Chaos Scientist Finds Hidden Financial Risks That Regulators Miss Oxford Professor Doyne Farmer is working with central banks to improve stress testing.

[Tom Johnson]

https://www.bloomberg.com/news/features/2019-10-03/chaos-scientist-finds-hidden-financial-risks-that-regulators-miss?fbclid=IwAR17xM4UEQ2cRYGpBqPxQhPqP2v5s8UkKQCCpVZcFptLnwpUiudGnK2DouU  

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[Barry MacKichan]

Thanks for the link.

This is totally off-topic, but in the chaos of Facebook and Twitter, I believe a small note of appreciation for technology is in order.

In the linked article, there is a picture of Farmer lying on a couch in front of his bookshelf. The resolution of web graphics is now high enough that I could indulge in one of my favorite hobbies. I copied the picture, pasted it into a graphics program and enlarged and rotated it. Aaah. Now I can browse through the titles on his shelves.

--Barry

On 3 Oct 2019, at 18:11, Tom Johnson wrote:

https://www.bloomberg.com/news/features/2019-10-03/chaos-scientist-finds-hidden-financial-risks-that-regulators-miss?fbclid=IwAR17xM4UEQ2cRYGpBqPxQhPqP2v5s8UkKQCCpVZcFptLnwpUiudGnK2DouU  

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[gepr]

"Existential Psychotherapy"! I suspect that's 500 pages I won't read. But what a cool title.

On 11/13/19 7:44 AM, Barry MacKichan wrote:
> Thanks for the link.
>
> This is totally off-topic, but in the chaos of Facebook and Twitter, I believe a small note of appreciation for technology is in order.
>
> In the linked article, there is a picture of Farmer lying on a couch in front of his bookshelf. The resolution of web graphics is now high enough that I could indulge in one of my favorite hobbies. I copied the picture, pasted it into a graphics program and enlarged and rotated it. Aaah. Now I can browse through the titles on his shelves.

--
☣ uǝlƃ

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[Frank Wimberly-2]

Duquesne University in Pittsburgh is a Catholic school whose most recognized graduate programs are in Existencial Philosophy and Existencial Clinical Psychology.  When I was in graduate school at Carnegie Mellon and the University of Pittsburgh I had friends at Duquesne.  At that time Duquesne doctoral students expected to spend 6 to 8 years in their programs.  

You should read the book, Glen.  I am sure it's fascinating.  Doyne Farmer probably thinks so.

Frank

On Wed, Nov 13, 2019, 11:44 AM uǝlƃ ☣ <[hidden email]> wrote:

"Existential Psychotherapy"! I suspect that's 500 pages I won't read. But what a cool title.

On 11/13/19 7:44 AM, Barry MacKichan wrote:
> Thanks for the link.
>
> This is totally off-topic, but in the chaos of Facebook and Twitter, I believe a small note of appreciation for technology is in order.
>
> In the linked article, there is a picture of Farmer lying on a couch in front of his bookshelf. The resolution of web graphics is now high enough that I could indulge in one of my favorite hobbies. I copied the picture, pasted it into a graphics program and enlarged and rotated it. Aaah. Now I can browse through the titles on his shelves.

--
☣ uǝlƃ

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[gepr]

Heh, "should" is a spectrum. 8^) I *should* drive the speed limit, too. But will I? No, probably not. I've had Penrose's "Cycles of Time" on my shelf, unread, for almost a decade, now. And Existential Psychotherapy would have to get in line behind that.

On 11/13/19 10:58 AM, Frank Wimberly wrote:
> Duquesne University in Pittsburgh is a Catholic school whose most recognized graduate programs are in Existencial Philosophy and Existencial Clinical Psychology.  When I was in graduate school at Carnegie Mellon and the University of Pittsburgh I had friends at Duquesne.  At that time Duquesne doctoral students expected to spend 6 to 8 years in their programs.  
>
> You should read the book, Glen.  I am sure it's fascinating.  Doyne Farmer probably thinks so.

--
☣ uǝlƃ

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[Frank Wimberly-2]

"Existential" not "Existencial".  I didn't think that looked right.  Where's autocorrect when you need it.

Maybe "existencial" is a word too.

On Wed, Nov 13, 2019, 12:07 PM uǝlƃ ☣ <[hidden email]> wrote:

Heh, "should" is a spectrum. 8^) I *should* drive the speed limit, too. But will I? No, probably not. I've had Penrose's "Cycles of Time" on my shelf, unread, for almost a decade, now. And Existential Psychotherapy would have to get in line behind that.

On 11/13/19 10:58 AM, Frank Wimberly wrote:
> Duquesne University in Pittsburgh is a Catholic school whose most recognized graduate programs are in Existencial Philosophy and Existencial Clinical Psychology.  When I was in graduate school at Carnegie Mellon and the University of Pittsburgh I had friends at Duquesne.  At that time Duquesne doctoral students expected to spend 6 to 8 years in their programs.  
>
> You should read the book, Glen.  I am sure it's fascinating.  Doyne Farmer probably thinks so.

--
☣ uǝlƃ

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[Frank Wimberly-2]

Slightly interesting:

I write texts and emails in both Spanish and English.  "Existencial" is the Spanish word.  I bet I wouldn't have made that mistake before my Spanish era.

On Wed, Nov 13, 2019, 12:36 PM Frank Wimberly <[hidden email]> wrote:

"Existential" not "Existencial".  I didn't think that looked right.  Where's autocorrect when you need it.

Maybe "existencial" is a word too.

On Wed, Nov 13, 2019, 12:07 PM uǝlƃ ☣ <[hidden email]> wrote:

Heh, "should" is a spectrum. 8^) I *should* drive the speed limit, too. But will I? No, probably not. I've had Penrose's "Cycles of Time" on my shelf, unread, for almost a decade, now. And Existential Psychotherapy would have to get in line behind that.

On 11/13/19 10:58 AM, Frank Wimberly wrote:
> Duquesne University in Pittsburgh is a Catholic school whose most recognized graduate programs are in Existencial Philosophy and Existencial Clinical Psychology.  When I was in graduate school at Carnegie Mellon and the University of Pittsburgh I had friends at Duquesne.  At that time Duquesne doctoral students expected to spend 6 to 8 years in their programs.  
>
> You should read the book, Glen.  I am sure it's fascinating.  Doyne Farmer probably thinks so.

--
☣ uǝlƃ

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[gepr]

You mean *very* interesting. 8^) Because it's a Spanish word, you might be right. But as Marcus' serialization glitch demonstrated, it may not be that simple. I tend to use very long pass phrases. And it never ceases to amaze me that whether I'll be able to type them right on the 1st try or the 10th try seems Gaussian. On the days when I'm confident I'll get it right quickly, it can take me 7-10 tries. And on days I'm foggy or have just lifted weights (so my hands are stiff) and am convinced I won't get it right, I can sometimes get it right on the 1st try. And I have little idea what errors I'm making ... did I hit the space bar too soon? Did I mix up "I" vs "U"? I'm not sure how I could test such a thing without compromising my pass phrases...

And just the other day, I was arguing about Intelligent Design at the pub and was trying to point out that we don't "come from monkeys", when my friend told me I'd said "we're descended from the same ancestor as Christians" ... I thought I'd said "we're descended from the same ancestor as monkeys". [sigh] I blamed it on the beer, but secretly doubt that.

On 11/14/19 4:58 PM, Frank Wimberly wrote:
> Slightly interesting:
>
> I write texts and emails in both Spanish and English.  "Existencial" is the Spanish word.  I bet I wouldn't have made that mistake before my Spanish era.

--
☣ uǝlƃ

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[Marcus G. Daniels]

Generative machine learning seems a heck of a lot easier than ABMs for stress testing. 

 

From: Friam <[hidden email]> on behalf of Barry MacKichan <[hidden email]>
Reply-To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
Date: Wednesday, November 13, 2019 at 7:45 AM
To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
Subject: Re: [FRIAM] Chaos Scientist Finds Hidden Financial Risks That Regulators Miss Oxford Professor Doyne Farmer is working with central banks to improve stress testing.

 

Thanks for the link.

This is totally off-topic, but in the chaos of Facebook and Twitter, I believe a small note of appreciation for technology is in order.

In the linked article, there is a picture of Farmer lying on a couch in front of his bookshelf. The resolution of web graphics is now high enough that I could indulge in one of my favorite hobbies. I copied the picture, pasted it into a graphics program and enlarged and rotated it. Aaah. Now I can browse through the titles on his shelves.

--Barry

On 3 Oct 2019, at 18:11, Tom Johnson wrote:

https://www.bloomberg.com/news/features/2019-10-03/chaos-scientist-finds-hidden-financial-risks-that-regulators-miss?fbclid=IwAR17xM4UEQ2cRYGpBqPxQhPqP2v5s8UkKQCCpVZcFptLnwpUiudGnK2DouU  

============================================
Tom Johnson - [hidden email]
Institute for Analytic Journalism   --     Santa Fe, NM USA
505.577.6482(c)                                    505.473.9646(h)
NM Foundation for Open Government
Check out It's The People's Data                 

============================================

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[Steve Smith]

On 11/14/19 6:10 PM, Marcus Daniels wrote:

Generative machine learning seems a heck of a lot easier than ABMs for stress testing. 

Agent-based models, used in fields from biology to sociology, are bottom-up, simulating the messy interactions of hundreds and even millions of agents—human cells or attitudes or financial firms—to explain the behavior of a complex system.

I think the point of Agent Models is to yield *explanatory* not just *predictive results?

 

From: Friam [hidden email] on behalf of Barry MacKichan [hidden email]
Reply-To: The Friday Morning Applied Complexity Coffee Group [hidden email]
Date: Wednesday, November 13, 2019 at 7:45 AM
To: The Friday Morning Applied Complexity Coffee Group [hidden email]
Subject: Re: [FRIAM] Chaos Scientist Finds Hidden Financial Risks That Regulators Miss Oxford Professor Doyne Farmer is working with central banks to improve stress testing.

 

Thanks for the link.

This is totally off-topic, but in the chaos of Facebook and Twitter, I believe a small note of appreciation for technology is in order.

In the linked article, there is a picture of Farmer lying on a couch in front of his bookshelf. The resolution of web graphics is now high enough that I could indulge in one of my favorite hobbies. I copied the picture, pasted it into a graphics program and enlarged and rotated it. Aaah. Now I can browse through the titles on his shelves.

--Barry

On 3 Oct 2019, at 18:11, Tom Johnson wrote:

https://www.bloomberg.com/news/features/2019-10-03/chaos-scientist-finds-hidden-financial-risks-that-regulators-miss?fbclid=IwAR17xM4UEQ2cRYGpBqPxQhPqP2v5s8UkKQCCpVZcFptLnwpUiudGnK2DouU  

============================================
Tom Johnson - [hidden email]
Institute for Analytic Journalism   --     Santa Fe, NM USA
505.577.6482(c)                                    505.473.9646(h)
NM Foundation for Open Government
Check out It's The People's Data                 

============================================

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[gepr]

Since Marcus hasn't answered, I think it's important that he pre-pended ML with "generative". There's plenty to argue about with respect to that word (e.g. my complaint that EricC _thinned_ relativism). But in my own work, I've made the somewhat hand-waving argument that mathematical models (e.g. systems of ODEs) are thin and component-based models are thick. But, I'll take the opportunity, here, to argue against myself (and against you, Steve 8^) that not only can mathematical models be "a little bit thick", but that models induced into combined-but-separable probability distributions are also "a little bit thick".

For math models, each term of some equation kinda-sorta represents a component of the model and then various operators are used to integrate those components (+ and - are the most boring). At the next layer, different constraints, mechanisms, components might be modeled by entirely different equations that have to be solved as a system. So, even in this brief conception, it seems clear that these models *can be* mechanistic ... i.e. explanatory, at least to some extent.

The same might be said of an induction method that produces, say, bifurcated components. If you find 2 modes in some output, then it seems reasonable that there might be 2 mechanisms at work.

So, in direct response to your response. Had you said the point of agent models is to yield *more* explanatory results than a generative ML classifier, I don't think there's much room to argue. We could turn the tables and argue that agent models might be more explanatory, but they'll be less predictive. So, maybe the total power is similar and we should all use *both*. (That's what I argue to my clients ... but it's rarely done because the skill sets are a bit different and it's more expensive. [sigh])


On 11/14/19 5:56 PM, Steven A Smith wrote:
>
> On 11/14/19 6:10 PM, Marcus Daniels wrote:
>>
>> Generative machine learning seems a heck of a lot easier than ABMs for stress testing. 
>>
>     Agent-based models, used in fields from biology to sociology, are bottom-up, simulating the messy interactions of hundreds and even millions of agents—human cells or attitudes or financial firms—to explain the behavior of a complex system.
>
> I think the point of Agent Models is to yield *explanatory* not just *predictive results?

--
☣ uǝlƃ

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[Marcus G. Daniels]

Stress testing is the activity of subjecting a potential investment strategy to plausible volatility from the market.    There is a record of what the market has done in the past, so one way to do stress testing is to do retrodiction on many different past periods to see how well the strategy performs.  One doesn't cheat in that testing (peek into the known future to train the model), beyond the recognition that the strategy must integrate knowledge of how markets work.   That kind of circularity exists in science.  Where does a hypothesis come from if not from past experience?

A limitation of this kind of protocol is that every bit of data one uses for training must be kept separate from the data one uses for testing.  While cross validation is possible -- using different training and testing subsets -- at the end of the day history only tells you what has happened not what could happen.    

Generative machine learning techniques can learn many-body joint probabilities of market events like price moves of different instruments.   Using these techniques one can generate not just a tape recording of what happened, but plausible looking trading patterns that share similar patterns -- like might occur in the future.   For ABM to be better, it seems to me, there has to be the possibility that a modeler can imagine subtleties of individual behaviors that, when coupled together, create phenomena that has never been seen on markets where equities can trade dozens of times a second.   Such a simulation could involve hundreds, or even thousands of types of traders each with their own evolving motivations.   Never mind the difficulty of implementing such a model, how could those rules be learned if not inferred from data?   How does one presume to know the motivations and resources of traders that often take active measures to hide themselves, e.g. disguise their price impact?

Perhaps this is more of an explanatory (academic) effort, positing that stress testing is possible using relatively simple models.   That's a nice hypothesis, maybe it even could explain a lot of variation, and the result of that would be satisfying, and a contribution to theory.   I expect practitioners have a different, higher, bar.

Marcus

On 11/15/19, 10:30 AM, "Friam on behalf of uǝlƃ ☣" <[hidden email] on behalf of [hidden email]> wrote:

    Since Marcus hasn't answered, I think it's important that he pre-pended ML with "generative". There's plenty to argue about with respect to that word (e.g. my complaint that EricC _thinned_ relativism). But in my own work, I've made the somewhat hand-waving argument that mathematical models (e.g. systems of ODEs) are thin and component-based models are thick. But, I'll take the opportunity, here, to argue against myself (and against you, Steve 8^) that not only can mathematical models be "a little bit thick", but that models induced into combined-but-separable probability distributions are also "a little bit thick".
   
    For math models, each term of some equation kinda-sorta represents a component of the model and then various operators are used to integrate those components (+ and - are the most boring). At the next layer, different constraints, mechanisms, components might be modeled by entirely different equations that have to be solved as a system. So, even in this brief conception, it seems clear that these models *can be* mechanistic ... i.e. explanatory, at least to some extent.
   
    The same might be said of an induction method that produces, say, bifurcated components. If you find 2 modes in some output, then it seems reasonable that there might be 2 mechanisms at work.
   
    So, in direct response to your response. Had you said the point of agent models is to yield *more* explanatory results than a generative ML classifier, I don't think there's much room to argue. We could turn the tables and argue that agent models might be more explanatory, but they'll be less predictive. So, maybe the total power is similar and we should all use *both*. (That's what I argue to my clients ... but it's rarely done because the skill sets are a bit different and it's more expensive. [sigh])
   
   
    On 11/14/19 5:56 PM, Steven A Smith wrote:
    >
    > On 11/14/19 6:10 PM, Marcus Daniels wrote:
    >>
    >> Generative machine learning seems a heck of a lot easier than ABMs for stress testing.
    >>
    >     Agent-based models, used in fields from biology to sociology, are bottom-up, simulating the messy interactions of hundreds and even millions of agents—human cells or attitudes or financial firms—to explain the behavior of a complex system.
    >
    > I think the point of Agent Models is to yield *explanatory* not just *predictive results?
   
    --
    ☣ uǝlƃ
   
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