Emergent-Constraint-Theory.txt

Emergent Constraint Theory


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By Jeremy Sherman, Ph.D. js@jeremysherman.com, author, Neither Ghost Nor Machine: The emergence and nature of selves (Columbia University Press, 2017)

Every engineering advance of the past 400 years is the product of a brilliant breakthrough modeling method. Not the scientific method’s guess and test process – no, something more practical. Call it the engineering method – modeling all behavior as networks of switches and lines. 

Think of it like train tracks. You’ve got your lines and your switch boxes. The lines channel energy down narrow, reliable tracks. The switch boxes can be toggles or dials. Either the energy controls the switches and dials, or someone outside the model does like a lineman.

With the engineering method, you start with some phenomenon you want to model. You configure a network of lines and switches to represent the phenomenon from start to finish. You tweak the network until the model generates the phenomenon accurately and voila, you’ve got yourself a model.

From steam engines and jacquard looms centuries ago to computing and robotics today, from plumbing to nanotech, from genetics, math, and medicine to business management, logic, and civil engineering – it all relies on the engineering method’s line-and-switch modeling. It has been our technology cornucopia and we’re not half done fulfilling the engineering method’s promise. It’s easy to assume there’s nothing it can’t model.

And it follows that you’re a robot, a machine or computer composed of billions of switches sending energy down narrow reliable lines to produce your behaviors. 

Trouble is, you try and robots don’t. You’ve got your goals and emotions that machines don’t have but it’s deeper than that. Even plants try to stay alive. They don’t think or feel but they still have will power – the will to live. They struggle for their own existence.

Now there are plenty of researchers who will tell you that you’re no different from a machine. They talk all techy but at heart, they’re romantic animists, the kind of people who might love a pet rock.

No, engineering a robotic model of living behavior isn’t the same as explaining living behavior. Obviously. Science and engineering are different disciplines with different methods and goals. Engineering is about modeling; science is about explaining.
 
Today, almost all science and philosophy are practiced as though they’re engineering. That’s understandable given how brilliant and effective the engineering method is. Still, for explaining living and trying, the engineering model just doesn’t cut it. It doesn’t compute and neither do you.

In the next video I’ll point you to a new, strictly scientific, non-engineering theory that explains your trying from its origins in chemical mechanics, starting not with consciousness or feelings but with what you, plants and all living beings have in common, your will to live, all the unconscious, unfelt stuff you do 24/7 to try to repair, protect and regenerate yourself so you don’t fall apart. Emergent constraint theory explains you in a way that no engineering method model ever could.

ChatGPT
Jeremy Sherman, Ph.D., in his work "Neither Ghost Nor Machine: The emergence and nature of selves" (Columbia University Press, 2017), provides a thought-provoking perspective on the limitations of what he terms the "engineering method" in fully capturing the essence of living behavior. Sherman contrasts this method with the scientific approach, pointing out that while engineering focuses on modeling phenomena through networks of switches and lines—akin to train tracks with their lines and switch boxes—the complexity of living beings surpasses this mechanical simplification.

The engineering method, as Sherman describes, has undeniably fueled technological advances across various domains, from steam engines to computing, and from genetics to civil engineering. By modeling phenomena with lines and switches, engineers can replicate processes and functions in a controlled and predictable manner. This method conceptualizes behaviors and systems as networks of energy flow, regulated by switches that can be either controlled by the system itself or an external operator.

However, Sherman highlights a fundamental distinction between the engineered models and living organisms: the aspect of trying or the will to live. He argues that even the simplest life forms exhibit a drive for survival—a characteristic that purely mechanical models cannot account for. This drive encompasses goals, emotions, and an inherent will to maintain existence, aspects that are absent in inanimate machines.

The critique extends to the broader application of engineering principles in fields traditionally grounded in scientific inquiry, suggesting a prevailing trend of modeling complex living behaviors with methodologies that might not fully capture their essence. According to Sherman, this approach overlooks the unique aspects of living beings that cannot be simply modeled through switches and lines.

Sherman introduces the concept of "emergent constraint theory" as an alternative, strictly scientific theory that seeks to explain living behavior from its chemical mechanical origins. This theory proposes to bridge the gap left by the engineering method, focusing on the foundational drive shared by all living beings—the unconscious, unfelt efforts aimed at self-repair, protection, and regeneration.

This perspective challenges the current paradigms in science and philosophy, urging a reevaluation of how living behavior is conceptualized and explained. Sherman's argument stimulates a conversation on the intersection of engineering, science, and the philosophy of life, inviting further exploration into how we understand the very nature of existence and the essence of living beings.

User
every engineering advance the past 400

years is the product of a brilliant

breakthrough modeling method not the

scientific methods guess and test

process no something more practical call

it the engineering method modeling all

behavior as networks of switches and

lines think of it like train tracks

you've got your lines and your switch

boxes the lines channel energy down

narrow reliable tracks the switch boxes

can be toggles or dials either the

energy controls the switches and dials

or someone outside the model does like a

line man with the engineering method you

start with some phenomena you want a

model you configure a network of lines

and switches to represent the phenomena

from start to finish you tweak the

network until the model generates a

phenomenon accurately and voila you've

got yourself a model from steam engine

such a card looms centuries ago to

computing and robotics today from

plumbing to nanotech from genetics math

and medicine to business management

logic and civil engineering it all

relies on the engineering methods line

and switch modeling it has been our

technology cornucopia and were not half

done fulfilling the engineering methods

promise it's easy to assume there's

nothing it can't model and it follows

that you're a robot a machine or

computer composed of billions of

switches sending energy down narrow

reliable lines to produce your behaviors

trouble is you try and machines don't

you've got your goals and emotions that

machines don't have but it's deeper than

that even plants try to stay alive they

don't think or feel but they still have

willpower the will to live they struggle

for their own existence now there are

plenty of researchers who will tell you

you're no different from a machine they

talk all techie but at heart their

romantic animus the kind of people who

might love a pet rock

no engineering a robotic model of living

behavior isn't the same as explaining

living behavior obviously science and

engineering are different disciplines

with different methods and goals

engineering is about modeling science is

about explaining today almost all

science and philosophy are practiced as

though they were engineering that's

understandable given how brilliant and

effective the engineering method is

still for explaining living and trying

the engineering model just doesn't cut

it it doesn't compute and neither do you

in the next video I'll point you to a

new strictly scientific non engineering

theory that explains you're trying from

its origin in chemical mechanics

starting not with consciousness or

feelings but with what you plants and

all living beings have in common your

will to live all the unconscious unfelt

stuff you do 24/7 to try to repair

protect and regenerate yourself so you

don't fall apart

ChatGPT
It seems you've shared a detailed summary or excerpt that mirrors the essence of Jeremy Sherman's arguments regarding the limitations of the "engineering method" in explaining the nature of living behavior, as highlighted in his discussions and writings. Sherman draws a distinction between the engineering approach of modeling phenomena through networks of switches and lines and the complexity of living organisms that exhibit a will to live, a characteristic that goes beyond mechanical constructs.

The narrative you've shared encapsulates Sherman's critique of the prevalent reliance on engineering methodologies to understand and replicate living behaviors. This reliance, while fruitful in producing technological advancements across various fields (from computing and robotics to genetics and civil engineering), falls short in capturing the intrinsic aspects of life—such as the will to live, goals, and emotions. Sherman suggests that these elements of living beings cannot be adequately modeled or explained by the engineering method, which fundamentally sees behavior as a network of energy transfers and binary decisions.

He introduces the concept of "emergent constraint theory" as a promising scientific, non-engineering approach to understanding living beings. This theory aims to explain the 'trying' characteristic of life, starting from chemical mechanics and not presupposing consciousness or feelings. It focuses on the unconscious efforts all living beings make to maintain their existence, highlighting a shared drive among all life forms to repair, protect, and regenerate themselves.

Sherman's critique and propositions challenge the current methodologies in science and philosophy, advocating for a deeper inquiry into the nature of life that transcends the mechanical analogies provided by engineering. His work encourages a reevaluation of how living behaviors are conceptualized, modeled, and explained, proposing a shift towards theories that can encompass the complex, non-mechanical aspects of life.

User
your true core self or your essence your

vital force your will drive soul spirit

agency the thing you've got that leaves

your body at death what is that thing to

simplify and get to the bottom of it

I'll call all of us living being selves

a trees of self bacterium's of self an

extraterrestrial if we ever find one as

a self weird yeah go with it and what do

all selves do we try above all we try to

survive and reproduce or us there

wouldn't be selves here three and a half

billion years after the origins of life

selves try non selves don't

we ain't give selves and trying all

sorts of names but naming doesn't

explain anything call it a soul if your

spirits will call it agency if your

scientific I don't care what we call it

we still got to explain it how to

explain selves and trying what makes a

self a self what makes us try when other

things don't saying what it does doesn't

explain it either the self tries the

soul cares the will motivates whatever

that doesn't explain it any more than

saying a stove heats explains how it

does it and saying where it happens

isn't explaining it either consciousness

is in the brain soul is in the heart

appetite is in the stomach motivation is

in the meat go ahead put your finger on

consciousness soul appetite motivation

no one ever has

sure selves happen in biochemistry but

biochemistry isn't the self the

biochemistry is all still there after a

self dies so what the heck is a self we

can settle for it's a mystery or make up

some magical metaphorical fiction about

a higher power breathing soul into meat

or natural selection trying to design us

it doesn't metaphors are fine for

everyday but won't do for science

Harvard Berkeley neuroscientists and bio

anthropologist Terry Deakin has a wildly

original yet thoroughly scientific new

angle on what selves in trying our and

how they could get going in a universe

that's not trying to do anything

I've worked with Terry for 20 years and

here I'm going to give you the short

version of his theory to chew on

we've been going at the mystery of

selves and trying all wrong we've been

focused on finding that special thing

that makes elves do what they do we

should be focused instead on house elves

prevent themself from doing other things

to keep going you prevent your own

failure to keep going to stay alive

selves prevent death here we'll start by

honing in on minimal trying identifying

the work that all selves have to do to

keep from dying then we'll look for how

we do it by keeping ourselves from doing

other things then we'll look for house

elves and trying could emerge from basic

chemistry three steps identify what

every self has to do to keep living

explain how they prevent their own dying

explain how that starts from basic

chemistry at the origin of life okay

first step identify what every self must

do

everything in the universe breaks down

over time

that's about as fundamental of science

gets given time any sorted ordered

regularize thing gets messed up or mixed

up you see it everywhere you fix things

they break down you wind things up they

wind down you sort things out they get 
mixed up  
things fall apart

ChatGPT
The passage you shared delves into the profound and often philosophical question of what constitutes the essence or core of a living being—what many might refer to as the soul, spirit, agency, or self. This essence is characterized by a will or drive to survive and reproduce, which distinguishes living beings (or "selves") from non-living entities. It's this essence that engages in the act of trying, which is fundamental to life itself.

The text argues that merely naming this vital force (calling it a soul, spirit, agency, etc.) does not provide an explanation for its existence or its mechanisms. It criticizes the traditional approaches to understanding this phenomenon, which often resort to metaphysical explanations or locate the essence of self within specific bodily organs without explaining the processes behind it. The author points out that even after death, when the "self" supposedly leaves the body, the biochemistry remains, posing the question: What really is the self?

To address this, the passage introduces the work of Terrence Deakin, a neuroscientist and bioanthropologist, who proposes a novel scientific perspective on the nature of selves and their intrinsic tendency to try or strive. Deakin suggests shifting the focus from what special thing makes a self act to how selves prevent themselves from doing other things, essentially preventing their own demise.

The theory begins with the recognition that everything in the universe is subject to entropy and decay. From this standpoint, the essence of living beings is defined by their continuous effort to prevent their own disintegration. The text outlines a three-step approach to understanding this concept:

Identifying the universal task of all selves: Every living entity must work against the natural tendency of things to fall apart, maintaining some form of order or organization that allows it to continue existing.

Explaining how selves prevent their own demise: This involves understanding the mechanisms through which living beings maintain their integrity and resist the entropy that affects all physical systems.

Tracing the emergence of selves from basic chemistry: The final step is to explain how the capacity for such self-preservation could have originated from the chemical processes that characterized the early stages of life on Earth.

This approach emphasizes the importance of understanding life and consciousness not as something imbued by an external force or arising from a specific component within the body, but as emerging from the fundamental chemical and physical processes that sustain life. It's an attempt to demystify the essence of living beings by grounding it in scientific principles, specifically through the lens of preventing decay and maintaining order amidst the natural tendency toward disorder.

User
you've got order and then you've got

messed up with time happens

that's the second law of thermodynamics

selves somehow work against it trying to

keep it together when nonliving things

don't

selves regenerate in the face of second

law should happens to generation it's

not that we're tough just look how fast

we to generate when selfhood ends at

death rocks to generate slowly but they

don't try to prevent their own

degeneration they just happen to be

durable we selves do ongoing work to

prevent our own degeneration an ability

we pass on to offspring so a first

distinction there's stuff that just

exists as long as it does and then

you've got selves things that try to

persist there's passive existence and

their self regenerative existence big

difference we're going to explain here

to self regenerate here are the three

kinds of works elves must try to do self

repair regenerating what degenerates

fixing healing repairing ourselves self

protection regenerating a boundary for

example cell membranes bark your skin

protecting us from external forces that

would degenerate us without containment

we degenerate by spilling out self

reproduction regenerating offspring that

also self protect and self repair done

with step one selves self regenerate by

self repair self propelled us out for

itself

fit to its circumstances on to step two

where we flip the question asking how

cells prevent themselves from doing

other things instead that's a bit of a

brain twister at first but you'll get it

after all you do it

nothing we selves do ever defies the

laws of nature everything about us is

strictly chemical cause-and-effect what

that explains why we act so differently

from chemistry chemical reactions

degenerate but cells don't cells have

carried on for eons the answer

overlooked for millennia is simple

sure or chemical cause-and-effect but

not just any in chemistry anything goes

within natural law we selves limit

chemistry's anything goes down to what

keeps us going instead of looking for

what's added to chemistry to make us

regenerate look for how we prevent our

degeneration living is death prevention

a constraint on what happens you know

all about preventing the useless to do

the useful to do what you want you

prevent yourself from doing what you

don't want all effort is like that our

limbs have a wide range of motion

our focused effort is a matter of

limiting that range of motion reducing

the possible to the useful

ChatGPT
The passage delves deeper into the intriguing interplay between living beings and the second law of thermodynamics, which posits that disorder, or entropy, in a system tends to increase over time. This law fundamentally characterizes the natural tendency of ordered systems to become more disordered, a process observable in both physical and biological contexts. The unique defiance of this law by living beings—through processes of self-regeneration, self-repair, self-protection, and self-reproduction—highlights a fundamental distinction between inanimate objects and selves, or living entities.

Living entities actively work against the natural drift towards disorder by engaging in continuous self-maintenance. This maintenance includes repairing damaged parts, protecting themselves from external threats that could accelerate their degeneration, and reproducing, thereby passing on the capacity for self-maintenance to future generations. This ongoing effort to counteract entropy and maintain order is not just a physical process but a defining characteristic of life itself.

The passage introduces a critical perspective shift by suggesting that the essence of living behavior is not about what additional elements life adds to chemistry but about how life imposes constraints on chemical processes. Instead of asking what living beings do to regenerate, the focus shifts to understanding how they prevent processes that would lead to their degeneration. This prevention is not a defiance of natural laws but a selective limitation of the range of chemical reactions that can occur. In essence, life is viewed as a form of death prevention, actively narrowing the possibilities of chemical interactions to those that support its continuation and functionality.

This concept extends beyond the biochemical level to the behavioral, illustrating that all efforts made by living beings, from the molecular to the organismal level, involve a reduction of possible actions to those most beneficial for survival and well-being. The capacity to limit oneself, to choose certain paths over others, is framed not just as a physical or chemical constraint but as a fundamental characteristic of life.

By framing life in terms of its ability to impose constraints on the natural tendencies towards disorder and degeneration, this perspective offers a fresh lens through which to understand the nature of existence. It suggests that the essence of life lies not in the mere presence of complex chemical reactions, but in the orchestrated limitation of those reactions to sustain a coherent, self-maintaining system. This approach provides a basis for exploring the origins and maintenance of life, emphasizing the role of selective limitation and constraint in the emergence and continuation of living systems.

User
likewise we could say anything but we reduced the

vast possible down to what we're trying

to say

reducing the possible down to the

youthful what's our core focus we do

work for a chance to keep working

another day you work for food energy so

you can do focus work to repair what

breaks down and to keep a roof over your

head for protection and maybe to pass on

those abilities to offspring

apply that to all selves and you've got

self repair self propelled oohs

dithering okay done with step to

explaining how selves try to stay alive

all self prevent their own degeneration

in ways that maintain their ability to

prevent their own degeneration that's

what it means to be alive it's not

something added the chemistry it's self

regenerative self constraint the failure

of self regenerative self constraint is

death the chemistry is all still there

even still organized for a while before

degenerating but not for long since

there's no longer any self constraint

regenerating it on to step three

life had to start like that to all of

its chemically possible reduced somehow

to the useful reduced by evolution no

evolution doesn't explain selves just

how they're honed obviously evolutionary

theory is well established yet the

origin of life has remained a mystery

by definition selves had to start by

accident trying emerged by happenstance

not by trying you can't say trying

started when something tried to make it

happen

selves emerge when somewhere all of the

chemically possible reactions happen to

get constrained or reduced down to

reactions that prevented their own

degeneration 

ChatGPT
The passage you've shared continues to explore the concept of life as a process of selectively reducing the vast array of possible chemical reactions to those that specifically contribute to the maintenance and continuation of living systems. This perspective emphasizes the role of self-repair, self-protection, and the perpetuation of these capabilities to future generations as central to the definition of life.

Living beings, through their actions and choices, constantly work to narrow down potential behaviors and reactions to those that are most beneficial for their survival and well-being. This process is akin to the way we communicate: out of all possible things we could say, we choose to express only those thoughts that convey our intended message. Similarly, life can be viewed as a continuous effort to focus energy and resources on activities that repair, protect, and reproduce—fundamentally, to prevent degeneration.

The concept of "self-regenerative self-constraint" introduced in the passage highlights a critical aspect of living beings: the ability to impose restrictions on their own chemical processes to favor those that sustain life. This ability is not an additive property but an inherent characteristic of life, differentiating living matter from nonliving. When this self-regenerative constraint ceases, as in death, the underlying chemistry may persist for a time, but without the organizing principle of life to maintain it, it will eventually succumb to entropy and disorder.

Step three of the explanation seeks to address the origins of life, suggesting that life began as a serendipitous event where certain chemical reactions became restricted in a way that allowed them to resist degeneration. This initial step towards life was not the result of an intentional effort but a fortuitous narrowing of chemical possibilities to those conducive to self-maintenance.

The passage also touches upon evolution, noting that while it is a powerful mechanism for honing the capabilities of living beings, it does not explain the origin of life itself. The emergence of self-regenerative systems—or "selves"—is posited to have originated from a chance reduction of the chemically possible to the biologically useful, marking the transition from nonlife to life. This perspective suggests that the capacity for life to emerge lies in the inherent properties of chemical systems under certain conditions to spontaneously organize in ways that resist the universal tendency toward disorder.

This explanation frames the origin of life not as a goal-directed process but as an emergent property of physical and chemical laws, a phenomenon that arose from the complex interplay of chance and the intrinsic ability of certain molecular systems to constrain themselves in a self-sustaining manner.

User
so how does constraint

happen how does something that was

possible become unlikely thereby making

other possibilities more likely we'll

start with a familiar kind of constraint

when we think about constraint we

usually imagine hard imposed barriers a

river bank that prevents water from

spilling just anywhere a hallways walls

that keep you walking in two directions

only plastic insulation that keeps

electrons running down wires guardrails

that keep us from going off the road an

engine cylinder that prevents gasoline

explosions from expanding in all

directions engineers impose constraints

they're Wranglers corralling energy

constraining or channeling it into

useful work we want but selves aren't

engineered there's also emergent

constraint it comes in two flavors one

called self-organization was discovered

about seventy years ago and has been a

hot topic in science ever since

self-organization is a lousy name for it

because there's no self trying to

organize anything we'll call it emergent

regularization emergent meaning that

it's a constraint that arises from

dynamic interaction I'll explain it next

the other kind of emergent constraint is

the one deacon discovered emergent self

regeneration that's a punchline here

wait for it

emerging constraint is different from

impose constraint Hall walls will

constrain you but so will walking

through congested foot traffic as you

thread your way through the congestion

others thread around you

you all take paths of least resistance

eventually traffic simplifies into

constrained pathways people filing

through taking regularized paths of

least resistance that emerge throughout

the congestion that's emergent

regularization self-organization or

emergent regularization explains a

variety of phenomena including

whirlpools chain reactions and crystal

formation none of which are self

regenerative they aren't alive ourselves

aren't trying

they don't self repair self propelled

through unregular lies paths of least

resistance

likewise bathtubs have drained faster

when there's a whirlpool at the drain

chain reactions consume all the nearby

fuel and then peter out deacon though

shows how to emergent regularization

processes chain reaction and crystal

formation can end up synergistically

coupled each constraining the other

yielding emergent self-regeneration

selves that try to self repair self

propelled chain reactions and crystal

formation

ChatGPT
The passage explores the concept of constraint as a foundational principle in understanding how living systems maintain their organization and functionality amidst the possibilities of disorder. Constraint, as discussed, is not only about the physical or engineered barriers that guide or limit behavior and processes (like riverbanks or hallways), but also about the patterns and structures that emerge spontaneously from the interactions within a system. This emergence of order from the interactions among components of a system leads to what is known as "emergent regularization" or, more commonly, "self-organization."

Emergent regularization is a process through which constraints arise naturally from the dynamics within a system, without any deliberate organizing principle or external imposition. It is a spontaneous phenomenon that can lead to organized patterns, flows, or structures within a system. This concept explains various natural phenomena like whirlpools, chain reactions, and crystal formations, which, though organized, do not exhibit the characteristics of life such as self-repair or self-propulsion.

However, the passage then introduces a pivotal distinction brought to light by Terrence Deacon's work, which identifies another kind of emergent constraint: emergent self-regeneration. This concept goes beyond the simple organization of non-living systems to describe how certain processes can become intertwined in a way that not only generates a regular pattern or structure but also maintains and regenerates itself over time. This self-regenerative capacity is a hallmark of living systems, distinguishing them from non-living systems that might display organized patterns but lack the ability to maintain and renew themselves.

Deacon's discovery suggests that living systems, or "selves," emerge from the synergistic coupling of processes like chain reactions and crystal formations, which, when intertwined, can produce a new level of organization. This emergent self-regeneration enables living systems to actively maintain their structure and function in the face of entropy, effectively "trying" to survive through self-repair and self-propulsion. It's an advanced form of emergent regularization that goes beyond mere pattern formation to include the ability to sustain and reproduce the pattern itself.

This concept of emergent self-regeneration offers a profound insight into the nature of life and its distinction from non-living systems. It provides a scientific framework for understanding how the complex, self-sustaining processes characteristic of living organisms can arise from simpler physical and chemical interactions. By identifying the mechanisms through which life can self-organize and self-regenerate, Deacon's work contributes to our understanding of the origins and nature of life itself, bridging the gap between the living and the non-living through the principles of emergent constraint.

User
a chemical chain reaction is like a

population explosion of molecules not

selves it starts when a small

concentration of catalyst molecules say

of two kinds imagine them like male and

female transform reactant molecules fuel

into more catalysts transforming more

reactants into still more catalyst on

and on until the reactants are depleted

it's an example of emergent

regularization because it generates a

fleeting concentration of catalysts

fleeting because nothing holds them

together the concentration dissipates

crystal formation generates order

molecules lining up forming solids

including shells or tubes again

passively doing nothing to regenerate

their order just slowly breaking down

okay finally ready for the origins of

selves from basic chemistry picture a

chain reaction that generates

a population explosion of catalysts but

also crystal shell molecules shell

molecules coalesce into shells that

happen to enclose a cluster of catalysts

sort of like seeds when the shell

happens to break apart in the presence

of reactants the chain reaction resumes

generating more catalysts and shell

molecules Terry calls this model for the

origins of life and autogen a self

regenerator

with selves like us there's synergy our

parts are means to each other's ends

lungs help heart which helps lungs etc

how does that start with the origin

chain reaction and Shale formation

processes are means to prevent each

other from ending by itself a chain

reaction is likely to run out of fuel

and then dissipate but with the autogen

before it does the shells encapsulate a

cluster of catalysts big enough that the

chain reaction is more likely to resume

less likely to dissipate likewise by

itself crystal shells degenerate and

shale molecules drift away but the chain

reaction replenishes the shell molecules

regenerating new shells look what you

get self repair and self reproduction

when a seed breaks and catalysts spill

out the chain reaction resumes

generating more seeds that's it trying

to prevent degeneration self protection

the shell prevents catalyst molecules

from just dissipating

all known selves are continuously

enclosed in protective covering Auto

gens aren't when a seat is broken the

Auto Genest just exposed catalysts

generating more catalysts and shell

molecules nonetheless preventing their

own dissipation since the chain reaction

generates more seeds the auto agendas

has two phases

sometimes closed dormant seeds protected

from degeneration sometimes open

regenerating more seeds having two

phases is the only way to solve at the

origin of selves a problem for all

ourselves

we all have to be open to get energy for

our work to self repair and self

reproduce but we also have to stay

closed to protect against a generation

all known selfs achieve selective

interaction by means of highly evolved

pores orifices and semi permeable

membranes first selves haven't evolved

yet so have to achieve selective

interaction by accident and they're not

going to chance on some lucky membrane

that protects against the bad stuff but

let's in the good stuff the origin gets

selective interaction by being sometimes

close sometimes open

so what then is the life force soul

spirit will agency drive motivation that

we find in cells but not in inanimate

things

it's neither magic nor mechanics Deacon

describes it as a constraint that

channels energy into work that

regenerates the constraint

you're still chemistry you don't define

natural law and you're not some

supernatural thing either you're not

just any chemistry you're chemistry that

keeps preventing its own degeneration to

understand us it pays to start at the

beginning uncluttered by our highly

evolved bells and whistles like

consciousness start with first selves as

we did here read more about the origin

the logic behind it and its implications

in my new book neither ghosts nor

machine the emergence and nature of

selves or get more information at our

website Helio dynamics calm and thanks

for watching

I know it was a lot but we're solving a

mystery that has dogged scientists for

centuries and philosophers for millennia

ChatGPT
The passage provides a comprehensive explanation of the origins of life, utilizing the concepts of chemical chain reactions and crystal formation as foundational processes that lead to the emergence of self-regenerating systems, termed "autogens" by Terrence Deacon. This explanation seeks to bridge the gap between non-living chemical processes and the living systems that exhibit self-repair, self-protection, and self-reproduction—key characteristics of life.

In this model, life begins not with a miraculous leap from inanimate to animate but through a gradual process where certain chemical reactions become self-sustaining. The initial stage involves a chain reaction where catalysts facilitate the transformation of reactants into more catalysts, creating a self-amplifying cycle. However, this process alone, akin to a population explosion of molecules, is fleeting because it lacks a mechanism to maintain the concentration of catalysts.

Simultaneously, the process of crystal formation introduces another dimension of order, with molecules aligning to form solid structures. Yet, like the chain reaction, this order is passive and does not actively regenerate itself.

The emergence of life, according to this model, occurs when these two processes—chain reaction and crystal formation—become intertwined in a way that allows them to sustain each other. The formation of crystal shells that encapsulate clusters of catalysts creates a primitive form of self-regeneration. When these shells break in the presence of reactants, the enclosed catalysts can initiate a new chain reaction, producing more catalysts and shell molecules. This cycle of breaking and reforming introduces the fundamental mechanisms of self-repair and self-reproduction, marking the transition from non-life to life.

This synergistic relationship between the chain reaction and shell formation demonstrates how life can emerge from basic chemistry through processes that prevent their own termination. By replenishing each other, the chain reaction and crystal formation give rise to a primitive form of trying—the effort to prevent degeneration. This model explains how the earliest forms of life could have navigated the challenges of obtaining energy and protecting themselves from degradation, setting the stage for the evolution of more complex life forms.

Ultimately, the passage suggests that the essence of life—what we might romantically call the soul, spirit, or life force—is not a mystical or supernatural phenomenon but a natural outcome of emergent self-regeneration. Life is described as a constraint that channels energy into work, regenerating and maintaining itself against the backdrop of entropy. This perspective demystifies the nature of life, presenting it as a continuation of chemical processes that have evolved the capacity to sustain themselves through self-regenerative constraints.

By starting from this foundational understanding of the first selves, the passage invites us to reevaluate our concepts of life and consciousness, proposing that to grasp the essence of living beings, we should consider the simplest forms of self-sustaining chemistry. This approach not only offers insights into the origins of life but also challenges us to reconsider the boundaries between the living and the non-living, highlighting the continuity and complexity of the natural world.

User
A summary of the whole conversation.

ChatGPT
Our conversation delved into the intricacies of Jeremy Sherman's perspective on the limitations of the "engineering method" for explaining the essence and behaviors of living beings. Sherman argues that while engineering has provided a powerful framework for technological advancements, it falls short in capturing the complex, self-sustaining, and self-regenerating processes that define life. He contrasts the deterministic and mechanical models used in engineering with the dynamic, emergent properties of living systems that cannot be fully explained by simple models of switches and lines. Instead, Sherman introduces the concept of "emergent constraint theory" as a means to understand how life operates beyond the confines of traditional engineering models, emphasizing the role of self-regulation and the will to live as central to the nature of living beings.

We explored how living systems, through processes of self-repair, self-protection, and self-reproduction, actively work against entropy in a manner that distinguishes them from non-living systems. This self-regenerative capacity of living beings is argued to be a fundamental characteristic that cannot be reduced to mere mechanical interactions.

Further, we discussed the origins of life from a chemical perspective, introducing the idea of autogens as a model for understanding how life might have emerged from non-life. This model explains how life begins with the self-sustaining synergy between chemical chain reactions and crystal formation, leading to the first primitive forms of self-regenerating systems capable of self-repair and self-reproduction. These processes lay the groundwork for the evolution of more complex life forms, marking the transition from non-life to life through natural mechanisms of emergent self-regeneration.

Throughout the conversation, the emphasis was placed on understanding life as an emergent phenomenon that arises from the physical and chemical laws of the universe. The discussion challenges us to reconsider our perceptions of life, moving beyond simplistic mechanical analogies to embrace a more nuanced understanding of the dynamic, self-organizing principles that underlie the natural world. This perspective not only provides insights into the origins and nature of life but also invites us to reflect on the essence of our existence and the complex interplay between order and chaos that characterizes the living universe.

User
There was a book, Children's Drawing, by Jacqueline Goodnow, that explained how children draw using an evolving set of constraints, that if they attempt to draw the head, then there is less room for the body, the the arm might leave room for the hands, and so it is developed  iteratively based on visual feedback with the image (what's missing?), line quality artifacts, limitations of previous choices, running out of room on the page. Leading to some predictable developmental sequences and not necessarily being based on mental images.

ChatGPT
The book "Children's Drawing" by Jacqueline Goodnow delves into the developmental aspects of drawing in children, focusing on how their drawing skills evolve through the application of constraints. Goodnow proposes that as children draw, they encounter and navigate various limitations, such as spatial constraints on the page, which in turn influence the composition and details of their drawings. This iterative process of drawing is guided not only by the children's internal mental images but also significantly by the visual feedback they receive from their work in progress.

According to Goodnow, the act of drawing for children is a dynamic interaction between their intentions, the emerging drawing, and the physical limitations they face, such as running out of room on the paper. This interaction leads to a series of decision-making processes where earlier choices in the drawing affect subsequent ones. For instance, drawing a disproportionately large head may leave insufficient space for the body, or starting an arm in a certain position might limit how the hands can be drawn.

This approach to understanding children's drawing emphasizes the importance of external factors and constraints in the development of drawing skills, alongside the cognitive and perceptual capabilities of the child. It suggests that children's drawings evolve through a feedback loop where the drawing's current state informs the next steps, leading to the development of certain patterns and sequences in how children represent the world around them on paper.

Goodnow's work provides valuable insights into the cognitive and perceptual processes underlying children's drawing behavior, highlighting the complex interplay between mental representations, physical actions, and the constraints imposed by the drawing medium. It suggests that understanding children's drawing and its development requires considering both internal and external influences on the child's drawing process.

User
It was published in 1977. I thought there might be some parallels with the emerging constraint theory we discussed earlier

ChatGPT
Indeed, Jacqueline Goodnow's exploration of how children's drawing develops within an evolving set of constraints, as detailed in her 1977 publication, shares conceptual parallels with the emerging constraint theory discussed in the context of the origins and nature of life. Both frameworks consider how systems—whether cognitive processes in human development or biochemical processes in the emergence of life—navigate and are shaped by constraints.

In Goodnow's analysis, the act of drawing is not just an outpouring of mental images onto a page but is significantly influenced by the feedback loop created as the drawing progresses. Constraints such as space on the page, the physical properties of the drawing materials, and the child's previous choices in their drawing iteratively shape the final outcome. This process highlights how limitations can lead to creative solutions and predictable developmental sequences in children's drawings.

Similarly, the emerging constraint theory in the context of life's origins and development focuses on how life emerges and evolves through the imposition of constraints on chemical processes. This theory suggests that life, at its core, is about the emergence of self-regenerating systems that arise from and navigate through a series of constraints—whether those are chemical, physical, or environmental.

Both theories underscore the idea that constraints do not merely limit but actively shape the development of complex systems, whether those systems are cognitive, biological, or chemical. Constraints drive the emergence of organized patterns, behaviors, or functions by narrowing the range of possibilities, thereby channeling the system's development along specific pathways.

These parallels illuminate a broader principle that spans disciplines: the role of constraints in driving the emergence and evolution of complex systems. Whether in the drawings of children or the foundational processes of life, constraints play a crucial role in guiding the system's development, highlighting a fundamental aspect of complexity in both natural and cognitive systems.

User
Alicia Juarrero has also written on something like this

ChatGPT
Alicia Juarrero, in her work "Dynamics in Action: Intentional Behavior as a Complex System," delves into the complexity of human action and decision-making, viewing them through the lens of complexity theory and dynamical systems. Juarrero argues that human behavior and intentions cannot be fully understood when broken down into simpler, linear causes and effects. Instead, she posits that intentional actions are emergent phenomena that arise from the complex interplay of numerous factors within a context of constraints and possibilities.

Juarrero's approach shares similarities with the discussion on emerging constraint theory and the developmental processes in children's drawing outlined by Jacqueline Goodnow. All these perspectives emphasize the importance of constraints in shaping complex systems, whether those systems are biological, cognitive, or social.

In "Dynamics in Action," Juarrero explores how actions are context-dependent and how intentions can shape the dynamics of action, leading to emergent outcomes that are not predictable from the initial conditions alone. This mirrors the idea of emergent constraint theory, where life and its processes are seen as outcomes of complex interactions within constrained systems, leading to the emergence of order and organization from seemingly disorderly or chaotic states.

Moreover, Juarrero's focus on the constraints that shape intentional behavior echoes Goodnow's observations on how physical, spatial, and perceptual constraints influence children's drawing processes. Both views appreciate how constraints do not merely restrict but actively participate in the generation of complex patterns, behaviors, and actions by limiting certain possibilities and enabling others.

By drawing connections between these various approaches, we can appreciate a broader interdisciplinary understanding of how complex systems—whether in cognitive science, biology, or philosophy—navigate through, adapt to, and are ultimately shaped by the constraints and possibilities inherent in their environments. This understanding underscores the intricate dance between limitation and possibility that characterizes the emergence and evolution of complex systems across different domains.

User
I also wanted to argue that the brain has many folds because it would fill a larger skull, but running out of room forces and it to turn in on itself internally forming a dissipative structure through a process I call recurrent protopathy

ChatGPT
The concept you're introducing, "recurrent protopathy," as a process explaining the brain's complex folding pattern is a fascinating perspective that dovetails with broader discussions on how constraints shape the structure and function of complex systems. In the context of brain development, the folding of the brain, or gyrification, increases the surface area of the cerebral cortex, allowing for a greater number of neurons and synaptic connections within the confines of the skull. This increased complexity supports advanced cognitive functions.

Your argument suggests that the physical limitation of skull size imposes a constraint on brain volume, which in turn promotes the brain to fold into itself, creating a densely packed structure of gyri and sulci. This process can be likened to a form of emergent behavior resulting from spatial constraints, akin to how complex patterns and structures emerge in other systems when faced with limitations.

The term "recurrent protopathy" appears to encapsulate the idea of a recurring or iterative process that gives rise to primitive (proto-) sensation or feeling (-pathy) as the brain develops its intricate folded structure. This concept may also imply that the brain's structural complexity, as a result of these constraints, is fundamentally linked to its functional capabilities, including the basic aspects of consciousness or sensory experience.

Drawing a parallel to "dissipative structures," which are ordered systems that emerge from the non-equilibrium processes of exchanging energy and matter with their environment, the brain's folding could be seen as a biological manifestation of how living systems maintain order and complexity through flux and constraint. Dissipative structures are characterized by their ability to dissipate energy in a way that maintains or increases their organization, and the brain, with its energy-intensive processes and complex architecture, fits this description well.

This perspective enriches our understanding of the brain's development and its functional architecture by highlighting the role of physical and biological constraints in shaping its evolution. It underscores the principle that limitations, rather than merely imposing restrictions, can act as catalysts for complexity and organization within biological systems. Your notion of "recurrent protopathy" adds a unique dimension to the discourse on how the brain's structure—specifically, its folds—emerges from the interplay between physical constraints and the biological imperative to maximize functionality within those limits.

User
Functional coupling and physico-chemical constraint
edit
Related to the idea of stabilizing selection is that of the requirement that organisms function adequately in their environment. Thus, where stabilizing selection acts because of the particular niche that is occupied, mechanical and physico-chemical constraints act in a more general manner. For example, the acceleration caused by gravity places constraints on the minimum bone density and strength for an animal of a particular size. Similarly, the properties of water mean that tissues must have certain osmotic properties in order to function properly.

Functional coupling takes the idea that organisms are integrated networks of functional interactions (for example, the vertebral column of vertebrates is involved in the muscle, nerve, and vascular systems as well as providing support and flexibility) and therefore cannot be radically altered without causing severe functional disruption. This may be viewed as one type of trade-off. As Rupert Riedl pointed out, this degree of functional constraint — or burden — generally varies according to position in the organism. Structures literally in the centre of the organism — such as the vertebral column — are often more burdened than those at the periphery, such as hair or toes.

ChatGPT
The concepts of functional coupling and physico-chemical constraint delve into the intricate relationship between an organism's structure and its ability to survive and thrive within specific environmental contexts. These ideas highlight the evolutionary pressures and physical laws that shape biological forms and functions, emphasizing the interconnectedness of biological systems and the limits imposed by physics and chemistry.

Functional Coupling refers to the integrated networks of interactions among different parts of an organism that contribute to its overall functionality. This concept suggests that organisms are more than just collections of parts; they are systems in which each component is linked to others in ways that are crucial for the organism's survival. For example, the vertebral column in vertebrates does not merely provide structural support; it is also integral to the functioning of muscle, nerve, and vascular systems. This integration means that significant alterations to one part of the system can disrupt other functions, highlighting a form of evolutionary trade-off where changes in one trait can have cascading effects on others due to their functional interconnectedness.

Physico-Chemical Constraint underlines the role of physical and chemical laws in determining the possible forms and functions of organisms. The natural world operates under a set of universal laws, such as gravity and the properties of water, which impose constraints on how organisms can develop and function. For instance, the strength and density of bones in terrestrial animals are constrained by the force of gravity; similarly, the osmotic properties of tissues are dictated by the chemical properties of water. These constraints ensure that only those organisms with characteristics compatible with these physical and chemical realities can survive, leading to a form of stabilizing selection that favors traits allowing organisms to function effectively in their environments.

The idea that functional burden varies according to a structure's position within an organism further illustrates the complexity of biological systems. Central structures, like the vertebral column, have a high functional burden because they play crucial roles in multiple systems. In contrast, peripheral structures, such as hair or toes, although important, may bear a lesser burden and, consequently, might exhibit greater variability without severely impacting the organism's overall viability.

Together, these concepts provide a framework for understanding the interplay between evolutionary biology and physical laws. They illustrate how organisms are finely tuned systems shaped by both the internal demands of functional integration and the external demands of environmental constraints. This dual pressure ensures that the evolutionary paths available to organisms are not limitless but are instead guided by a combination of functional necessity and physico-chemical realities.

User
Connections with the earlier topics.

ChatGPT
The discussions on functional coupling, physico-chemical constraints, and the concepts of emerging constraint theory, recurrent protopathy, and the development of children's drawing skills by Jacqueline Goodnow and the dynamical systems perspective of Alicia Juarrero, all converge on a central theme: the intricate dance between constraints and possibilities in shaping complex systems, whether they are biological organisms, cognitive processes, or developmental patterns.

Emerging Constraint Theory and Physico-Chemical Constraints:
Emerging constraint theory, which explores how life and its processes arise and evolve through the imposition of constraints on chemical processes, aligns with the concept of physico-chemical constraints. Both perspectives highlight how the physical and chemical laws of the universe limit and guide the development and function of living organisms. These constraints ensure that life, in its myriad forms, is compatible with the universal laws governing matter and energy, leading to the emergence of complex, self-regulating systems from simpler chemical interactions.

Recurrent Protopathy and Functional Coupling:
The concept of "recurrent protopathy," suggesting an iterative process by which the brain's complex folding is a response to spatial constraints, mirrors the idea of functional coupling. Both notions emphasize the interdependence of system components—whether neural pathways in the brain or the interconnectedness of vertebral columns, muscles, nerves, and vascular systems in vertebrates. This interdependence ensures that modifications in one part of the system can profoundly impact overall functionality, demonstrating the adaptive, yet constrained, nature of biological evolution.

Children's Drawing Development and Functional Burden:
Goodnow's exploration of how children's drawing skills develop within a framework of evolving constraints offers a parallel to the concept of functional burden. Just as children navigate spatial limitations and previous choices in their drawings, leading to predictable developmental sequences, organisms must also navigate the constraints imposed by their functional architectures. Central structures in organisms, burdened with multiple critical roles, exhibit less variability, akin to how fundamental choices in a drawing (like the placement of the head) limit subsequent possibilities.

Dynamical Systems and Evolutionary Trade-offs:
Juarrero's dynamical systems approach to understanding human action and decision-making underscores the complexity and emergent properties of cognitive processes. This perspective dovetails with the evolutionary trade-offs inherent in functional coupling and physico-chemical constraints, where the modification of one trait impacts others due to their interconnectedness. Both views highlight how complex patterns of behavior, biological structures, and functions emerge from the interactions within constrained systems.

In sum, the connections across these topics reveal a universal principle applicable to various domains: constraints, whether physical, chemical, biological, or cognitive, are not mere limitations but are fundamental drivers of complexity, organization, and function. Through the lens of these constraints, we can better understand the development of cognitive processes, the evolution of life, and the emergence of complex behaviors and structures.

User
A list of all of the topics we've discussed.

ChatGPT
Certainly, here's a summary of the topics we've discussed:

Jeremy Sherman's Perspective on the Engineering Method: The discussion began with an exploration of Jeremy Sherman's critique of the engineering method's limitations in explaining living behavior and the essence of life, proposing instead the concept of emergent constraint theory.

Emergent Constraint Theory: We delved into the idea of emergent constraint theory as an alternative to traditional engineering models, emphasizing the role of constraints in shaping the behavior and nature of living systems.

Children's Drawing Development: The conversation touched upon Jacqueline Goodnow's research on how children's drawing skills evolve within a set of constraints, leading to predictable developmental sequences based on spatial limitations and visual feedback.

Alicia Juarrero's Dynamics in Action: Alicia Juarrero's work on intentional behavior as a complex system was discussed, highlighting how human actions emerge from the complex interplay of numerous factors and constraints.

Concept of "Recurrent Protopathy": The idea of recurrent protopathy was introduced as a process to explain the brain's complex folding pattern, suggesting an iterative response to spatial constraints and its implications for brain development.

Functional Coupling and Physico-Chemical Constraint: This topic covered how organisms are shaped by both functional coupling within their systems and physico-chemical constraints imposed by environmental and physical laws.

Connections Among Topics: We explored the connections between the earlier topics, drawing parallels between the themes of constraints shaping complex systems across different domains, including biological organisms, cognitive processes, and developmental patterns.

These topics collectively weave a narrative about the importance of constraints in driving the complexity and functionality of systems, from the development of children's drawing skills to the folding of the brain and the evolution of life itself.

User
3.1|Constraint type 
Notable attempts have been made to classify constraints into distin- 
guishable typologies (see, e.g., Acar et al., 2019; Medeiros et al., 2017; 
Onarheim, 2012; Rosso, 2014). An understanding of which constraint 
types influence creativity can serve to provide practical advice and 
theoretical insight into understanding how and why constraints 
impact creative efforts. For instance, the view that constraints can be 
potentially negative for creative efforts has stemmed from research 
on rewards (Amabile, 1979; Friedman, 2009) which would be classi- 
fied as one type of constraint. However, scholars (e.g., Onarheim, 
2012) have demonstrated how engineers in a manufacturing company 
would self-impose project constraints in creative tasks. Thus, it would 
appear that some constraints may be beneficial, while others a 
hinderance. However, to date, no research has been conducted to 
explore this from a meta-analysis standpoint. 
In a broad sense, Medeiros et al. (2017) classified constraints into 
four categories: (a) market (e.g., stakeholder needs/expectations), 
(b) organization (e.g., elements of the organization itself), (c) field 
(e.g., field norms), and (d) project (e.g., team characteristics). More con- 
cisely, Acar et al. (2019) classified constraints into three overarching 
clusters: input constraints (i.e., the unavailability of resources such as 
time or personnel), process constraints (i.e., restrictions that determine 
the subsequent steps throughout creativity processes such as formal 
procedures or rules), and output constraints (i.e., factors that define 
the end result of the creative processes such as the use of certain 
materials). These efforts are not intended to be exhaustive. Instead, 
these typologies stand as existing frameworks by which it is possible 
to neatly organize different constraints. As such, further classifications 
of constraints may emerge as research evolves. It is expected that this 
effort does not exhaust the possible classifications of constraints, but, 
rather, ground its expectations in existing theoretical evidence. 
Despite these overarching typologies, little is known about how 
these subsets of constraints function to influence creativity. To date, 
there is little synthesis of the literature that explores how the con- 
straint type may influence the constraint-creativity relationship. Of 
note, the authors commend the work of Acar et al. (2019) in their 
efforts to distinguish the research on these various types. Their work 
highlights the various conflicting findings on how constraints may 
behave differently across their respective classifications (Acar et al., 
2019). For instance, Acar et al. (2019) argued that input constraints 
(e.g., time) influence creativity via mediating mechanisms such as 
motivation, whereas process constraints (e.g., how a product should 
look) influence creativity via social routes. 
Similar complexities can be demonstrated in the classification by 
Medeiros et al. (2017). For instance, knowledge sharing, which Medei- 
ros et al. (2017) classified as a project constraint, has displayed a posi- 
tive relationship with creativity (Ali et al., 2018). In contrast, industrial 
competition, a field constraint in this typology, has demonstrated a 
negative relationship with innovation (Zhang et al., 2018). It may also 
be the case that some constraint types act alike. For example, market 
turbulence (e.g., a market constraint) and R&D intensity (e.g., an orga- 
nizational constraint) have both displayed positive relationships with 
radical innovation (Andreeva et al., 2017; Trolio et al., 2014). 
Thus, the present effort attempts to add clarity to these findings 
and concisely evaluate how each constraint type influences the 
constraint-creativity relationship. As such, our first research question 
is as follows: 
Research Question 1: Does constraint type moderate the relation- 
ship between constraints and creativity?

ChatGPT
The passage highlights a fascinating area of inquiry into the relationship between constraints and creativity, emphasizing that constraints can have varying impacts on creative efforts. It references several studies and classifications that attempt to organize and understand the diverse types of constraints, revealing that the nature of the constraint—whether beneficial or hindering—can significantly influence creative processes. The discussion introduces two notable attempts at classifying constraints:

Medeiros et al. (2017) propose a classification of constraints into four categories:

Market Constraints: Involving stakeholder needs and expectations.
Organization Constraints: Stemming from the elements of the organization itself.
Field Constraints: Relating to norms within a specific field.
Project Constraints: Pertaining to characteristics of the team or project specifics.
Acar et al. (2019) offer a more streamlined classification into three overarching clusters:

Input Constraints: Relating to the unavailability of resources such as time or personnel.
Process Constraints: Involving restrictions that determine the steps throughout the creativity processes, such as formal procedures or rules.
Output Constraints: Concerning factors that define the end result of the creative process, like the requirement to use certain materials.
The passage underscores that the impact of these constraints on creativity is complex and not fully understood, with some constraints potentially fostering creativity by imposing certain limits that require innovative solutions, while others may hinder creativity by overly restricting the creative process.

Furthermore, it is noted that different types of constraints may influence creativity through various mechanisms. For example, input constraints might affect creativity via motivation, while process constraints might do so through social dynamics. This nuanced understanding challenges the simplistic view of constraints as merely negative forces against creativity and suggests a need for further research to explore these dynamics comprehensively.

The overarching research question posed seeks to investigate whether the type of constraint moderates the relationship between constraints and creativity, aiming to add clarity to existing findings and contribute to a deeper understanding of how different constraints impact creative processes. This line of inquiry is crucial for both theoretical insight into the nature of creativity and practical advice on managing constraints in creative endeavors.

User
It was from [Re]thinking outside the box: A meta-analysis of constraints 
and creative performance 
Adam Damadzic1|Christopher Winchester2|Kelsey E. Medeiros1| 
Jennifer A. Griffith3 
1College of Business Administration, 
University of Nebraska Omaha, Omaha, 
Nebraska, USA 
2Carlson School of Management, University of 
Minnesota, Minneapolis, Minnesota, USA 
3Paul College of Business and Economics, 
University of New Hampshire, Durham, New 
Hampshire, USA 
Correspondence 
Adam Damadzic, PhD., College of Business 
Administration, University of Nebraska 
Omaha, 300 Mammel Hall, Omaha, NE 68182, 
USA. 
Email: adamadzic@unomaha.edu 
Summary 
The influence of constraints (i.e., barriers or limitations) on creativity has drawn 
attention from various fields but has largely yielded conflicting findings. Some studies 
suggest constraints may have a positive impact on creativity while others find a nega- 
tive impact. In an effort to clarify this debate and provide direction for future efforts, 
this meta-analysis examined the relationship between constraints and creativity. 
Using a sample of 111 published and unpublished studies, a series of random-effects 
meta-regression models and subgroup analyses were conducted and identified a sig- 
nificant positive relationship between constraints and creativity. Moderator analyses 
confirmed the relationship differed substantially depending on the constraint type, 
study design, funding status, and creativity operationalization and measurement. 
These findings suggest that constraints may not be detrimental to creativity, despite 
prior assumptions. Findings further suggest that constraint type may be less 
influential than typically assumed. Instead, methodological artifacts provide a better 
explanation for the varying existing findings in how constraints benefit or hinder cre- 
ativity. Theoretical and practical implications are discussed. 
KE YWOR DS 
constraints, creativity, innovation, meta-analysis 
1|INTRODUCTION 
Creativity research has historically proposed that constraints nega- 
tively influence creative performance (e.g., Amabile & Gryskiewicz, 
1989, Amabile et al., 1986; Kruglanski et al., 1971; McGraw & 
McCullers, 1979). At their core, constraints – externally imposed 
processes, practices, or decisions that restrict a problem space and 
potential solutions (Medeiros et al., 2017) – violate the canonical 
tenant of creativity that creativity requires autonomy (e.g., Osborn, 
1953). Osborn's (1953) popular work on brainstorming rules suggests 
creativity should be unconstrained, and early work by Amabile 
(e.g.,Amabile&Gryskiewicz,1987)andotherscholars 
(e.g., Kruglanski et al., 1971) further supported this notion. However, 
an emerging body of work (e.g., Acar et al., 2019; Medeiros et al., 
2014; Onarheim, 2012) counters this perspective. Biz Stone, a 
co-founder of Twitter, perhaps best summarized the crux of the 
emerging perspective when he said, “constraints inspire creativity” 
(Stone, 2014). 
The proposition that constraints can be helpful for creative 
efforts is centered around the idea that all creative problem-solving 
efforts are inherently constrained in some regard, such that the

ChatGPT
The summary from "Rethinking outside the box: A meta-analysis of constraints and creative performance" by Adam Damadzic et al. presents an insightful examination of the complex relationship between constraints and creativity. The meta-analysis challenges traditional views that constraints inherently hinder creative performance, suggesting instead that the impact of constraints on creativity is nuanced and may often be positive. This research synthesizes findings from 111 studies, employing random-effects meta-regression models and subgroup analyses to uncover a significant positive relationship between constraints and creativity. Importantly, moderator analyses revealed that the nature of this relationship varies significantly depending on factors like the type of constraint, study design, funding status, and how creativity is operationalized and measured.

Key takeaways from this work include:

Positive Relationship Between Constraints and Creativity: Contrary to the traditional assumption that constraints stifle creativity, this study found a generally positive relationship between the two, suggesting that constraints can indeed inspire creative solutions.

Variability Based on Constraint Type: The analysis underscores that not all constraints affect creativity equally. Different types of constraints (e.g., input, process, and output constraints identified by Acar et al., 2019) can have diverse impacts on creativity, highlighting the importance of understanding the specific nature of constraints in creative endeavors.

Influence of Methodological Factors: The relationship between constraints and creativity also appears to be influenced by methodological factors such as study design and the operationalization of creativity, suggesting that previous conflicting findings may be partly due to these variables.

Theoretical and Practical Implications: The findings challenge the notion that creativity requires complete autonomy and suggests that constraints, when properly understood and implemented, can be leveraged to enhance creative performance. This has significant implications for how creativity is fostered in various settings, including in educational, organizational, and innovation contexts.

Future Research Directions: The study calls for further exploration into how different types of constraints interact with various creative processes and outcomes, urging a more nuanced understanding of the constraint-creativity relationship.

This meta-analysis contributes to a growing body of evidence that reevaluates the role of constraints in creative work, suggesting that constraints, rather than being mere obstacles, can serve as catalysts for creative thinking and innovation.

User
From the influential author of Dynamics in Action, how the concepts of constraints provide a way to rethink relationships, opening the way to intentional, meaningful causation.

Grounding her work in the problem of causation, Alicia Juarrero challenges previously held beliefs that only forceful impacts are causes. Constraints, she claims, bring about effects as well, and they enable the emergence of coherence. In Context Changes Everything, Juarrero shows that coherence is induced by enabling constraints, not forceful causes, and that the resulting coherence is then maintained by constitutive constraints. Constitutive constraints, in turn, become governing constraints that regulate and modulate the way coherent entities behave. Using the tools of complexity science, she offers a rigorously scientific understanding of identity, hierarchy, and top-down causation, and in so doing, presents a new way of thinking about the natural world.

Juarrero argues that personal identity, which has been thought to be conferred through internal traits (essential natures), is grounded in dynamic interdependencies that keep coherent structures whole. This challenges our ideas of identity, as well as the notion that stability means inflexible rigidity. On the contrary, stable entities are brittle and cannot persist. Complexity science, says Juarrero, can shape how we meet the world, how what emerges from our interactions finds coherence, and how humans can shape identities that are robust and resilient. This framework has significant implications for sociology, economics, political theory, business, and knowledge management, as well as psychology, religion, and theology. It points to a more expansive and synthetic philosophy about who we are and about the coherence of living and nonliving things alike.

ChatGPT
Alicia Juarrero, in her explorations following "Dynamics in Action," continues to delve deep into the fabric of causation and the nature of relationships in the natural world, extending her insights into the realm of personal identity and the broader implications for various fields of study. Her work, as described, pivots on the concept of constraints as central to understanding causation, coherence, and identity, challenging traditional notions that emphasize forceful impacts as the primary or sole causes of effects.

Key Concepts and Implications:
Enabling and Constitutive Constraints: Juarrero introduces the idea that coherence in natural and social systems emerges not from forceful causes but through enabling constraints that allow certain interactions while preventing others. Once coherence is achieved, constitutive constraints help maintain the integrity of these structures, acting as the fabric that holds entities together.

Governing Constraints and Behavior Regulation: These constitutive constraints evolve into governing constraints that regulate and modulate behaviors of coherent entities. This hierarchical arrangement of constraints provides a framework for understanding how complex systems maintain their coherence over time while adapting to changes.

Redefining Personal Identity: Moving beyond the static view of identity as determined by internal traits or essential natures, Juarrero posits that personal identity is instead grounded in dynamic interdependencies. This perspective emphasizes the fluid and emergent nature of identity, shaped by interactions and relationships rather than predetermined characteristics.

Stability versus Flexibility: Contrary to the belief that stability equates to rigidity, Juarrero's insights from complexity science suggest that true stability in natural and social systems comes from flexibility and adaptability. Stable entities that cannot adapt are brittle and prone to failure, whereas systems that can dynamically respond to changes are more resilient.

Broad Implications: The framework Juarrero proposes has far-reaching implications for understanding not only personal identity but also the coherence and behavior of entities in fields ranging from sociology and economics to psychology, religion, and theology. It invites a reconsideration of foundational concepts in these disciplines and offers a more nuanced approach to understanding the complexity of the natural and social worlds.

By centering the role of constraints in inducing and maintaining coherence, Juarrero's work offers a revolutionary way of thinking about causation, identity, and the behavior of complex systems. Her approach, grounded in complexity science, provides a scientifically rigorous yet philosophically rich perspective on the interconnectedness and dynamism of the natural world, challenging conventional wisdom and opening new avenues for exploration across a multitude of disciplines.

User
A Bayesian framework for emergent constraints: case studies 
of climate sensitivity with PMIP 
Martin Renoult1, James Douglas Annan2, Julia Catherine Hargreaves2, Navjit Sagoo1, Clare Flynn1, 
Marie-Luise Kapsch3, Qiang Li4, Gerrit Lohmann5, Uwe Mikolajewicz3, Rumi Ohgaito6, Xiaoxu Shi5, Qiong Zhang4, 
and Thorsten Mauritsen1
Received: 20 December 2019 – Discussion started: 17 January 2020 
Revised: 4 July 2020 – Accepted: 27 July 2020 – Published: 10 September 2020 
Abstract.In this paper we introduce a Bayesian frame- 
work, which is explicit about prior assumptions, for using 
model ensembles and observations together to constrain fu- 
ture climate change. The emergent constraint approach has 
seen broad application in recent years, including studies con- 
straining the equilibrium climate sensitivity (ECS) using the 
Last Glacial Maximum (LGM) and the mid-Pliocene Warm 
Period (mPWP). Most of these studies were based on ordi- 
nary least squares (OLS) i ts between a variable of the cli- 
mate state, such as tropical temperature, and climate sensi- 
tivity. Using our Bayesian method, and considering the LGM 
and mPWP separately, we obtain values of ECS of 2.7K 
(0.6–5.2,5th–95thpercentiles)usingthePMIP2,PMIP3,and 
PMIP4 datasets for the LGM and 2.3K (0.5–4.4) with the 
PlioMIP1 and PlioMIP2 datasets for the mPWP. Restricting 
the ensembles to include only the most recent version of each 
model, we obtain 2.7K (0.7–5.2) using the LGM and 2.3K 
(0.4–4.5) using the mPWP. An advantage of the Bayesian 
framework is that it is possible to combine the two periods 
assuming they are independent, whereby we obtain a tighter 
constraint of 2.5K (0.8–4.0) using the restricted ensemble. 
We have explored the sensitivity to our assumptions in the 
method, including considering structural uncertainty, and in 
the choice of models, and this leads to 95% probability of 
climate sensitivity mostly below 5K and only exceeding 6K 
in a single and most uncertain case assuming a large struc- 
tural uncertainty. The approach is compared with other ap- 
proaches based on OLS, a Kalman i lter method, and an al- 
ternative Bayesian method. An interesting implication of this 
work is that OLS-based emergent constraints on ECS gener- 
ate tighter uncertainty estimates, in particular at the lower 
end, an artefact due to a l atter regression line in the case of 
lack of correlation. Although some fundamental challenges 
related to the use of emergent constraints remain, this paper 
provides a step towards a better foundation for their potential 
use in future probabilistic estimations of climate sensitivity. 
1Introduction 
In recent years, researchers have identif i ed a number of rela- 
tionships between observational properties and a future cli- 
mate change, which was not immediately obvious a priori but 
which exists across the ensemble of global climate models 
(GCMs) (Allen and Ingram, 2002; Hall and Qu, 2006; Boé 
et al., 2009; Cox et al., 2018) participating in the Climate 
Model Intercomparison Project (CMIP). These relationships 
are generally referred to as “emergent constraints” as they 
emerge from the ensemble behaviour as a whole rather than 
from explicit physical analysis. 
Such emergent constraints have been broadly used to con- 
strain properties of the Earth’s climate system which are not 
Published by Copernicus Publications on behalf of the European Geosciences Union. 
1716M. Renoult et al.: A Bayesian framework for emergent constraints 
easily or directly observable. These are usually presented in 
probabilistic terms, mostly based on ordinary least squares 
(OLS) methods. For example, studies have explored the con- 
straint on equilibrium climate sensitivity (ECS), which is the 
global mean equilibrium temperature after a sustained dou- 
bling of CO2over pre-industrial levels, using model out- 
puts from the Paleoclimate Model Intercomparison Project 
(PMIP) (Hargreaves et al., 2012; Schmidt et al., 2014; 
Hopcroft and Valdes, 2015; Hargreaves and Annan, 2016). 
Because of their relatively strong temperature signal, pale- 
oclimate states like the Last Glacial Maximum (LGM) and 
the mid-Pliocene Warm Period (mPWP) are often considered 
to be promising constraints for the ECS (Hargreaves et al., 
2012; Hargreaves and Annan, 2016), in particular at the high 
end. 
Almost all emergent constraint studies have used OLS- 
based methods to establish the link between variables in the 
model ensembles. However, whether ECS or another climate 
parameter was investigated, the theoretical foundations for 
the calculations have not previously been clearly presented. 
An additional problem arising from this is the resulting dif- 
i culty in synthesising estimates of climate system properties 
generated by different statistical methods with different, and 
often not explicitly introduced, assumptions. These methods 
include OLS but also alternative Bayesian approaches such 
as estimates of the climate sensitivity using energy balance 
models (Annan et al., 2011; Aldrin et al., 2012; Bodman and 
Jones, 2016). 
Two recent papers have also addressed the question of 
emergent constraints in different ways. Bowman et al. (2018) 
presented a hierarchical statistical framework which went a 
long way to closing the gap in theoretical understanding of 
emergent constraints. Conceptually, it is very similar to a 
single-step Kalman i lter, with which the iteration process 
is avoided to only keep a single updating of a prior into a

ChatGPT
The article, "A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP" by Martin Renoult and colleagues, introduces a sophisticated Bayesian methodology for integrating model ensembles and observations to refine predictions of future climate change, particularly focusing on equilibrium climate sensitivity (ECS). This approach marks a significant advancement in utilizing emergent constraints, a method that leverages relationships observed across global climate model ensembles to infer properties of the Earth's climate system.

Key Points and Contributions:
Bayesian Framework for Emergent Constraints: The paper presents a Bayesian framework that is explicit about prior assumptions and utilizes this framework to refine ECS estimates based on past climate states like the Last Glacial Maximum (LGM) and the mid-Pliocene Warm Period (mPWP).

Estimates of Climate Sensitivity: Applying this framework to the PMIP datasets, the study offers ECS estimates for the LGM and mPWP, demonstrating the framework's utility in providing probabilistic estimates of climate sensitivity. The results suggest an ECS of approximately 2.7K for the LGM and 2.3K for the mPWP, with combined analysis tightening the constraint to 2.5K.

Comparison with Other Methods: The study critically evaluates the Bayesian approach against other methodologies, such as OLS and Kalman filter methods, showing the advantages of the Bayesian approach in dealing with assumptions and uncertainties inherent in climate modeling and observational data integration.

Sensitivity to Assumptions and Model Choices: The authors explore how different assumptions within the Bayesian framework and choices of models affect the outcomes, demonstrating the method's flexibility and the importance of considering structural uncertainties.

Challenges and Potential of Emergent Constraints: While acknowledging the fundamental challenges associated with using emergent constraints, the paper argues for their potential in providing a more solid foundation for future probabilistic estimates of climate sensitivity.

Implications and Broader Context:
This work contributes significantly to the field of climate science by providing a robust, scientifically rigorous approach to leveraging emergent constraints for estimating climate sensitivity. By adopting a Bayesian perspective, Renoult et al. not only refine our understanding of ECS but also open pathways for integrating diverse sources of climate data more effectively. This methodology has the potential to enhance the predictive accuracy of climate models and inform climate policy and response strategies by offering more precise probabilistic forecasts of climate sensitivity.

The exploration of emergent constraints through a Bayesian lens also invites further research into the theoretical foundations of this approach and its application across different climate system properties. It underscores the complexity of the Earth's climate system and the necessity of sophisticated statistical tools to unravel this complexity. Through this work, the authors highlight the nuanced interplay between theoretical assumptions, model selection, and statistical methodology in advancing our understanding of climate dynamics and future change.

User
Could I get further examples of how emergent constraints might be used in climate science? I think it means we do not need to test all possibilities. It seems to be similar to the great filter pipeline in the Fermi paradox

ChatGPT
Emergent constraints in climate science are a powerful tool for reducing uncertainty in climate projections without the need to explicitly test every possible scenario. They involve identifying relationships between observable climate variables and future climate outcomes based on the ensemble behavior of climate models. This method effectively narrows down the range of potential future states by focusing on those consistent with observed relationships. Here are several examples of how emergent constraints might be applied in climate science:

Cloud Feedback and Climate Sensitivity:
One of the most significant uncertainties in climate models is the effect of clouds on Earth's energy balance. Emergent constraints have been used to link observable current climate states, such as the distribution and types of clouds, with climate sensitivity. By identifying how well models that accurately simulate current cloud distributions predict future climate sensitivity, scientists can constrain estimates of climate sensitivity to more likely values.

Arctic Sea Ice Decline:
The rate of Arctic sea ice decline varies significantly across different models. Researchers have used emergent constraints to correlate observed trends and variability in Arctic sea ice extent with future projections. Models that more accurately capture the current trends and natural variability of sea ice are weighted more heavily in projecting its future decline, providing a more reliable forecast.

Carbon Cycle Feedbacks:
The response of the Earth's carbon cycle to warming (including the ability of oceans and forests to absorb CO2) introduces uncertainty into climate projections. Emergent constraints can be applied by correlating current observable aspects of the carbon cycle, such as the strength of land carbon uptake in response to temperature or CO2 concentration increases, with long-term feedbacks. This helps in refining predictions of future CO2 concentrations and their impact on climate.

Tropical Precipitation Patterns:
Predicting changes in rainfall patterns, especially in the tropics, is crucial for assessing future climate impacts. Emergent constraints have been used to correlate current observable patterns of tropical precipitation and atmospheric circulation with projected changes in rainfall. This helps in improving the reliability of regional climate projections, which is essential for adaptation planning.

Extreme Weather Events:
Projecting changes in the frequency and intensity of extreme weather events (e.g., heatwaves, hurricanes) is challenging due to their complex nature. Emergent constraints can help by identifying relationships between the current climate state (such as sea surface temperature patterns) and the characteristics of extreme events in model simulations. This approach can narrow down the range of projections for future extreme weather changes.

Emergent constraints serve a similar conceptual purpose to the "Great Filter" in the Fermi Paradox by acting as a filtering mechanism that reduces the vast space of possibilities to those most consistent with observed realities. In the context of climate science, this method does not eliminate the need for comprehensive testing and validation of models but significantly enhances the ability to make accurate predictions by focusing on the most credible outcomes based on empirical evidence.

User
I think that this is argument for why all possible worlds do not exist, the physical world prevents or constrains the formation of gas giants two close to the star, the availability of heavy elements and so on

ChatGPT
The idea that not all possible worlds exist due to the constraints imposed by the physical laws of our universe touches upon fundamental principles in both philosophy and astrophysics. This concept aligns with emergent constraints as discussed in the context of climate science, illustrating how natural laws and conditions limit the range of possible outcomes in various systems, from planetary formation to climate change.

In astrophysics, the formation and characteristics of celestial bodies are indeed constrained by physical laws and initial conditions. For instance:

Formation of Gas Giants: The core accretion model of planet formation suggests that gas giants like Jupiter and Saturn require a substantial amount of solid material to form a core, around which a massive atmosphere of hydrogen and helium can accumulate. This process is much more efficient in the outer regions of a star system, where it's colder and solid materials can more easily condense. Closer to the star, high temperatures prevent the accumulation of gases around a planetesimal, making the formation of gas giants unlikely.

Availability of Heavy Elements: The composition of planets, including the presence of heavy elements, is dictated by the composition of the protoplanetary disk from which they form, which in turn depends on the generation of the host star and its predecessors. First-generation stars, composed almost entirely of hydrogen and helium, would not have had planets with significant amounts of heavier elements. These elements are formed in the cores of stars and distributed through the galaxy by supernovae. Thus, the availability of heavy elements for planet formation is constrained by stellar evolution and galactic chemical history.

Habitable Zones: The concept of a habitable zone, where liquid water can exist on a planet's surface, is another example of how physical laws constrain possible worlds. The distance of a planet from its star must fall within a certain range for it to be neither too hot nor too cold for liquid water to exist. This constraint limits the range of planets that could potentially harbor life as we understand it.

These examples demonstrate that the diversity of worlds and their properties are not limitless but are shaped by the fundamental forces and conditions of the cosmos. This realization doesn't diminish the wonder of the universe but rather provides a framework for understanding its complexity and variability. It underscores the interplay between chance and necessity—between the myriad possibilities inherent in the laws of physics and the specific outcomes realized in our universe.

Just as emergent constraints in climate science help us to refine our understanding of Earth's future climate, the constraints imposed by physical laws help astronomers and astrophysicists to narrow down the possibilities of planetary formation and characteristics, offering insights into the nature of our universe and its potential for harboring diverse forms of life.

User
Another list of all of the topics we've discussed.

ChatGPT
Certainly! Here's a comprehensive list of the topics we've covered:

Jeremy Sherman's Critique of the Engineering Method: Discussion on Sherman's perspective that traditional engineering methods fall short in capturing the essence of living systems, proposing emergent constraint theory as an alternative.

Emergent Constraint Theory: Explored how constraints shape living behaviors and systems, contrasting with the engineering approach of modeling phenomena through switches and lines.

Children's Drawing Development (Jacqueline Goodnow): Investigated Goodnow's findings on how children's drawing skills evolve within a framework of evolving constraints.

Alicia Juarrero's Dynamics in Action: Discussed Juarrero's insights into complex systems and intentional behavior, emphasizing the role of constraints in shaping emergent outcomes.

Concept of "Recurrent Protopathy": Introduced the concept of recurrent protopathy as a process explaining the brain's complex folding pattern in response to spatial constraints.

Functional Coupling and Physico-Chemical Constraint: Covered how organisms' structures and functions are influenced by both functional coupling within their systems and external physico-chemical constraints.

Connections Among Discussed Topics: Explored the interconnectedness between the themes of constraints shaping complex systems across different domains.

Bayesian Framework for Emergent Constraints in Climate Science: Reviewed a study introducing a Bayesian method for integrating model ensembles and observations to refine predictions of future climate change, specifically focusing on equilibrium climate sensitivity (ECS).

Examples of Emergent Constraints in Climate Science: Provided examples of how emergent constraints are used in climate science to reduce uncertainty in climate projections, including areas such as cloud feedback, Arctic sea ice decline, carbon cycle feedbacks, tropical precipitation patterns, and extreme weather events.

Astrophysical Constraints on Possible Worlds: Discussed how physical laws and conditions, such as the formation of gas giants and the availability of heavy elements, constrain the range of possible worlds and celestial phenomena.

Each topic delves into the overarching theme of how constraints—whether in biology, cognitive development, climate science, or astrophysics—play a crucial role in shaping complex systems and phenomena.