Any non-biological complex systems?

[gepr]

On 05/29/2017 09:02 AM, Steven A Smith wrote:
> I appreciate your stating it this way.  I did hear Nick ask if a system could (somehow?) choose it's own boundaries and dismissed it as (yet another) distraction but would now like to hear more.   It felt like an anthropomorphism to suggest a system could "choose" it's own boundaries, but I'm open to having that explored if anyone can/will.

Well, I think it's a core part of the original post.  I'd be happy to see Russ disagree with me and object to the idea that a boundary is necessary for being a symbol machine.  With regard to "choosing", that was the entire gist behind Rosen's considerable side-track.  I'm not a scholar, or even a serious person in any way.  But I think all of Rosen's other yaddayadda was an outgrowth of his conception of agency.  E.g. what is the minimal construct that can be said to have agency?  He came up with M,R-Systems.  And it eventually lead him to his "anticipatory systems".  Our modern arguments about free will and choice could be enlightened quite a bit by that thread.

Please note that I disagree with most of what Rosen argued.  But to see it ignored (or attributed to others) is much worse than disagreeing with it. 8^)

--
☣ glen

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[Stephen Guerin-5]

On Mon, May 29, 2017 at 8:28 AM, Marcus Daniels <[hidden email]> wrote:

Hurricanes are an instances of multiscale fluid dynamics, or set of problems (cyclogenesis, heat engine, cyclolysis).   They are all complicated coupled systems, but it is not clear to me what extra insight is gained by calling them complex systems. 

Great question, Marcus. I think this helps reinforce my point more clearly. 

Depending on how you *model* cyclogenesis, there may not be any benefit using the language of complex systems. Just because it's a complicated coupled system, does not mean it's a complex system (in my definition).

I'm not an expert on cyclogenesis, but a quick wiki lookup led me to one model description being around a coupled differential equation model using Q-Vectors:

And here's more on Q-Vectors.

To me, this class of model is closer to my second model of a hurricane (random walker with specific terms of curl for Coriolis and and a global wind vector) than the first (dissipative structure formation where vorticity would be a symmetry breaking dynamic). And the second one, I offered was not a complex system. 

To me, the Q-Vectors model of cyclogenesis is missing micro and macro frames and probably would be a stretch to classify it as a complex systems model. Further, terms like vorticity is a prescribed time-based derivative and not emergent in the model. In alternative models, by contrast, vorticity would result from symmetry breaking in the critical regime and would be a candidate order parameter. I would be more likely to classify that type of model as a Complex Systems model.

That said, I would more likely classify the Q-Vectors model as Dynamical System model instead of a Complex System. I would argue knowing it's a Dynamical System you get the benefit to automatically know:

• it can be characterized with phase spaces with attractors and repellors.
• it has phase space will have basins of attraction and related separatices
• it may have chaotic regimes in phase space

While all complex systems models are dynamic they may not necessarily qualify as Dynamical Systems where time is modeled explicitly. And it is usually the case that a Dynamical Systems model is not a Complex System Model. Some researchers would disagree with me on this point. Dynamical Systems and Complex Systems language are often used interchangeably by different complexity researchers and the boundaries are fuzzy in practice.

If we were able to classify it as a Complex Systems model, it allows one to import what's known to universally to apply to complex systems. eg:

• the model will have narrow critical regimes which will have:
• fractal pattern formation with powerlaw statistics
• long range correlation in microlevel agents/entities
• critical slowing down
• critical fluctuations
• high variation between runs (unpredictable) and sensitivity to initial conditions (chaotic)
• symmetry breaking of macroscopic properties
• rapid increase on constraints on degrees of freedom on microlevel components
• highly predictable macroscopic patterns on either side of the critical regime
• potential for self-tuning
• It will have a universal order parameter of symmetry breaking at the macro scale. What breaks symmetry will be model dependent
• I assert (I can't yet prove) that it will have a universal control parameter which can be characterized by the asymmetries of the micro-level interactions. eg:
• the asymmetry of deceleration in deceleration and acceleration in a traffic model is a control parameter that can move the order parameter of the macroscopic symmetry breaking of a backward shockwave of a traffic jam through a phase transition.
• the asymmetry of attraction and repulsion in a flocking model can move the macroscopic symmetry-breaking of linear momentum of a flock through a phase transition

Another benefit of classifying it as a Complex Systems model is that it allows one to identify subclasses within complex systems models and find homologies which I think Nick would agree is the real power of metaphor in science. For example, a subclass of models in complex systems is a percolation model which exhibits all the characteristics above. But further, if I say a particular forest fire model, particular rumor model and a particular voter model are all of class percolation models, I can make many creative leaps back and forth between the three. And like all uses of metaphors it would be instructive when the metaphors break down between the systems. However, if the models are equivalent, it allows communication between disciplines.

Another example is a particular ant food foraging model and a particular lightning model of dialectic breakdown which are equivalent in their implementation. This allows a Plasma Physicist to now have a fruitful interaction with an Entomologist and do some joint research.

One last important point as I try to close myself out from this thread: I recognize that Complexity Research taken as a whole is a big tent and Complexity covers many loosely-related areas:

• Network Dynamics / Graph Theory
• Self-Organization
• Evolutionary Dynamics and Evolutionary Computation
• Dynamical Systems
• Iterative Game Theory
• Computational Complexity
• Ecological Dynamics
• Adaptation, Robustness and Resilience
• Chaos Theory
• Catastrophe Theory
• Nonlinear Dynamics
• Non-Equilibrium Thermodynamics
• Non-Extensive Statistical Mechanics
• etc

It's gotten to the point where SFI describes itself more as a trans-disciplinary research institute than an institute focused on Complexity.

The closest you get to agreement on a definition of Complexity which you'll see in the Complexiy Explore Video that Owen linked to is something like:

   "A system of multiple interacting components that have the capacity for emergent behavior."

I'm good with that and recognize there's fuzzy terms lurking.

In the list of areas above, my definitions of Complex Systems is very biased toward Self-Organization an Non-Equilibrium Thermodynamics as it's a handle on which I can make sense of the world and feel I can make some progress. When I use a definition of Complex Systems on this thread, I recognize it's particular to my understanding and many researchers have their own definitions.  As you start to look at models in other spaces in Complex Systems research, my definitions will be strained to apply. It is not my intent to restrict other researchers from using the term Complex Systems based on my narrow definitions. 

If Russ and Glenn want to define Complex Systems in their way, there's plenty of precedent. I will just point out where there definitions are different than mine or push back if they assert my models don't qualify as Complex Systems.

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[Stephen Guerin-5]

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[Marcus G. Daniels]

Stephen writes:

 

“Dynamical Systems and Complex Systems language are often used interchangeably by different complexity researchers and the boundaries are fuzzy in practice.

 

I would say a modelling effort would be more of interest to the Complex Systems community, if say, a weather model were coupled to a weather modification effort and the weather modification effort was coupled to economic or defense concerns.   In your second example, it is not crucial to have a sophisticated physics model of the weather.   In the example above, it would be.

 

Marcus

 

 

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[Stephen Guerin-5]

I agree. 

It would still be left open as to whether you choose do model the weather, economics or defense systems as Dynamical Systems, Complex Systems (ABM) or Discrete Event Queuing models. The whole coupled system could be one way or the other or most probably a hybrid

To take an example of traffic modeling that we've done we use all three:

For large spatial areas with many roads, we'll model traffic as a Dynamical System of coupled differential equations of traffic density flowing from sources to sinks. As we zoom in, we explode the road densities to agent-based complex systems models that can have driver-driver interactions that allows for congestion and traffic jam dynamics. As we get to intersections, we transition from agent-based models and model the intersections as a discrete event queue where the traffic light is moving cars from incoming road graph edges to outgoing edges.

On Mon, May 29, 2017 at 11:33 AM, Marcus Daniels <[hidden email]> wrote:

Stephen writes:

 

“Dynamical Systems and Complex Systems language are often used interchangeably by different complexity researchers and the boundaries are fuzzy in practice.

 

I would say a modelling effort would be more of interest to the Complex Systems community, if say, a weather model were coupled to a weather modification effort and the weather modification effort was coupled to economic or defense concerns.   In your second example, it is not crucial to have a sophisticated physics model of the weather.   In the example above, it would be.

 

Marcus

 

 

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[Nick Thompson]

SG,

 

There are now THREE issues lurking here between us.

 

IS THE CRITERION FOR A SYSTEM ARBITRARY: You say yes; I say no.  We’ve already covered that ground.

 

IS A HURRICANE A SYSTEM:  For me, that is the question of whether the collection of thunderstorms we call a hurricane interact with one another more than they interact with their collective surroundings.  Another way to put this question is in terms of redundancy.  If we were to go about describing the movements of the thunderstorms of a hurricane, would we get a simpler, less redundant description if we referred their movements to the center of the hurricane.  I think the answer to this question is clearly YES.

 

IS A HURRICANE COMPLEX?  For me, complexity means “multi-layered” .  So, a complex system is one composed of other systems.  A hurricane is a system of thunderstorms which themselves are a system of thermals (handwaving, here).  Thus a hurricane is at least a three-level system.  So, yes.  It is complex.

 

SS, am I splitting hairs or playing at language?  Absolutely not.  Or if I am, shoot me now before I do more harm. What we are arguing about here is whether complexity science actually has a wet edge, or whether you are painting yourselves into a corner.   I gather that you, and most of the folks on this list want to define complex in terms of its dynamics.  In other words you want to define Complexity-sub-SG as the causes of complexity-sub-NST. My suspicion is that this kind of definition will lead you into a devastating circularity loop, similar to the circularity loop that follows when people define adaptation as whatever natural selection produces.  Being in a circularity loop is like participating in a square dance; it’s lots of fun, and you work up a sweat, but you don’t actually get anywhere. It is circular reasoning, I suspect, that gives complexity talk some of the aura of a cult. 

 

Now, circularity in scientific reasoning is not quite the anti-heuristic poison I have always taken it to be.  Much interesting research has been done within the circular adaptionist frame work of contemporary evolutionary psychology, for instance;  I don’t know complexity science well enough to say, but the success of Simtable is evidence enough to me of its creativity.  But, I would argue, that despite all this scientific activity, not much progress has been made concerning the fundamental question of the selective origins of natural design.  In a similar way, hearing you guys argue, I wonder if much progress has been made on the question of what conditions make possible the spontaneous progressive layering of natural systems.   Or if it has been done, it has been done by people who did not define complexity in terms of its processes, but rather in terms of its products.

 

Eric Smith? 

 

 

Nick

 

 

 

 

Nicholas S. Thompson

Emeritus Professor of Psychology and Biology

Clark University

http://home.earthlink.net/~nickthompson/naturaldesigns/

 

From: Friam [mailto:[hidden email]] On Behalf Of Stephen Guerin
Sent: Monday, May 29, 2017 1:11 AM
To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
Subject: Re: [FRIAM] Any non-biological complex systems?

 

 Nick asks:

Is a hurricane a “complex system”? 

 

It depends. What is your metaphor (model) of a hurricane? 

 

If I wanted to understand how a hurricane forms, I might model dissipative structure formation in the presence of temperature and pressure gradients. I would call this a complex system.

 

If I needed to add a hurricane track simulation to our Simtable, for the purposes of how my customers would use it for emergency planning, it would probably be enough to model its track as a random walker biased by global winds and a curve parameter to represent the Coriolis effect. I would not call this a complex system.

 

 

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[Stephen Guerin-5]

On Mon, May 29, 2017 at 12:10 PM, Nick Thompson <[hidden email]> wrote:

SG,

 

There are now THREE issues lurking here between us.

 

IS THE CRITERION FOR A SYSTEM ARBITRARY: You say yes; I say no.  We’ve already covered that ground.

In my post, I said it is not arbitrary. It's a function of what the researcher is trying to use it for or explain.

 

 

IS A HURRICANE A SYSTEM:  For me, that is the question of whether the collection of thunderstorms we call a hurricane interact with one another more than they interact with their collective surroundings.  Another way to put this question is in terms of redundancy.  If we were to go about describing the movements of the thunderstorms of a hurricane, would we get a simpler, less redundant description if we referred their movements to the center of the hurricane.  I think the answer to this question is clearly YES.

Yes you could model the movement in a simpler way by modeling the movement of the center point. And that was my second model of a hurricane as a random walker biased by a global wind vector and Coriolis curve term. And I said that was not a complex system.

 

 

IS A HURRICANE COMPLEX?  For me, complexity means “multi-layered” .  So, a complex system is one composed of other systems.  A hurricane is a system of thunderstorms which themselves are a system of thermals (handwaving, here).  Thus a hurricane is at least a three-level system.  So, yes.  It is complex.

I agree about complex systems as having multiple layers - a macro scale and a micro scale. I would say there's one system. If I was trying to model a hurricane in my first example of an emergent vortex dissipating temperature and pressure gradients, I would model the air with a combination of air particles and patches of air - at LANL they would describe these as particle in a box models or hybrid lagrangian and eulerian models. I would not introduce thunderstorms at the micro level. But there's many ways to skin a hurricane :-)

Some would say the micro level air particles and air cell components which I would model as finite state machines (agents with a lower case "a") are systems in their own right and have boundaries. I don't see the benefit of calling them systems as their aren't multiple interacting components within them. But don't feel like arguing too hard here.

 

Eric Smith? 

Yes, where are you Eric Smith?

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[Nick Thompson]

SS,

 

I was going to let this go, but now see that I can’:

 

.  I did hear Nick ask if a system could (somehow?) choose it's own boundaries and dismissed it as (yet another) distraction

 

I hate anthropomorphism in all its forms, so despite the plain meaning of these words, I did not mean this anthropomorphically.

 

My “high bit” as Owen used to say, is distinguishing between the thing we are explaining and the thing we are explaining it WITH.  If I have anything to contirubute to this conversation it is to make you all aware that you keep sliding back and forth between those two things. So when I asked SG if a system “get’s a voice” in whether it is a system or not, I was only asking if there was a “thing-with-properties” out there that we are curious about before we begin bring to bear all the heavy weaponry of complexity talk.  My candidate for the “thing that excites our curiosity” is multilevel systems, whether or not they involve organisms.  I promise I have NO INTENTION of asking a huriicane if it is a system, and I wouldn’t trust the answer if I got one.

 

Also, I don’t one understands what philosophy can do for science if you call it sophistry.  If you were happily painting the floor of a room and I pointed out that you had neglected to leave yourself a way out of the room, you wouldn’t call that sophistry, no matter how well the painting was going at the moment or how beautiful the painted floor looked.  That’s the role of philosophy in science.

 

Nick  

 

 

Nicholas S. Thompson

Emeritus Professor of Psychology and Biology

Clark University

http://home.earthlink.net/~nickthompson/naturaldesigns/

 

 

-----Original Message-----
From: Friam [mailto:[hidden email]] On Behalf Of Steven A Smith
Sent: Monday, May 29, 2017 12:02 PM
To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
Subject: Re: [FRIAM] Any non-biological complex systems?

 

Glen -

> I think the sophistry around the defn of "model" is important, but a

> distraction from this conversation.  (I've got a few publications that

> target it almost directly if anyone cares.)

Yes, it was an aside, but I think an important one to help Nick follow/focus with us.

>    As Russ and Nick point out, this conversation is about the boundary and its ontological status.  Russ is leaping a bit further ahead and focusing on an _effect_ of the boundary while Nick (and I) are focusing on the prerequisite for symbol machines.

I appreciate your stating it this way.  I did hear Nick ask if a system could (somehow?) choose it's own boundaries and dismissed it as (yet

another) distraction but would now like to hear more.   It felt like an

anthropomorphism to suggest a system could "choose" it's own boundaries, but I'm open to having that explored if anyone can/will.

 

Similarly, your and Stephen's sparring about boundaries (compartments in

a refrigerator?) and the distinction of systems/subsystems, etc.    was

not something I felt able to parse out completely, so I'm hoping your post here leads to more elaboration of that question.

 

> 

> My claim is that Stephen's 3 examples are _not_ systems, much less complex systems at all because they are idealized out of their context.  In order to be systems, they have to have some sort of objectively determined boundary (like a petri dish).  Any bounded gob of goo can be thought of as a system.  An agent, however, must be _closed_ under some operation.  Hence, all agents are systems.  But not all systems are agents.  Whether the agent's boundary is loopy, self-defining, or not is the subject of Rosen's work (from which Kauffman's is derivative).

> 

> Whether a symbol machine can be merely a system (with an objectively determined boundary) or must be an agent (with some form of closure) is an important question.

I'm waiting with 'bated breath, and trying to guess if this is directly relevant to Russ's comment/question about "living in two worlds" of symbol AND energy processing (if I understood his point correctly).

 

I wish I had more to contribute myself, perhaps I should study Rosen again, some more.

 

- Steve

> 

> 

> On 05/28/2017 08:40 PM, Nick Thompson wrote:

>> [NST==>Ok, but the question before us is, Does the system itself “get

>> to participate” in determining its own boundaries.  <==nst]

> On 05/28/2017 08:35 AM, Russ Abbott wrote:>

>> Symbolic processing, including computers, is a step beyond switches.

>> Half a century ago Newell and Simon defined computers as physical symbol machines.

>> We and many biological organisms are  physical symbol machines also.

>> I think that's an important way to look at it.

>> 

>> The thing about physical symbol machines is that the rules of

>> causation they follow are more complex than those of physics.

> 

 

 

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[gepr]

Ugh.  Sorry.  I often forget to use "other people's words" when I talk.  Sophistry is not a bad thing in my own private lexicon.  We are surrounded by sophismata (is that the right word?).  The disambiguation of the meanings of "model" is one such sophisma.  It is not resolvable, at least in the short term.  But every conversation about such disambiguation is fruitful and worthwhile.  It's just not the particular sophistry we need for this conversation.

On 05/29/2017 11:45 AM, Nick Thompson wrote:
> Also, I don’t one understands what philosophy can do for science if you call it sophistry.  If you were happily painting the floor of a room and I pointed out that you had neglected to leave yourself a way out of the room, you wouldn’t call that sophistry, no matter how well the painting was going at the moment or how beautiful the painted floor looked.  That’s the role of philosophy in science.

--
☣ glen

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[Nick Thompson]

Thanks, Steve.

 

Larding below

 

Nicholas S. Thompson

Emeritus Professor of Psychology and Biology

Clark University

http://home.earthlink.net/~nickthompson/naturaldesigns/

 

From: Friam [mailto:[hidden email]] On Behalf Of Stephen Guerin
Sent: Monday, May 29, 2017 2:30 PM
To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
Cc: Eric Smith <[hidden email]>
Subject: Re: [FRIAM] Any non-biological complex systems?

 

 

_______________________________________________________________________
[hidden email]

CEO, Simtable  http://www.simtable.com

1600 Lena St #D1, Santa Fe, NM 87505

office: [phone] mobile: [phone]

twitter: @simtable

 

On Mon, May 29, 2017 at 12:10 PM, Nick Thompson <[hidden email]> wrote:

SG,

 

There are now THREE issues lurking here between us.

 

IS THE CRITERION FOR A SYSTEM ARBITRARY: You say yes; I say no.  We’ve already covered that ground.

 

In my post, I said it is not arbitrary. It's a function of what the researcher is trying to use it for or explain.

[NST==>Well, that sounds like arbitrary to me.  But it’s a subtle point, and bordering on the edge of a word-bicker, so I won’t pursue it now.  Someday, I would like to do a thing on “subjective vs objective” some day, but today time is limited.  <==nst]

 

 IS A HURRICANE A SYSTEM:  For me, that is the question of whether the collection of thunderstorms we call a hurricane interact with one another more than they interact with their collective surroundings.  Another way to put this question is in terms of redundancy.  If we were to go about describing the movements of the thunderstorms of a hurricane, would we get a simpler, less redundant description if we referred their movements to the center of the hurricane.  I think the answer to this question is clearly YES.

 

Yes you could model the movement in a simpler way by modeling the movement of the center point. And that was my second model of a hurricane as a random walker biased by a global wind vector and Coriolis curve term. And I said that was not a complex system.

 

 

IS A HURRICANE COMPLEX?  For me, complexity means “multi-layered” .  So, a complex system is one composed of other systems.  A hurricane is a system of thunderstorms which themselves are a system of thermals (handwaving, here).  Thus a hurricane is at least a three-level system.  So, yes.  It is complex.

 

I agree about complex systems as having multiple layers - a macro scale and a micro scale. I would say there's one system. If I was trying to model a hurricane in my first example of an emergent vortex dissipating temperature and pressure gradients, I would model the air with a combination of air particles and patches of air - at LANL they would describe these as particle in a box models or hybrid lagrangian and eulerian models. I would not introduce thunderstorms at the micro level. But there's many ways to skin a hurricane :-)

 

Some would say the micro level air particles and air cell components which I would model as finite state machines (agents with a lower case "a") are systems in their own right and have boundaries. I don't see the benefit of calling them systems as their aren't multiple interacting components within them. But don't feel like arguing too hard here.

 

Eric Smith? 

 

 

Yes, where are you Eric Smith?

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[Stephen Guerin-5]

All Hurricanes are Dynamical System or Hurricanes and Dynamical Systems are Dynamical Systems ;-p

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

On Mon, May 29, 2017 at 12:49 PM, glen ☣ <[hidden email]> wrote:

Ugh.  Sorry.  I often forget to use "other people's words" when I talk.  Sophistry is not a bad thing in my own private lexicon.  We are surrounded by sophismata (is that the right word?).  The disambiguation of the meanings of "model" is one such sophisma.  It is not resolvable, at least in the short term.  But every conversation about such disambiguation is fruitful and worthwhile.  It's just not the particular sophistry we need for this conversation.

On 05/29/2017 11:45 AM, Nick Thompson wrote:
> Also, I don’t one understands what philosophy can do for science if you call it sophistry.  If you were happily painting the floor of a room and I pointed out that you had neglected to leave yourself a way out of the room, you wouldn’t call that sophistry, no matter how well the painting was going at the moment or how beautiful the painted floor looked.  That’s the role of philosophy in science.

--
☣ glen

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[Marcus G. Daniels]

Nick writes:

 

“In a similar way, hearing you guys argue, I wonder if much progress has been made on the question of what conditions make possible the spontaneous progressive layering of natural systems.”

 

You might look at the deep learning literature.  Starting from the Hubel and Wiesel, all the way to learning of optical flow.   There is surely more to it than just one directional layering, though.

 

Marcus

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[Barry MacKichan]

I’m not sure this qualifies, but it’s too pretty to pass up. They recently got a good look at Jupiter’s poles:

https://www.nasa.gov/press-release/a-whole-new-jupiter-first-science-results-from-nasa-s-juno-mission

--Barry

On 25 May 2017, at 22:08, Stephen Guerin wrote:

Are there any good examples of a complex system that doesn't involve biological organisms (including human beings)?

Three most used non-biological examples I've seen are:

• ferromagnetism (described with ising model)
  
• Bénard cells (convection)
• Belousov–Zhabotinsky reaction

Practically any physical system that transacts forms of energy can have critical regimes of phase transitions and would all qualify as complex systems.

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[Stephen Guerin-5]

Wow, cool!

You can play with a sphere at Los Alamos Bradbury Museum that has an inner rotating sphere to generate similar turbulent patterns similar to Taylor Couette instabilities in rotating cylinders:

  https://www.youtube.com/watch?v=BNiocOsgxW8

I suspect Jupiter has more of a magnetic interaction but concepts I suspect are similar.

Somewhat related is the unexpected reversibility of these systems as illustrated by these UNM professors:

  https://www.youtube.com/watch?v=p08_KlTKP50

On Mon, May 29, 2017 at 2:44 PM, Barry MacKichan <[hidden email]> wrote:

I’m not sure this qualifies, but it’s too pretty to pass up. They recently got a good look at Jupiter’s poles:

https://www.nasa.gov/press-release/a-whole-new-jupiter-first-science-results-from-nasa-s-juno-mission

--Barry

On 25 May 2017, at 22:08, Stephen Guerin wrote:

Are there any good examples of a complex system that doesn't involve biological organisms (including human beings)?

Three most used non-biological examples I've seen are:

• ferromagnetism (described with ising model)
  
• Bénard cells (convection)
• Belousov–Zhabotinsky reaction

Practically any physical system that transacts forms of energy can have critical regimes of phase transitions and would all qualify as complex systems.

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[gepr]

On 05/29/2017 12:01 PM, Marcus Daniels wrote:
> There is surely more to it than just one directional layering, though.

And there's more to it than the omnidirectional layering (like an onion), too.  I'm happy that y'all are using the word "layer" rather than "level" (despite that the unfortunate concepts of macro and micro crept in).  But even swapping out layers for levels isn't enough.  The idealistic conceptions of bottom-up vs. top-down causation have always seemed to miss the underlying point, which is the idea of a diversity of layering.  That a boundary can be closed to one type of thing and open to another begins to address the deeper issue.  Rosen cherry-picked and fixated on the one Aristotelian cause (agency).  But that's only one way to decompose the problem.  There are plenty of ways to either refine that (typology of agency) or abandon that and go with another categorization.

But the essence of it is right.  To go beyond the naive up/down or onion-based conceptions of layering, one has to _close_ off regions.  If you do that, then you can suggest things like region A is closed to X, region Y is closed to Y, and region Z is closed to Z and Y, but not to X.  You can even approach complicated composite boundaries where, say, an apical part of the boundary is closed to X and Y, but the basal part is closed to Y and open to X.

In this way, the slight relaxing of Russ' idea from agents to closable boundaries allows us to escape the limited (directional) layering.

--
☣ glen

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[Nick Thompson]

Now THAT’S a model. 

 

N

 

Nicholas S. Thompson

Emeritus Professor of Psychology and Biology

Clark University

http://home.earthlink.net/~nickthompson/naturaldesigns/

 

From: Friam [mailto:[hidden email]] On Behalf Of Stephen Guerin
Sent: Monday, May 29, 2017 5:38 PM
To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
Subject: Re: [FRIAM] Any non-biological complex systems?

 

Wow, cool!

 

You can play with a sphere at Los Alamos Bradbury Museum that has an inner rotating sphere to generate similar turbulent patterns similar to Taylor Couette instabilities in rotating cylinders:

  https://www.youtube.com/watch?v=BNiocOsgxW8

 

I suspect Jupiter has more of a magnetic interaction but concepts I suspect are similar.

 

Somewhat related is the unexpected reversibility of these systems as illustrated by these UNM professors:

  https://www.youtube.com/watch?v=p08_KlTKP50

_______________________________________________________________________
[hidden email]

CEO, Simtable  http://www.simtable.com

1600 Lena St #D1, Santa Fe, NM 87505

office: [phone] mobile: [phone]

twitter: @simtable

 

On Mon, May 29, 2017 at 2:44 PM, Barry MacKichan <[hidden email]> wrote:

I’m not sure this qualifies, but it’s too pretty to pass up. They recently got a good look at Jupiter’s poles:

https://www.nasa.gov/press-release/a-whole-new-jupiter-first-science-results-from-nasa-s-juno-mission

 

--Barry

 

On 25 May 2017, at 22:08, Stephen Guerin wrote:

On Wed, May 24, 2017 at 6:59 PM, Russ Abbott <[hidden email]> wrote:

Are there any good examples of a complex system that doesn't involve biological organisms (including human beings)?

 

Three most used non-biological examples I've seen are:

• ferromagnetism (described with ising model)
• Bénard cells (convection)
• Belousov–Zhabotinsky reaction

Practically any physical system that transacts forms of energy can have critical regimes of phase transitions and would all qualify as complex systems.

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Meets Fridays 9a-11:30 at cafe at St. John's College
to unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com
FRIAM-COMIC http://friam-comic.blogspot.com/ by Dr. Strangelove

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[David Eric Smith]

Hi Steve and Nick,

Sorry to have dropped off.  I tried to read the very vigorous thread, to the extent I could, as it went by.  There is a lot there that seems to remain in the core of one thing that brings this crowd and several others together, and is conceptually far from finished business.  I can’t aim for Nick’s precision, or Steve’s coverage, unless there is some particular thing to solve, so my way of doing these things tends to be more limited than the main thread was.

On Russ’s question, I tried to give a lecture in an informal summer school a couple of years ago, to propose what sequence of changes in physical architecture would justify bringing in each of a series of new concepts.  I don’t have worked examples behind any of these cases (for a couple of them there are toy-model ideas), so this is the kind of work that is probably of little value and even less trustworthiness.  I don’t remember exactly the layout, but I think the sequence contained something like this (ALLCAPS are meant to be informal descriptors for concept keywords):

1. Protected degrees of freedom are a precondition to even the possibility of MEMORY.  If you are a mere physical degree of freedom, and you are always coupled to your environment, you are nothing different than an instant-by-instant reflection of the immediate local state of your environment.  All of the later concepts in the list require various forms of internal state that have enough insulation to be protected from constant harassment.  So where in the physical world are suitably decoupled degrees of freedom available to be found?  (Much later, to be built, but not yet.)

2. Some kind of dynamical variables need to be capable of being couplers that can become DOORWAYS, so that the other DOF are sometimes coupled and sometimes not.  A DOF that is always behind a wall (a chemical reaction behind such a high energy barrier that it is never achieved) can’t remember anything because, although it can certianly hold a state, it is never in contact with the environment that would imprint anything on that state.  This doesn’t yet talk about how the open/close states of the doorway happen, which will determine when and what it allows the environment to imprint on the memory variable, and for how long that imprint can be held.  Here one can be quite precese with examples without invoking biology.  Organic chemistry at low energy in water is largely non-active.  Metal centers, particular d-block elements, are the major doorways that govern the sectors of organic chemistry available to early ocean-rock worlds.  Many enzymes still use them in something not too far from a mineral or soluble metal-ligand complex state, with a little tuning.  In this case, the doorway works just through physical drift.  Molecules free in solution are inert; those that bump into a metal can potentially become active; when they dissolve and drift on, they become inert again.  This leads to a very different set of relations between thermal energy and information in reactions, than simple thermally-activated reactions among the same species.  Probably one can invoke many other examples.  

3. Some of the internal variables need to be capable of carrying on an AUTONOMOUS dynamics or internal process.  I guess a memory variable can sit there passively and still, at some level, categorize the way a system (set of DOF) responds to an environmental event, but for most of the later levels, there needs to be actual internal dynamics.  This in itself is not so hard; the world is far from equilibrium in any number of dimensions, and for something to be moving in a direction is not rare.

4. Internal dynamics can be autonomous, but it isn’t really “about” anything unless something about the configuration constitutes a MODEL in the sense of Conant and Ashby from old 1950s control theory.  How the model is registered, and how reflexive or self-referential the internal dynamics needs to be for a meaningful model to be imprinted, probably ramify to many differenent questions.  I would of course be happy to produce an interesting case of the emergence of any of them.

5. At some stage, a protected internal process of which the state of the model is part needs to act back on the doorway, if we are to be justified in saying the basic relation of a CONTROL SYSTEM has come into existence.  Here again I intend a Conant and Ashby line of thought: that “Every good controller “contains? entails?” a model of the system controlled.  There has to be some internal state that is capable of being in different relations to the state of the world, and then the internal dynamics has to take an input from a comparison of those two states.  Only if the resulting action feeds back on the state, does the system start controlling its own interaction with the world (for instance, what gets remembered).

6. The next one is hard for me to say, even at the very low standards of the previous five:  I can be a control system with a model of my world, even if I have only modest machinery.  A membrane-bound protein that lets in some molecules and ignores others, and which is preserved in a population through some kind of filtering, is a perfectly good control-theoretic model in the C&A sense.  But it only implicitly models its environment.  I have not yet added the assumption that there is some kind of REFLEXIVITY or REFLECTION (in the sense of Quines) so that the model includes representations of possible counterfactual states of the internal variables themselves.  If there is a physical process that drives a system’s parts into a configuration where that happens, then one of the things an internal process _could_ do is use the modeled futures to internally select among many responses to a situation of which it is capable.  Only at that stage would I feel compelled to introduce a concept of AGENCY, where for my practical purposes, I am happy to use the word as game theorists use it.  An agent is a kind of thing that fills one of the slots that games have for “players”, which must be provided for the mechanics of the game to execute, and where the agents have some way to convert specification of the game into a sequence of moves that are not individually dictated by the game itself.  I am sure there are lots of other notions of agency (ABM has a much more permissive notion, which can be as little as a dynamical Monte Carlo, or can be full-blown game-theoretic player), but for the purpose of trying to draw levels from the foregoing, this one seemed enough to me to propose a concrete problem.

I am sure there are more, but I think I stopped there, and this was about as far as Russ was asking, too, I think.

So the challenge (speaking only for myself, of course) is to find places in matter where the structure of the dynamics as one starts with it, drives the activity into regions of material architecture that take on first one, and then another, of the above new patterns.  I assume they have to occur in more or less this order, because it is hard for me to see how to build the later concepts without having the earlier ones as building blocks.  I like chemistry as a medium, because the state space itself supports a lot of complexity, and the temporal variability of reactions, plus the fact that catalytic relations exist, offer large separations of timescales that can be used to fill functional classes like memories.  Whether it becomes hard to build much hierarchy in any system that doesn’t benefit from the intensive way chemistry makes complexity easy, is a question I find interesting.  I don’t know how one answers it with better than musing.

This is all kind of armchair statements of the obvious, and I don’t mean to make it out as more.  I know there are people like Rosen who made long careers of trying to tease all this out at length, and have written a lot on it.  Maybe they include all this obvious stuff and also much more.


But branching, to Nick’s point about the extent to which “a system” “chooses” something about the relevant delimitations of itself.  I think this becomes an operationalizable question in the spirit of Leslie Valiant’s PAC learnability framework.  (Probably Approximately Correct).  Valiant’s wish was to show that the learnable tasks, like the computable functions, make a formally definable class.  I don’t think that discussion is anywhere near being closed one way or another, but the attempt to systematize what can be learned, how hard it is, and how much either of those depends on the embedding context, seems very helpful and clarifying to me.

The connection would be this:  Suppose you have some internal state, and some internal dynamics, and the state under the influence of the dynamics — or even intervals within the dynamics under the influence of their longer trajectory — can pass through many different patterns.  Suppose that somewhere there is a reinforcement learner working on those patterns in some systematic way.  It could be an environment selectively filtering many copies of you with slight variations, or it could be some other subroutine within your internal dynamics.  The kind of thing I have in mind is: suppose there is a synthetic organic chemistry generating small molecules, lots of copies of some of them, fewer copies of others, and as a by-product of that molecular pool, something like polymers large enough to be capable of function, but happening to have functions only at random, are one of the things that can arise.  Out of all this mess, focus on the PAC-learnability problem of evolving an enzyme.  The things that should determine whether a given selective protocol can find and then fix something should be:
1. how frequently is that substrate even encountered?  If not often, it is hard to maintain any memory about it.  It is easy for farmers to remember to water the crops during dry spring winds, because that happens every year.  It is harder for a culture to remember to run uphill when the tide goes way out for a very long time, since maybe that hasn happened where they live in the past 600 years.
2. how consequential is the particular molecule.  If very consequential, selection can be more severe, and leave a stronger signal, which maybe can be remembered a little longer.  
There is probably lots of other fine structure to learnability, such as whether the environment is effectively serving as a “teacher” with respect to that particular problem (Valiant’s term, used to illustrate concrete cases), but I won’t ramble more than this.

How does that relate to Nick’s point; one more indirection on the way to getting there:
Steve mentioned (in some thread, a few weeks ago) the concept of Order Parameter, which is a kind of predictive statistic that suddenly starts to have a lot more predictive content, and to be more stable, when a system goes into an ordered phase.  If you are going to try to use reinforcement learning to select higher-order structure on some low-order patterns that you are already producing, the order parameters are the things that take the most regular values, and they most robustly support induction, which is what all reinfocement learning is.  (A finite system cannot help but induct: in a world of potentially unlimited variability, it has only finitely many possible states, so perforce it will make infinite equivalence classes of environmental states, by responding to many situations that in detail are different, with the same response.  That doesn’t mean that “the problem of induction” “has” any solution.  It only means that every finite system is perforce a commitment to some inductive hypothesis.)  

So I would argue that, with respect to the accumulation of hierarchy, there is a natural sense of a system’s own delimitation, to the extent that the parts that are sufficiently stable and sufficiently consequential to build something on top of by reinforcement become the foundation that holds other parts together.  I agree with the purpose underlying Steve G’s point: that this can depend in part on what kind of environment there is, since this is part of the learning protocol.  But we also all recognize that — at least insofar as the statistical concepts found useful in equilibrium thermo and fundamental processes continue to be useful in more elaborate dynamical realms — the Order Parameter as a Minimum Sufficient Statistic for distributions over future states is an informationally special quantity.

Sorry for long harangue, and I don’t know whether this has anything new in it that the list hasn’t revisited many times.

All best,

Eric

> On May 29, 2017, at 8:29 PM, Stephen Guerin <[hidden email]> wrote:
>
>
>
> _______________________________________________________________________
> [hidden email]
> CEO, Simtable  http://www.simtable.com
> 1600 Lena St #D1, Santa Fe, NM 87505
> office: [phone] mobile: [phone]
> twitter: @simtable
>
> On Mon, May 29, 2017 at 12:10 PM, Nick Thompson <[hidden email]> wrote:
> SG,
>
>  
>
> There are now THREE issues lurking here between us.
>
>  
>
> IS THE CRITERION FOR A SYSTEM ARBITRARY: You say yes; I say no.  We’ve already covered that ground.
>
>
> In my post, I said it is not arbitrary. It's a function of what the researcher is trying to use it for or explain.
>  
>
>  
>
> IS A HURRICANE A SYSTEM:  For me, that is the question of whether the collection of thunderstorms we call a hurricane interact with one another more than they interact with their collective surroundings.  Another way to put this question is in terms of redundancy.  If we were to go about describing the movements of the thunderstorms of a hurricane, would we get a simpler, less redundant description if we referred their movements to the center of the hurricane.  I think the answer to this question is clearly YES.
>
>
> Yes you could model the movement in a simpler way by modeling the movement of the center point. And that was my second model of a hurricane as a random walker biased by a global wind vector and Coriolis curve term. And I said that was not a complex system.
>  
>
>  
>
> IS A HURRICANE COMPLEX?  For me, complexity means “multi-layered” .  So, a complex system is one composed of other systems.  A hurricane is a system of thunderstorms which themselves are a system of thermals (handwaving, here).  Thus a hurricane is at least a three-level system.  So, yes.  It is complex.
>
>
> I agree about complex systems as having multiple layers - a macro scale and a micro scale. I would say there's one system. If I was trying to model a hurricane in my first example of an emergent vortex dissipating temperature and pressure gradients, I would model the air with a combination of air particles and patches of air - at LANL they would describe these as particle in a box models or hybrid lagrangian and eulerian models. I would not introduce thunderstorms at the micro level. But there's many ways to skin a hurricane :-)
>
> Some would say the micro level air particles and air cell components which I would model as finite state machines (agents with a lower case "a") are systems in their own right and have boundaries. I don't see the benefit of calling them systems as their aren't multiple interacting components within them. But don't feel like arguing too hard here.
>  
>
> Eric Smith?
>
>
>
>
> Yes, where are you Eric Smith?

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[Marcus G. Daniels]

Eric writes:

"I like chemistry as a medium, because the state space itself supports a lot of complexity, and the temporal variability of reactions, plus the fact that catalytic relations exist, offer large separations of timescales that can be used to fill functional classes like memories."  

One could imagine coupling a physical simulation to a search procedure for functional behaviors like memories and doorways.   The detection combinatorics would be challenging, assuming the physical simulation were possible at sufficient fidelity, but perhaps could be constrained by virtue of spatial locality.  

I don't know much about coarse-graining organic chemistry simulators.  For comparison, with molecular dynamics a billion atoms is possible (on a budget of a few megawatts), but not for more than tens of nanoseconds.   I've found game physics engines like Bullet Physics are nice for coarse-grained models because they are fast (optimized to graphics processors) and easy to interleave control or detection logic.  However, they couldn't (without more work) decompose the space across memory domains of a cluster.

Marcus


 
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[Owen Densmore]

Nick: would this thread, and past similar ones, be a good start for your Friam Discussions project? I can help you if you need it.

   -- Owen

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[David Eric Smith]

Thanks for this Marcus,

> One could imagine coupling a physical simulation to a search procedure for functional behaviors like memories and doorways.   The detection combinatorics would be challenging, assuming the physical simulation were possible at sufficient fidelity, but perhaps could be constrained by virtue of spatial locality.  

Yes.  I can’t bring to mind anybody who seems to be doing important work in this entirely within simulation, but Leroy Cronin in Glasgow is trying to combine highly parallel robot-maintained reaction vessels with pattern-matching computation and feedback, so see if he can search for properties and then extract chemical mixtures that will instantiate them.
http://www.chem.gla.ac.uk/cronin/
Lee is a handful, and his group is the size of a small town (larger than some small towns in NM, I suspect), so one gets the sense of massive seiving for most-anything, with the hope that some fraction of that will remain of interest for longer than the time Lee is promoting it.  The project is really different, though, from anything I have seen before.  It begs to be integrated with modern AI, which has become quite flexible in what you are allowed to call salient, so one can search in very open-ended ways.

> I don't know much about coarse-graining organic chemistry simulators.  For comparison, with molecular dynamics a billion atoms is possible (on a budget of a few megawatts), but not for more than tens of nanoseconds.   I've found game physics engines like Bullet Physics are nice for coarse-grained models because they are fast (optimized to graphics processors) and easy to interleave control or detection logic.  However, they couldn't (without more work) decompose the space across memory domains of a cluster.

Interesting.  I don’t know much about molecular-dynamics simulations, which is deeply an expert’s game, though I guess most major universities have somebody in chemistry or biochem who specializes in it.  If one is willing to go one level out, and ask which questions are hard at the level of network synthesis and search, taking reaction primitives as input data, the graph-grammar methods are becoming pretty sophisticated.  The best I know of is the current state of the project that started with Peter Stadler but is now dispersed across German-speaking Europe and Scandinavia:
http://cheminf.imada.sdu.dk/mod/
Much of what makes this a hard and interesting computational project doesn’t show if one merely wants to do chemistry.  It comes up because they are trying to create a consistent representational system.  This creates difficulties like deciding when two things are the same molecule; when two molecules arrived at through different pathways are actually isomorphisms of the same label set, etc.  In random network-extension algorithms, this entails solving the graph-isomorphism problem a very very large number of times, and the underlying representational system must be provably well-defined.  It is in coming up with representations that are more well-defined than SMILES or INCHI, and implementing most-modern isomorphism searches, that these guys are the furthest along.  There is also a playground linked from their main page, though I am told they recognize their documentation may be a bit off-putting to people not used to wading into new systems.  

The current state of the graph-grammar project is several-fold:
1. Bond topology is present, and has been for some time.
2. Simple stereochemistry of carbon is now implemented, and less-simple stereochemistry that requires non-local propagation through a molecule to determine equivalence of representations is next to come.
3. Stereochemistry of metals, which will be the gateway to crucial mechanisms of metal catalysis, is planned.
4. There is a project, privately held, to port the entire Beilstein database of reaction mechanisms to graph-grammar representations, after which engines like this become an incredible tool.  RIght now the bottleneck is usually manual coding of the mechanisms of interest.  
5. There has been some discussion of raising pattern-matching above the level of atoms or local clusters, to inductively-defined patterns like crystal faces, but no serious attempt to formulate that problem yet.

Even with the limited state of what they can do, they have achieved some tolerable comparisons against messy chemical systems, like the formaldehyde-addition network known as the “formose network”, and the HCN polymerization and hydrolysis system.  Both are famously complicated, and both have long-standing interest to Origin of Life people, though their exact situation relative to planetary chemistry is easy to argue about.

All best,

Eric



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