The book by Thompson of the constructs of models is an
interesting read. It is clearly targeted for the general audience but does
provide some enlightenment to the world of models, especially ones that have
played a part in our day to day living. Two of the most recent are those
related to COVID and those related to climate change.
Overall the books is well written and the author gets their
point across. However, there are many points of views on models. I have worked with
models for well over sixty years. Thus I have the distinct disadvantage of
experience. Early on I had to model the dynamics and control of spacecraft,
some of which is controlled by the laws of physics and some by random or
stochastic processes. Namely there are event we model to account for things
that happen beyond our control or that we have no idea about other than trying
to anticipate them. These models got people to the moon and back.
The next level of
models are those in business plans for startups. I spent several decades in
that space both as an entrepreneur and as an investor. In over hundreds of
business plan models I do not think a one ever was even close to what happened.
Success came from the entrepreneur being able to recognize issue and adapt
accordingly.
The author interjects models of COVID, especially those from
Ferguson and the London cabal. In later March of 2020 they predicted in their
model deaths in excess of 2.5 million in the US in less than a year. So far three
years out we have less than 1.1 million. That is three years and more than two
dozen variants. The flaws in their models are extensive. They did not
anticipate the mutation rates we see but anyone with minimal skill in virology
would have done so since it was a single stranded RNA virus and was easily
mutated. Secondly, even today, we really do not know the details of
transmission. Large crowds yes but aerosols and virions and then what? Third
they made recommendations from those models which resulted in enormous societal
impacts and economic disasters. Their models were specious at best and lacking
in any details to have reached such conclusions. They did get a lot of Press however.
The author discusses LTCM, and the Black Scholes disaster in
1998. I recall that quite well, I was in Moscow watching banks collapse as
well. In 1972 I was at a conference at the University of Chicago with a team
from MIT. We were to work with economists on systems and models, especially in
a stochastic domain. Some of the folks later at LTCM were there as we had a
discussion of an Ito process, and the formula. My parting remark having had the
experience of spacecraft and especially Apollo XIII was that they should beware
of the long tails on the probability distribution, that are never Gaussian.
Thus LTCM was not a surprise. The model was flawed and they knew it.
The author then moves to climate and the related models. Of
course one must be careful here since there are many committed souls who have
faith in these models. Yet one thing we know about models is that are
reflections of their makers not necessarily of the reality they are attempting
to describe. In the lates 60s I had the experience in some of the early models,
and worked on getting data on aerosols and heating. I learned that each time I
gathered mor data I had more questions. I suspect global climate models evoke
similar responses.
Let me now make some specific comments.
p.15 Ockham dies in 1348 thus he lived and was active in the
14th century NOT the 13th. In fact his Nominalism was a
major factor in examining each entity as unique and the existence of the
abstract was denied.
p. 19 Regarding mouse models we have the issue that they are
used to examine, for example, immune system responses on specially genetically
engineered mice. That in itself is an interesting tale of models. Mice ae mice,
and genetically engineered mice are just that. Humans have unique characteristics
and each is unique unto themselves. Thus mouse models explore just what the
model is capable of stating.
P 36 The single story statement is interesting. Perhaps that
is why we have 4 Gospels?
Pp 50-51 Are models sufficient to tell us something? Yes,
like an exercise. One must recall that the Navy had a model for war with Japan,
War Plan Orange. This was a model of how to fight Japan. So how well did that
work out? Think Pearl Harbor. Likewise the Big Data and AI issues are
themselves models, but models devoid of any paradigms. Namely AI is nothing
more than lots of input with little output. For example thousands of mammograms
and ultrasounds with the training for breast cancer. Place a new set in and it
will tell you if it cancer or not. Hopefully.
Pp 52-53. Feedback in reality is a key factor found in
nature. The Cybernetics of Wiener is a critical element lacking in many models
of physical systems. However it is a critical reality in almost all if not
every organic system.
P 83 is part of the Black Scholes model I discussed above.
The author finds that a model wanting but perhaps for just some of the right
reasons. Nature and the markets have that feedback capacity that often changes
the underlying model elements one starts out with. Then again there are those
nasty Black Swans.
P 108 The author calls AI and autonomous system. I would say
it goes well beyond that. AI is not based on underlying first principles proven
buy clear and repeatable physical experiments. In contrast it is oftentimes just
an amalgam of data weighted in a manner suitable to the designer and “trained”
to produce a “proper” answer.
p 114 et seq Economic models are complex abstractions of
reality. Demand, supply, etc are abstractions and measured at some gross level.
Each economic crisis we go through evokes some alternative economic reality
that is an attempt to justify actions. But alas one wonders where this specific
economic paradigm was before the crisis and what should have been done to
prevent it.
The latter part of the book is an attempt to justify climate
models and discusses what governments should be doing. The reality
unfortunately is that climate models are just that, models limited by data and
the stochastic dynamics of reality.
Overall the book is a good take on models in general. Some
models are good, some necessary, some useless, and some cause undue harm. COVID
models were example where anyone could be their own epidemiologist, albeit
lacking any knowledge of the virus itself. The virus has taken on a life of its
own and may very well outlast all it models.
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