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cite="mid:7F9C2C64-3CDA-4D19-A8C8-AAA0C35CA3B7@snoutfarm.com">
<pre class="moz-quote-pre" wrap="">One way to address the N/A issue is to repeatedly perturb the real-world system so as to elicit those correlations. When that is practical.. </pre>
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<p>We are, in a time of real-world system perturbation, right now.
The whole world is responding to what is *roughly* the same virus
with *roughly* what is the same human phenotype/metabolism in a
myriad of *roughly* the same modes of human organization. This
IS a testbed of human (-system?) response to a widespread,
somewhat invisible threat. From Wuhan to Singapore to Italy to
Iran to Sweden to Germany to NYC to WA State to the Navajo Nation
to Florida's beaches, this IS a huge coupled systems
dynamics/agent-model executed in real-time by real-people with
real casualties and real consequences. <br>
</p>
<p>We are, to varying degrees (collectively) recording the results
of these "experiments" and if we are lucky (or smart, or both) we
will do some post-game analysis intended to understand more-better
how best to (self-)organize around a (nearly) existential
world-scale threat. And to the extent this is a game that will
never end, we have to begin the analysis while we cope with it's
consequences. Feels a bit like the models pof Physics
Interreality.</p>
<p><a
href="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.75.057201">
https://journals.aps.org/pre/abstract/10.1103/PhysRevE.75.057201</a>
</p>
<p>Hanging too aggressive of a model on this (or collecting the
data against too premature of a model) will reduce the utility of
such data gathering and analysis. Whatever the dual of
overfitting a model is? Overmodeling? Premature Modeling?<br>
</p>
<p>What I'm looking (askance) to(ward) Pearl for is a better way of
rapidly constructing, maintaining, revising as generic of a model
as possible in response to "this moment". Four months ago we
should have been interested in models of how one limits a virus
such as COVID19 getting a foothold in this country. One month
ago we should have been interested in how one limits COVID19 (with
new understanding of it's virility, it's fatality, it's symptoms,
it's mode of spread) once it HAS a foothold, now we are faced
with trying to understand how to cope with it once it is pervasive
in our population whilst continuing/returning to "business as
usual" and in another thread, I'm encouraging that we "try to
plan/consider/think-about" what we want to do with this somewhat
"blank slate" (our ass?) we are having handed to us. <br>
</p>
<p>And how to think about this without premature modeling... what I
think I was railing (whining/pushing-back) about with Dave on the
Bellamyist thread earlier this morning.</p>
<p>- Steve<br>
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cite="mid:7F9C2C64-3CDA-4D19-A8C8-AAA0C35CA3B7@snoutfarm.com">
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<pre class="moz-quote-pre" wrap="">On Apr 19, 2020, at 8:33 AM, uǝlƃ ☣ <a class="moz-txt-link-rfc2396E" href="mailto:gepropella@gmail.com"><gepropella@gmail.com></a> wrote:
Well, the argument I often end up making is that you can do a kind of face validation with the fake data. Show it to someone who's used to dealing with that sort of data and if the fake data looks a lot like the data they normally deal with, then maybe more data-taking isn't necessary. If it looks fake to the "expert", then more data-taking is definitely needed.
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<pre class="moz-quote-pre" wrap="">On 4/19/20 8:29 AM, Marcus Daniels wrote:
I have a hard time with this as a way to extend data. If it is high-dimensional it will be under-sampled. Seems better to me to measure or simulate more so that the joint distribution can be realistic. And if you can do that there is no reason to infer the joint distribution because you *have* it.
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<pre class="moz-quote-pre" wrap="">On Apr 19, 2020, at 8:18 AM, Frank Wimberly <a class="moz-txt-link-rfc2396E" href="mailto:wimberly3@gmail.com"><wimberly3@gmail.com></a> wrote:
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<pre class="moz-quote-pre" wrap="">
Going back and forth: If you infer the causal graph from observational data you can use that graph to simulate data with the same joint distribution as the original data.
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