anthropological observations

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Re: anthropological observations

Steve Smith

> Unfortunately, after a couple of attempts to read it, I couldn't understand anything in your post except this part. My previous post was just under 300 words. So, I decided to try to make the next one under that mark as well.
>
> On 4/18/20 1:22 PM, Steven A Smith wrote:
>> From whence (or wherest?) did you get your 300 word target?

you might not be alone in that...  perhaps it was just gibberish.  And likely more than three hundred words of it.


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Re: anthropological observations

Marcus G. Daniels
The opposite of TLDR is the technique described by Scott Adams.   This leads me to posit that those that complain things are TLDR are likely just the incurious, the impatient, and the entitled, and likely to be part of the problem.  Is  there some particular crisis of their Valuable Attention that must be conserved at all cost?  Are we running out of disk space?   Are we running out of network bandwidth?   No.   Netflix is blazing gigabytes of nothing 24 hours a day to the drooling masses.   Enough.

From: Friam <[hidden email]> on behalf of Steven A Smith <[hidden email]>
Sent: Saturday, April 18, 2020 7:26 PM
To: [hidden email] <[hidden email]>
Subject: Re: [FRIAM] anthropological observations
 

> Unfortunately, after a couple of attempts to read it, I couldn't understand anything in your post except this part. My previous post was just under 300 words. So, I decided to try to make the next one under that mark as well.
>
> On 4/18/20 1:22 PM, Steven A Smith wrote:
>> From whence (or wherest?) did you get your 300 word target?

you might not be alone in that...  perhaps it was just gibberish.  And likely more than three hundred words of it.


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Judea Pearl: Book of Why

Steve Smith
In reply to this post by Steve Smith

Glen -

I'm in the midst (early part) of Judea Pearl's "Book of Why".  

I had a vague memory of his earlier book: "Causality" having been referenced if not discussed on this list.   Searching the archives, I discovered what I considered to be quite a Pearl (NPI) circa 2013.  In this long chain, you recommended Pearl's "Causality" which I now wish I had followed up on then.  

http://friam.471366.n2.nabble.com/beyond-reductionism-twice-td7582273i20.html#a7582308

Among the many gems in the thread were the voices of two of our deceased members, Doug Roberts and Tory Hughes.   Doug coined one of his classic lines about (paraphrase) "being violently disinterested in the philosophy of causation" (or complexity or agent-based-model-design).

After Nick's recent "violent disinterest in the Cult of Feynman" and in particular to any quote that might imply that birds are (paraphrase) "not first-class-citizens who would in fact be interested in ornithology, if they were given access to it", my eyes caught on your own quote (in 2013) of S. Ulam:

    "Talking about non-linear mathematics is like talking about non-elephant
    zoology." -- Stanislaw Ulam

- Steve (176)


      
Unfortunately, after a couple of attempts to read it, I couldn't understand anything in your post except this part. My previous post was just under 300 words. So, I decided to try to make the next one under that mark as well.

On 4/18/20 1:22 PM, Steven A Smith wrote:
From whence (or wherest?) did you get your 300 word target? 
you might not be alone in that...  perhaps it was just gibberish.  And likely more than three hundred words of it.


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Re: Judea Pearl: Book of Why

Frank Wimberly-2
Steve,

I'm surprised you didn't find any posts by me in your search for "causality" .  Usually, when someone says "correlation is not causation" it triggers me.  In the early 90s/late 80s there were two teams working on inference of causal graphs from observational data:  Pearl et al at UCLA and Glymour et al at Carnegie Mellon.  They cooperated and developed algorithms based on d-separation which was based on conditional independence relations (correlation).  Glymour et al's book is "Causation, Prediction and Search".  I implemented many algorithms in Java for that group over a period of about 10 years and I was co-author of several papers.

Sorry for the narcissistic reaction.

Frank

---
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On Sun, Apr 19, 2020, 6:26 AM Steven A Smith <[hidden email]> wrote:

Glen -

I'm in the midst (early part) of Judea Pearl's "Book of Why".  

I had a vague memory of his earlier book: "Causality" having been referenced if not discussed on this list.   Searching the archives, I discovered what I considered to be quite a Pearl (NPI) circa 2013.  In this long chain, you recommended Pearl's "Causality" which I now wish I had followed up on then.  

http://friam.471366.n2.nabble.com/beyond-reductionism-twice-td7582273i20.html#a7582308

Among the many gems in the thread were the voices of two of our deceased members, Doug Roberts and Tory Hughes.   Doug coined one of his classic lines about (paraphrase) "being violently disinterested in the philosophy of causation" (or complexity or agent-based-model-design).

After Nick's recent "violent disinterest in the Cult of Feynman" and in particular to any quote that might imply that birds are (paraphrase) "not first-class-citizens who would in fact be interested in ornithology, if they were given access to it", my eyes caught on your own quote (in 2013) of S. Ulam:

    "Talking about non-linear mathematics is like talking about non-elephant
    zoology." -- Stanislaw Ulam

- Steve (176)


      
Unfortunately, after a couple of attempts to read it, I couldn't understand anything in your post except this part. My previous post was just under 300 words. So, I decided to try to make the next one under that mark as well.

On 4/18/20 1:22 PM, Steven A Smith wrote:
From whence (or wherest?) did you get your 300 word target? 
you might not be alone in that...  perhaps it was just gibberish.  And likely more than three hundred words of it.


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Re: Judea Pearl: Book of Why

Steve Smith


Frank -

Steve,

I'm surprised you didn't find any posts by me in your search for "causality" .
Actually I did find you, your voice was in that thread as well, and in others. 
  Usually, when someone says "correlation is not causation" it triggers me.  In the early 90s/late 80s there were two teams working on inference of causal graphs from observational data:  Pearl et al at UCLA and Glymour et al at Carnegie Mellon.  They cooperated and developed algorithms based on d-separation which was based on conditional independence relations (correlation).  Glymour et al's book is "Causation, Prediction and Search".  I implemented many algorithms in Java for that group over a period of about 10 years and I was co-author of several papers.

Sorry for the narcissistic reaction.

I am glad you spoke up immediately.  If I had something actionable to do with this right now, I would be consulting your grounded experience, but also welcome your broader perspective.  I saw your references to Glymour and wish (also) that I had followed that "back then".   I think the first discussions I saw here were as early as 2003?  I snagged on the 2013 one because of Doug and Tory's "voices" from the other side of the veil.

I was lead to Pearl's latest by a colleague who has been encouraging me to look at the Pandemic Modeling challenge in a somewhat different way than has been traditional.   Essentially a space-time network of causal implications ( I think) which would be rich in post-hoc elaboration.   The simplest SIR models are challenged with not knowing nearly enough about the Infected in particular, and even then after they have been Infectous for days or even weeks.  The recent hints (Santa Clara county?) that infection rates may be 10x or higher than believed (a or sub or crypto -symptomatic)  demonstrate that acutely.   My work of late (other than SimTable) has been in the realm of trying to analyze ensembles of predictive simulations.   This is a logical next step (forward and backward propogating data and constraints as they are recorded/discovered/postulated) across space (populations) and time.

More offline maybe?

- Steve


Frank

---
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505 670-9918
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On Sun, Apr 19, 2020, 6:26 AM Steven A Smith <[hidden email]> wrote:

Glen -

I'm in the midst (early part) of Judea Pearl's "Book of Why".  

I had a vague memory of his earlier book: "Causality" having been referenced if not discussed on this list.   Searching the archives, I discovered what I considered to be quite a Pearl (NPI) circa 2013.  In this long chain, you recommended Pearl's "Causality" which I now wish I had followed up on then.  

http://friam.471366.n2.nabble.com/beyond-reductionism-twice-td7582273i20.html#a7582308

Among the many gems in the thread were the voices of two of our deceased members, Doug Roberts and Tory Hughes.   Doug coined one of his classic lines about (paraphrase) "being violently disinterested in the philosophy of causation" (or complexity or agent-based-model-design).

After Nick's recent "violent disinterest in the Cult of Feynman" and in particular to any quote that might imply that birds are (paraphrase) "not first-class-citizens who would in fact be interested in ornithology, if they were given access to it", my eyes caught on your own quote (in 2013) of S. Ulam:

    "Talking about non-linear mathematics is like talking about non-elephant
    zoology." -- Stanislaw Ulam

- Steve (176)

Unfortunately, after a couple of attempts to read it, I couldn't understand anything in your post except this part. My previous post was just under 300 words. So, I decided to try to make the next one under that mark as well.

On 4/18/20 1:22 PM, Steven A Smith wrote:
From whence (or wherest?) did you get your 300 word target? 
you might not be alone in that...  perhaps it was just gibberish.  And likely more than three hundred words of it.


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Re: Judea Pearl: Book of Why

gepr
The *ensemble* point is the primary reason I regret not being able to parse your response to my Necker cube summarization of EricS' TLDR. It goes back to the original question of how/whether distributional conceptions better catch the unknown unknowns left dangling in the ambience. Pearl's attempts to burst "causality" into graphs, away from chains (though helping to identify chains when they do exist) is along the same line.

To boot, it evokes both Gödel's interpretation of von Neumann's interpretation of Gödel's work (that it takes an infinite expression to describe a thing) and Rosen's definition of complexity (basically anything that requires an infinite number of models to describe).

And, although I can't get my hands on the Rota paper EricS posted, I'm leery of relying on any phenomenology. Heidegger I trust a bit. Husserl not so much. Regardless, I don't think it's *necessary* to go that deep to grok the main point, which is that the transformation should be invertible. We should be able to flip back and forth from goo to thing such that the flipping doesn't change it. The goo we get after flipping from the things should be the same goo we had to start with.

On 4/19/20 6:25 AM, Steven A Smith wrote:
> My work of late (other than SimTable) has been in the realm of trying to analyze ensembles of predictive simulations.   This is a logical next step (forward and backward propogating data and constraints as they are recorded/discovered/postulated) across space (populations) and time.


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Re: Judea Pearl: Book of Why

Frank Wimberly-2
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.


Frank

On Sun, Apr 19, 2020 at 9:11 AM uǝlƃ ☣ <[hidden email]> wrote:
The *ensemble* point is the primary reason I regret not being able to parse your response to my Necker cube summarization of EricS' TLDR. It goes back to the original question of how/whether distributional conceptions better catch the unknown unknowns left dangling in the ambience. Pearl's attempts to burst "causality" into graphs, away from chains (though helping to identify chains when they do exist) is along the same line.

To boot, it evokes both Gödel's interpretation of von Neumann's interpretation of Gödel's work (that it takes an infinite expression to describe a thing) and Rosen's definition of complexity (basically anything that requires an infinite number of models to describe).

And, although I can't get my hands on the Rota paper EricS posted, I'm leery of relying on any phenomenology. Heidegger I trust a bit. Husserl not so much. Regardless, I don't think it's *necessary* to go that deep to grok the main point, which is that the transformation should be invertible. We should be able to flip back and forth from goo to thing such that the flipping doesn't change it. The goo we get after flipping from the things should be the same goo we had to start with.

On 4/19/20 6:25 AM, Steven A Smith wrote:
> My work of late (other than SimTable) has been in the realm of trying to analyze ensembles of predictive simulations.   This is a logical next step (forward and backward propogating data and constraints as they are recorded/discovered/postulated) across space (populations) and time.


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Re: anthropological observations

gepr
In reply to this post by Marcus G. Daniels
I won't read whatever argument Scott Adams might have made about long narratives, mostly because I doubt he has anything useful to say. But also because I *do* prioritize my time. It's not that my time is valuable. It's that if I didn't prioritize (and triage against people like Adams), I'd never be able to get any work done.

Those that *complain* about TL;DR probably comprise a complicated set of multi-faceted people, that won't be well-categorized by the "incurious, impatient, entitled, part of the problem" predicate. That's OK. I'm willing to allow the over-generalization. But it's those that do NOT complain, but simply ignore TL;DR's are more interesting, I think.

One interesting tactic for avoiding constructing TL;DR's is familiar, here in this forum, and consists of *citation*. E.g. one need not post a long explanation of negative probability when there's already an excellent TL;DR exposition out there. All one need do is post a pithy preamble and link to the extant exposition. But the interesting people are not those that complain the TL;DR exposition is difficult to slog through. The interesting people are those who never say a word about it. Did they click the link at all? Did they read it after seeing Feynman's name atop it? Did they get past the 1st couple of pages? Etc.

It reminds me of this bit of hilarity: https://youtu.be/X-ZFoco_1gQ Where Klepper goes round and round some of them "Read the transcript!" "Did you read the transcript?" "No."

On 4/18/20 6:36 PM, Marcus Daniels wrote:
> The opposite of TLDR is the technique described by Scott Adams.   This leads me to posit that those that complain things are TLDR are likely just the incurious, the impatient, and the entitled, and likely to be part of the problem.  Is  there some particular crisis of their Valuable Attention that must be conserved at all cost?  Are we running out of disk space?   Are we running out of network bandwidth?   No.   Netflix is blazing gigabytes of nothing 24 hours a day to the drooling masses.   Enough.

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Re: Judea Pearl: Book of Why

Marcus G. Daniels
In reply to this post by Frank Wimberly-2
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. 

On Apr 19, 2020, at 8:18 AM, Frank Wimberly <[hidden email]> wrote:


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.


Frank

On Sun, Apr 19, 2020 at 9:11 AM uǝlƃ ☣ <[hidden email]> wrote:
The *ensemble* point is the primary reason I regret not being able to parse your response to my Necker cube summarization of EricS' TLDR. It goes back to the original question of how/whether distributional conceptions better catch the unknown unknowns left dangling in the ambience. Pearl's attempts to burst "causality" into graphs, away from chains (though helping to identify chains when they do exist) is along the same line.

To boot, it evokes both Gödel's interpretation of von Neumann's interpretation of Gödel's work (that it takes an infinite expression to describe a thing) and Rosen's definition of complexity (basically anything that requires an infinite number of models to describe).

And, although I can't get my hands on the Rota paper EricS posted, I'm leery of relying on any phenomenology. Heidegger I trust a bit. Husserl not so much. Regardless, I don't think it's *necessary* to go that deep to grok the main point, which is that the transformation should be invertible. We should be able to flip back and forth from goo to thing such that the flipping doesn't change it. The goo we get after flipping from the things should be the same goo we had to start with.

On 4/19/20 6:25 AM, Steven A Smith wrote:
> My work of late (other than SimTable) has been in the realm of trying to analyze ensembles of predictive simulations.   This is a logical next step (forward and backward propogating data and constraints as they are recorded/discovered/postulated) across space (populations) and time.


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Re: Judea Pearl: Book of Why

gepr
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.

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. 
>
>> On Apr 19, 2020, at 8:18 AM, Frank Wimberly <[hidden email]> wrote:
>>
>> 
>> 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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Re: Judea Pearl: Book of Why

Marcus G. Daniels
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..

> On Apr 19, 2020, at 8:33 AM, uǝlƃ ☣ <[hidden email]> 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.
>
>> 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.
>>
>>>> On Apr 19, 2020, at 8:18 AM, Frank Wimberly <[hidden email]> wrote:
>>>
>>> 
>>> 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.
>
> --
> ☣ uǝlƃ
>
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TL;DR

Steve Smith
In reply to this post by gepr
Glen -

I very much appreciate your balance in this regard.  I did not (and
likely Marcus did not either) interpret your frustration with parsing my
(overly) layered response to your Necker-Cube post as judgement of TL;DR
unless the "L" was for "Layered" not "Long".  

I also appreciate your *prescription* for citation with pithy preamble,
and your *example* of it which I think has evolved over the years we
have been doing this continuous "salsa rueda".

(too)Much of my rambling here, unfortunately, is little more than
"thinking out loud" and I DO trust that most (though not all) will
delete or skip or skim most of my maunderings.  

I have enough evidence that others find bits and bobs, gems and pearls,
in the swill that is my monolog (channeling Stephen Colbert?), that I
continue.   Yet I am inclined to try to *learn* from my betters (many
here are MUCH better at being concise and precise) by example to
hopefully increase the apparent signal/noise ratio.  I need not demand
of everyone that they have my  public-key  memorized to decrypt my often
crytpographic gibberings.  

Waxing Gibbous,

 - Steve

On 4/19/20 9:29 AM, uǝlƃ ☣ wrote:

> I won't read whatever argument Scott Adams might have made about long narratives, mostly because I doubt he has anything useful to say. But also because I *do* prioritize my time. It's not that my time is valuable. It's that if I didn't prioritize (and triage against people like Adams), I'd never be able to get any work done.
>
> Those that *complain* about TL;DR probably comprise a complicated set of multi-faceted people, that won't be well-categorized by the "incurious, impatient, entitled, part of the problem" predicate. That's OK. I'm willing to allow the over-generalization. But it's those that do NOT complain, but simply ignore TL;DR's are more interesting, I think.
>
> One interesting tactic for avoiding constructing TL;DR's is familiar, here in this forum, and consists of *citation*. E.g. one need not post a long explanation of negative probability when there's already an excellent TL;DR exposition out there. All one need do is post a pithy preamble and link to the extant exposition. But the interesting people are not those that complain the TL;DR exposition is difficult to slog through. The interesting people are those who never say a word about it. Did they click the link at all? Did they read it after seeing Feynman's name atop it? Did they get past the 1st couple of pages? Etc.
>
> It reminds me of this bit of hilarity: https://youtu.be/X-ZFoco_1gQ Where Klepper goes round and round some of them "Read the transcript!" "Did you read the transcript?" "No."
>
> On 4/18/20 6:36 PM, Marcus Daniels wrote:
>> The opposite of TLDR is the technique described by Scott Adams.   This leads me to posit that those that complain things are TLDR are likely just the incurious, the impatient, and the entitled, and likely to be part of the problem.  Is  there some particular crisis of their Valuable Attention that must be conserved at all cost?  Are we running out of disk space?   Are we running out of network bandwidth?   No.   Netflix is blazing gigabytes of nothing 24 hours a day to the drooling masses.   Enough.


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Re: Judea Pearl: Book of Why

Steve Smith
In reply to this post by Marcus G. Daniels

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.. 

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.  

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.

    https://journals.aps.org/pre/abstract/10.1103/PhysRevE.75.057201

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?

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.  

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.

- Steve


On Apr 19, 2020, at 8:33 AM, uǝlƃ ☣ [hidden email] 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.

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. 

On Apr 19, 2020, at 8:18 AM, Frank Wimberly [hidden email] wrote:

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.
-- 
☣ uǝlƃ

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Re: Judea Pearl: Book of Why

Marcus G. Daniels
Steve writes:

< 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. >

There are hundreds of common HLA alleles across humans.   In a diverse country like the US, with hundreds of thousands of positive cases and tens of thousands of deaths the hundreds of alleles would be well sampled.   Too bad our medical surveillance is so bad, and made worse by the moron.  Imagine if everyone had full genome sequencing and every viral sample was deep sequenced. 

Marcus

From: Friam <[hidden email]> on behalf of Steven A Smith <[hidden email]>
Sent: Sunday, April 19, 2020 10:11 AM
To: [hidden email] <[hidden email]>
Subject: Re: [FRIAM] Judea Pearl: Book of Why
 

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.. 

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.  

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.

    https://journals.aps.org/pre/abstract/10.1103/PhysRevE.75.057201

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?

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.  

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.

- Steve


On Apr 19, 2020, at 8:33 AM, uǝlƃ ☣ [hidden email] 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.

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. 

On Apr 19, 2020, at 8:18 AM, Frank Wimberly [hidden email] wrote:

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.
-- 
☣ uǝlƃ

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Re: Judea Pearl: Book of Why

Frank Wimberly-2
In reply to this post by Marcus G. Daniels
You want to do an agent-based simulation of, say, consumer behavior where the relevant properties of the agents have the joint distribution of a sample.  You want it to be in a context that or for a period of time not realizable with actual subjects...

On Sun, Apr 19, 2020 at 9:30 AM Marcus Daniels <[hidden email]> 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. 

On Apr 19, 2020, at 8:18 AM, Frank Wimberly <[hidden email]> wrote:


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.


Frank

On Sun, Apr 19, 2020 at 9:11 AM uǝlƃ ☣ <[hidden email]> wrote:
The *ensemble* point is the primary reason I regret not being able to parse your response to my Necker cube summarization of EricS' TLDR. It goes back to the original question of how/whether distributional conceptions better catch the unknown unknowns left dangling in the ambience. Pearl's attempts to burst "causality" into graphs, away from chains (though helping to identify chains when they do exist) is along the same line.

To boot, it evokes both Gödel's interpretation of von Neumann's interpretation of Gödel's work (that it takes an infinite expression to describe a thing) and Rosen's definition of complexity (basically anything that requires an infinite number of models to describe).

And, although I can't get my hands on the Rota paper EricS posted, I'm leery of relying on any phenomenology. Heidegger I trust a bit. Husserl not so much. Regardless, I don't think it's *necessary* to go that deep to grok the main point, which is that the transformation should be invertible. We should be able to flip back and forth from goo to thing such that the flipping doesn't change it. The goo we get after flipping from the things should be the same goo we had to start with.

On 4/19/20 6:25 AM, Steven A Smith wrote:
> My work of late (other than SimTable) has been in the realm of trying to analyze ensembles of predictive simulations.   This is a logical next step (forward and backward propogating data and constraints as they are recorded/discovered/postulated) across space (populations) and time.


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Re: Judea Pearl: Book of Why

Steve Smith
In reply to this post by Marcus G. Daniels

Marcus -



I believe that Andrew Niccol DID imagine something like that:


I wish I had a pithy preamble for this dystopian BioPunk reference, but perhaps it speaks for itself?


https://en.wikipedia.org/wiki/Gattaca


- Steve

Steve writes:

< 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. >

There are hundreds of common HLA alleles across humans.   In a diverse country like the US, with hundreds of thousands of positive cases and tens of thousands of deaths the hundreds of alleles would be well sampled.   Too bad our medical surveillance is so bad, and made worse by the moron.  Imagine if everyone had full genome sequencing and every viral sample was deep sequenced. 

Marcus

From: Friam [hidden email] on behalf of Steven A Smith [hidden email]
Sent: Sunday, April 19, 2020 10:11 AM
To: [hidden email] [hidden email]
Subject: Re: [FRIAM] Judea Pearl: Book of Why
 

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.. 

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.  

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.

    https://journals.aps.org/pre/abstract/10.1103/PhysRevE.75.057201

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?

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.  

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.

- Steve

On Apr 19, 2020, at 8:33 AM, uǝlƃ ☣ [hidden email] 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.

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. 

On Apr 19, 2020, at 8:18 AM, Frank Wimberly [hidden email] wrote:

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.
-- 
☣ uǝlƃ

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Re: Judea Pearl: Book of Why

Marcus G. Daniels
< I wish I had a pithy preamble for this dystopian BioPunk reference, but perhaps it speaks for itself? >

Gattaca only considered the consequences of the read genome, not the write genome, or of all the interventions that could be discovered as a result of statistical inference.   Hypothetically, Vincent could have had his heart repaired and not been at risk for heart failure in the first place.

Marcus

From: Friam <[hidden email]> on behalf of Steven A Smith <[hidden email]>
Sent: Sunday, April 19, 2020 10:41 AM
To: [hidden email] <[hidden email]>
Subject: Re: [FRIAM] Judea Pearl: Book of Why
 

Marcus -



I believe that Andrew Niccol DID imagine something like that:


I wish I had a pithy preamble for this dystopian BioPunk reference, but perhaps it speaks for itself?


https://en.wikipedia.org/wiki/Gattaca


- Steve

Steve writes:

< 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. >

There are hundreds of common HLA alleles across humans.   In a diverse country like the US, with hundreds of thousands of positive cases and tens of thousands of deaths the hundreds of alleles would be well sampled.   Too bad our medical surveillance is so bad, and made worse by the moron.  Imagine if everyone had full genome sequencing and every viral sample was deep sequenced. 

Marcus

From: Friam [hidden email] on behalf of Steven A Smith [hidden email]
Sent: Sunday, April 19, 2020 10:11 AM
To: [hidden email] [hidden email]
Subject: Re: [FRIAM] Judea Pearl: Book of Why
 

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.. 

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.  

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.

    https://journals.aps.org/pre/abstract/10.1103/PhysRevE.75.057201

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?

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.  

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.

- Steve

On Apr 19, 2020, at 8:33 AM, uǝlƃ ☣ [hidden email] 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.

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. 

On Apr 19, 2020, at 8:18 AM, Frank Wimberly [hidden email] wrote:

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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Re: Judea Pearl: Book of Why

Prof David West
In reply to this post by Marcus G. Daniels
Marcus said, "Imagine if everyone had full genome sequencing and every viral sample was deep sequenced." Iceland has something close to this already.

davew

On Sun, Apr 19, 2020, at 10:34 AM, Marcus Daniels wrote:
Steve writes:

< 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. >

There are hundreds of common HLA alleles across humans.   In a diverse country like the US, with hundreds of thousands of positive cases and tens of thousands of deaths the hundreds of alleles would be well sampled.   Too bad our medical surveillance is so bad, and made worse by the moron.  Imagine if everyone had full genome sequencing and every viral sample was deep sequenced. 

Marcus



From: Friam <[hidden email]> on behalf of Steven A Smith <[hidden email]>
Sent: Sunday, April 19, 2020 10:11 AM
To: [hidden email] <[hidden email]>
Subject: Re: [FRIAM] Judea Pearl: Book of Why
 

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.. 

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.  

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.

    https://journals.aps.org/pre/abstract/10.1103/PhysRevE.75.057201

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?

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.  

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.

- Steve


On Apr 19, 2020, at 8:33 AM, uǝlƃ ☣ [hidden email] 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.


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. 


On Apr 19, 2020, at 8:18 AM, Frank Wimberly [hidden email] wrote:


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.

-- 
☣ uǝlƃ

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Re: Judea Pearl: Book of Why

Prof David West
In reply to this post by Steve Smith
Not directly relevant, but another good sci-fi about genetics — Daniel Suarez' Change Agent.

davew


On Sun, Apr 19, 2020, at 10:41 AM, Steven A Smith wrote:

Marcus -



I believe that Andrew Niccol DID imagine something like that:


I wish I had a pithy preamble for this dystopian BioPunk reference, but perhaps it speaks for itself?


https://en.wikipedia.org/wiki/Gattaca


- Steve

Steve writes:

< 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. >

There are hundreds of common HLA alleles across humans.   In a diverse country like the US, with hundreds of thousands of positive cases and tens of thousands of deaths the hundreds of alleles would be well sampled.   Too bad our medical surveillance is so bad, and made worse by the moron.  Imagine if everyone had full genome sequencing and every viral sample was deep sequenced. 

Marcus


From: Friam [hidden email] on behalf of Steven A Smith [hidden email]
Sent: Sunday, April 19, 2020 10:11 AM
To: [hidden email] [hidden email]
Subject: Re: [FRIAM] Judea Pearl: Book of Why
 

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.. 

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.  

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.

    https://journals.aps.org/pre/abstract/10.1103/PhysRevE.75.057201

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?

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.  

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.

- Steve

On Apr 19, 2020, at 8:33 AM, uǝlƃ ☣ [hidden email] 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.


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. 


On Apr 19, 2020, at 8:18 AM, Frank Wimberly [hidden email] wrote:


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.

-- 
☣ uǝlƃ

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Re: Judea Pearl: Book of Why

Marcus G. Daniels
In reply to this post by Prof David West
Dave writes:

< Marcus said, "Imagine if everyone had full genome sequencing and every viral sample was deep sequenced." Iceland has something close to this already. >


Marcus

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