of science teachers a long time ago
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