PSC Tornado Visualization (2008) [720p] - YouTube

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Re: PSC Tornado Visualization (2008) [720p] - YouTube

gepr
Nah. I think you inferred too far. I read the sentiment simply as a call for balance in the relationship between first principles and opaque but predictive methods. I have to fight this fight all the time in my work. Even though my reputation (shambles that it is) is in mechanistic modeling (a type of first principles), I have to consistently argue that the opaque (ML) or largely meaningless (over-fit) models can be just as useful, depending on the context.

At my first co-op gig, building trailers to house the engineers launching rockets in the desert, a colleague offered to help me build some speakers for my truck. He pulled out these charts and tables that mapped the properties of the acoustic space, power available, speaker sizes, box sizes/shapes, frequencies (mostly lower to resonate in the shelled bed), etc. Bunches of numbers that he claimed to understand, but with my EE classes under my belt I could tell he didn't *really* understand them. >8^D

So, I derived most of the properties of the speakers from (what I thought were) first principles. I got nearly the same answers he got, which was very satisfying for both of us. I think had either of us bailed on our role, the project would have been much less satisfying ... asymmetry between the two types of understanding is unhealthy.

On 5/14/20 5:28 AM, Steven A Smith wrote:
> I *think* this discussion (or this subthread) has devolved to suggesting that predictive power is the only use of modeling (and simulation) whilst explanatory power is not (it is just drama?).  

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Re: PSC Tornado Visualization (2008) [720p] - YouTube

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

Steve writes:

“I *think* this discussion (or this subthread) has devolved to suggesting that predictive power is the only use of modeling (and simulation) whilst explanatory power is not (it is just drama?). “ 

First principles explanations start with some assumptions and reason forward.   The explanation will be wrong if the assumptions are wrong.   If the validation data is inadequate in depth or breadth, or at the wrong scale, the validation that is achieved will be wrong or illusory too.   In Nick’s example, the problem was that flight evidence was on the wrong scale.  If the flight continued for 120 years, I’d argue that is a distinction without a difference.   There won’t be a widow, because she’ll be dead too.

I suspect a lot of the appeal of explanatory power does not come from the elaboration or analysis that derivations provide, but simply from a desire for control, and a desire to have something to talk about.

Some machine learning approaches give simple models, models that do not involve thousands of parameters.  If one gets to the same equations from an automated process, nothing prevents derivations or deconstruction starting from them.   Other machine learning approaches generalize, but give black boxes that are inscrutably complex.   When the latter is far more powerful than the former, what is one to do?  Ignore their utility?

Marcus


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Re: PSC Tornado Visualization (2008) [720p] - YouTube

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

Frank writes:

 

“Which email has that parody of Trump attached to it?  Very funny.”

 

Suppose the work wasn’t done by a student but by an organization like a drug cartel or political action committee.   To achieve the aims of such organizations it would need fidelity not explanatory power.

 

Marcus


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Re: PSC Tornado Visualization (2008) [720p] - YouTube

Frank Wimberly-2
In reply to this post by Marcus G. Daniels
Above I should have written "both/and is better than either/or".  For clarity.

Marc Raibert founded Boston Dynamics which was bought by Google.  They're the people that develop the walking animals, etc that appear in so many videos.

Marc and I did an experiment that involved solving differential equations (first principles) offline and storing the results in very large tables.  In real time the walking machine fits curves (not first principles) to the tables to determine how to move a joint to achieve balance.

Is that an example of a synthesis?
---
Frank C. Wimberly
140 Calle Ojo Feliz,
Santa Fe, NM 87505

505 670-9918
Santa Fe, NM

On Thu, May 14, 2020, 9:08 AM Marcus Daniels <[hidden email]> wrote:

Steve writes:

“I *think* this discussion (or this subthread) has devolved to suggesting that predictive power is the only use of modeling (and simulation) whilst explanatory power is not (it is just drama?). “ 

First principles explanations start with some assumptions and reason forward.   The explanation will be wrong if the assumptions are wrong.   If the validation data is inadequate in depth or breadth, or at the wrong scale, the validation that is achieved will be wrong or illusory too.   In Nick’s example, the problem was that flight evidence was on the wrong scale.  If the flight continued for 120 years, I’d argue that is a distinction without a difference.   There won’t be a widow, because she’ll be dead too.

I suspect a lot of the appeal of explanatory power does not come from the elaboration or analysis that derivations provide, but simply from a desire for control, and a desire to have something to talk about.

Some machine learning approaches give simple models, models that do not involve thousands of parameters.  If one gets to the same equations from an automated process, nothing prevents derivations or deconstruction starting from them.   Other machine learning approaches generalize, but give black boxes that are inscrutably complex.   When the latter is far more powerful than the former, what is one to do?  Ignore their utility?

Marcus

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Pirsig's reflections on "Mu"

Steve Smith
In reply to this post by Marcus G. Daniels
I just read Pirsig's (in "Zen and the Art of Motorcycle Maintenance"
reflections on the Japanese concept of "Mu" and thought of at least a
half-dozen of our threads here...  

https://www.awakin.org/read/view.php?tid=583

I'm wondering if Carl (Tollander) is listening here and might offer his
own perspective (as a Japanophile and practicioner on this edge IMO)

- Steve


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Re: Pirsig's reflections on "Mu"

Marcus G. Daniels
Mu is an unspecified function parameterized by everything and that returns a distribution.   Not as convenient to put into a table as "NA"!

On 5/14/20, 8:30 AM, "Friam on behalf of Steven A Smith" <[hidden email] on behalf of [hidden email]> wrote:

    I just read Pirsig's (in "Zen and the Art of Motorcycle Maintenance"
    reflections on the Japanese concept of "Mu" and thought of at least a
    half-dozen of our threads here...  
   
    https://www.awakin.org/read/view.php?tid=583
   
    I'm wondering if Carl (Tollander) is listening here and might offer his
    own perspective (as a Japanophile and practicioner on this edge IMO)
   
    - Steve
   
   
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Re: PSC Tornado Visualization (2008) [720p] - YouTube

thompnickson2
In reply to this post by gepr
I like this example.

Nicholas Thompson
Emeritus Professor of Ethology and Psychology
Clark University
[hidden email]
https://wordpress.clarku.edu/nthompson/
 


-----Original Message-----
From: Friam <[hidden email]> On Behalf Of u?l? ?
Sent: Thursday, May 14, 2020 8:58 AM
To: FriAM <[hidden email]>
Subject: Re: [FRIAM] PSC Tornado Visualization (2008) [720p] - YouTube

Nah. I think you inferred too far. I read the sentiment simply as a call for balance in the relationship between first principles and opaque but predictive methods. I have to fight this fight all the time in my work. Even though my reputation (shambles that it is) is in mechanistic modeling (a type of first principles), I have to consistently argue that the opaque (ML) or largely meaningless (over-fit) models can be just as useful, depending on the context.

At my first co-op gig, building trailers to house the engineers launching rockets in the desert, a colleague offered to help me build some speakers for my truck. He pulled out these charts and tables that mapped the properties of the acoustic space, power available, speaker sizes, box sizes/shapes, frequencies (mostly lower to resonate in the shelled bed), etc. Bunches of numbers that he claimed to understand, but with my EE classes under my belt I could tell he didn't *really* understand them. >8^D

So, I derived most of the properties of the speakers from (what I thought were) first principles. I got nearly the same answers he got, which was very satisfying for both of us. I think had either of us bailed on our role, the project would have been much less satisfying ... asymmetry between the two types of understanding is unhealthy.

On 5/14/20 5:28 AM, Steven A Smith wrote:
> I *think* this discussion (or this subthread) has devolved to
> suggesting that predictive power is the only use of modeling (and simulation) whilst explanatory power is not (it is just drama?).

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Re: PSC Tornado Visualization (2008) [720p] - YouTube

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

Frank writes:

 

“Marc and I did an experiment that involved solving differential equations (first principles) offline and storing the results in very large tables.  In real time the walking machine fits curves (not first principles) to the tables to determine how to move a joint to achieve balance.

Is that an example of a synthesis?”

 

I’d call that a space/speed tradeoff, a computational trick to work within resource constraints.

 

Marcus


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Re: PSC Tornado Visualization (2008) [720p] - YouTube

thompnickson2
In reply to this post by Frank Wimberly-2

Frank,

 

I am way out of my territory, here, but are “differential equasions” necessarily “first principles”.  It seems to me that one could derive differential equasions based on any fictions.  How do I misunderstand what is going on, here?

 

Nick

 

Nicholas Thompson

Emeritus Professor of Ethology and Psychology

Clark University

[hidden email]

https://wordpress.clarku.edu/nthompson/

 

 

From: Friam <[hidden email]> On Behalf Of Frank Wimberly
Sent: Thursday, May 14, 2020 9:20 AM
To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
Subject: Re: [FRIAM] PSC Tornado Visualization (2008) [720p] - YouTube

 

Above I should have written "both/and is better than either/or".  For clarity.

 

Marc Raibert founded Boston Dynamics which was bought by Google.  They're the people that develop the walking animals, etc that appear in so many videos.

 

Marc and I did an experiment that involved solving differential equations (first principles) offline and storing the results in very large tables.  In real time the walking machine fits curves (not first principles) to the tables to determine how to move a joint to achieve balance.

Is that an example of a synthesis?

---
Frank C. Wimberly
140 Calle Ojo Feliz,
Santa Fe, NM 87505

505 670-9918
Santa Fe, NM

 

On Thu, May 14, 2020, 9:08 AM Marcus Daniels <[hidden email]> wrote:

Steve writes:

“I *think* this discussion (or this subthread) has devolved to suggesting that predictive power is the only use of modeling (and simulation) whilst explanatory power is not (it is just drama?). “ 

First principles explanations start with some assumptions and reason forward.   The explanation will be wrong if the assumptions are wrong.   If the validation data is inadequate in depth or breadth, or at the wrong scale, the validation that is achieved will be wrong or illusory too.   In Nick’s example, the problem was that flight evidence was on the wrong scale.  If the flight continued for 120 years, I’d argue that is a distinction without a difference.   There won’t be a widow, because she’ll be dead too.

I suspect a lot of the appeal of explanatory power does not come from the elaboration or analysis that derivations provide, but simply from a desire for control, and a desire to have something to talk about.

Some machine learning approaches give simple models, models that do not involve thousands of parameters.  If one gets to the same equations from an automated process, nothing prevents derivations or deconstruction starting from them.   Other machine learning approaches generalize, but give black boxes that are inscrutably complex.   When the latter is far more powerful than the former, what is one to do?  Ignore their utility?

Marcus

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Re: PSC Tornado Visualization (2008) [720p] - YouTube

gepr
You're right. There are mechanistic DE models and non-mechanistic DE models.

On 5/14/20 9:09 AM, [hidden email] wrote:
> I am way out of my territory, here, but are “differential equasions” necessarily “first principles”.  It seems to me that one could derive differential equasions based on any fictions.  How do I misunderstand what is going on, here?


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uǝʃƃ ⊥ glen
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Re: PSC Tornado Visualization (2008) [720p] - YouTube

Frank Wimberly-2
What Glen said.

---
Frank C. Wimberly
140 Calle Ojo Feliz,
Santa Fe, NM 87505

505 670-9918
Santa Fe, NM

On Thu, May 14, 2020, 10:14 AM uǝlƃ ☣ <[hidden email]> wrote:
You're right. There are mechanistic DE models and non-mechanistic DE models.

On 5/14/20 9:09 AM, [hidden email] wrote:
> I am way out of my territory, here, but are “differential equasions” necessarily “first principles”.  It seems to me that one could derive differential equasions based on any fictions.  How do I misunderstand what is going on, here?


--
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Re: PSC Tornado Visualization (2008) [720p] - YouTube

Frank Wimberly-2
In reply to this post by gepr
But  in the context of maintaining balance in a walking machine they are derivable from classical mechanics.

---
Frank C. Wimberly
140 Calle Ojo Feliz,
Santa Fe, NM 87505

505 670-9918
Santa Fe, NM

On Thu, May 14, 2020, 10:14 AM uǝlƃ ☣ <[hidden email]> wrote:
You're right. There are mechanistic DE models and non-mechanistic DE models.

On 5/14/20 9:09 AM, [hidden email] wrote:
> I am way out of my territory, here, but are “differential equasions” necessarily “first principles”.  It seems to me that one could derive differential equasions based on any fictions.  How do I misunderstand what is going on, here?


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Re: PSC Tornado Visualization (2008) [720p] - YouTube

thompnickson2

Got it!  Thanks everybody.  N

 

Nicholas Thompson

Emeritus Professor of Ethology and Psychology

Clark University

[hidden email]

https://wordpress.clarku.edu/nthompson/

 

 

From: Friam <[hidden email]> On Behalf Of Frank Wimberly
Sent: Thursday, May 14, 2020 10:20 AM
To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
Subject: Re: [FRIAM] PSC Tornado Visualization (2008) [720p] - YouTube

 

But  in the context of maintaining balance in a walking machine they are derivable from classical mechanics.

---
Frank C. Wimberly
140 Calle Ojo Feliz,
Santa Fe, NM 87505

505 670-9918
Santa Fe, NM

 

On Thu, May 14, 2020, 10:14 AM uǝlƃ <[hidden email]> wrote:

You're right. There are mechanistic DE models and non-mechanistic DE models.

On 5/14/20 9:09 AM, [hidden email] wrote:
> I am way out of my territory, here, but are “differential equasions” necessarily “first principles”.  It seems to me that one could derive differential equasions based on any fictions.  How do I misunderstand what is going on, here?


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Re: PSC Tornado Visualization (2008) [720p] - YouTube

gepr
Dude! You're passing on the opportunity to troll for the meaning of "mechanics", "mechanism", etc? The social distancing must be getting to you. 8^D

On 5/14/20 9:23 AM, [hidden email] wrote:
> Got it!  Thanks everybody.  N

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