Cook, Stephen P. 2012 "Imagining A Theory Of Everything For Adaptive Systems"
published in: The Origin(s) of Design in Nature: A Fresh, Interdisciplinary Look at How Design Emerges in Complex Systems, Especially Life
After appreciating that speculations about the origin of design in nature are worldview dependent, this chapter discusses the "Theory of Everything" (TOE) idea, and identifies questions a TOE should address if it is to be useful. Building the following into a TOE is then considered: information concepts, optimizing principles, adaptive mechanisms, autopoietic circularity, closure, feedback, understanding non-local information transfer, and mechanisms for improving internal conceptual models. A recipe for using this imagined TOE is presented, built upon a proposed Generalized Optimal Action Principle and use of Darwinian natural selection. Conceivably, it might be applied to quantum states (Quantum Darwinism), genes (biological evolution), neural networks (brain), conceptual frameworks (worldviews) or pocket universes (cosmological natural selection). The chapter touches on controversies (intelligent design, downward causation mechanisms, etc.) and new science (complexity theory, quantum biology, holographic principle, etc.)
(pre-publication version, December 26 2011)
A THEORY OF EVERYTHING FOR ADAPTIVE SYSTEMS
BY STEPHEN P. COOK
Introduction: Worldviews, Reality, and a Theory of Everything
Many scientists define objective reality as independent of mind
or worldview by limiting it to events
and phenomena that can be recorded by devices.
different from how we describe it, like the difference between
physical terrain and the map of that terrain.
Biblical passages hint at this difference: in Genesis,
"In the beginning
God created the heavens and the earth," and in John,
"In the beginning
was the Word, and
the Word was with God, and the Word was God."
Some feel that scientific accounts of "In the
beginning..." are about to change.
Recently Eric Verlinde has advanced efforts to unify the four
fundamental forces as part of a "grand unified theory" (Verlinde
2010). It seems one of the
forces, gravitation, can be understood as something else: "an
entropic force caused by changes in the information associated with the
positions of material bodies."
Verlinde deduced this using some new physics: the holographic
difficulties reconciling the theory of gravity (general relativity) with
the wildly successful, but difficult to understand, theory of quantum
mechanics are over?
We mustn't get our hopes up!
I don't think a "theory of everything" (TOE) is right
around the corner. I
don't think we'll ever have a map of reality that perfectly
represents the terrain, or a model that gives perfect predictions for
everything of interest. A
worthy goal is making them increasingly useful.
Physicist James Hartle, who in 1983 collaborated with Stephen
Hawking in a paper entitled "Wave Function of the Universe,"
tells a story about Murray Gell-Mann (Hartle 2003).
Murray used to ask Hartle, "If you know the wave function of
the universe, why aren't you rich?"
Certainly seeking fundamental understanding is important and
there is more to life than economic gain.
But environmental concerns point to problems scientists might
work on that seem especially urgent.
In this paper I imagine what might inspire the construction of a
useful TOE, then consider how it might be formulated.
I begin by identifying what questions such a theory should
attempt to answer. At
the top of my list I'd put a problem, "How can humans adapt and
learn to live as part of nature?"
Solving that might require exploring "Who are we?" and
"How'd we get here?"
Daniel Dennett describes Darwin's Dangerous Idea as the
notion that design can emerge in the natural world from mere order via
an algorithmic process, rather than requiring an intelligent creator
(Dennett 1995). Skeptics see it as highly improbable that blind, mindless,
random processes could have produced seemingly purposefully designed
complex structures. Richard
Dawkins answers them in Climbing Mount Improbable with an analogy
emphasizing the power of accumulation (Dawkins 2004).
"On the summit sits a complex device such as an eye or a
bacterial flagellar motor. The
absurd notion that such complexity could spontaneously self-assemble is
symbolized by leaping from the foot of the cliff to the top in one
bound. Evolution, by
contrast, goes around the back of the mountain and creeps up the gentle
slope to the summit--easy!"
While biologists overwhelmingly accept Darwin's idea, other
scientists including physicists and cosmologists are not so sure.
As Susskind describes it, "The bitterness and rancor of the
controversy have crystallized around a single phrase--the Anthropic
Principle--a hypothetical principle that says that the world is
fine-tuned so that we can be here to observe it!"
In The Cosmic Landscape, he describes "the illusion
of intelligent design" and provides a "scientific explanation
of the apparent benevolence of the universe," one he calls
"the physicist's Darwinism."
He believes an eternal inflation mechanism has created a
"bubble bath universe." Space
cloning itself in nucleating bubbles has conceivably produced 10500
possible separate universes. While
not all of these actually exist, enough do to make our part of this
megaverse look like nothing special.
While it's obviously compatible with the intelligent life we
represent, most other pocket universes are not.
Susskind thinks maybe we're just lucky after all!
Clearly any TOE needs to once and for all answer the question,
"How, why (if there is a reason), and when was the universe
created?" For those
still clinging to an Intelligent Designer, if a TOE posits one, it must
address questions like, "How did the Intelligent Designer come into
being?" and "What maintenance (if any) on this design does the
Intelligent Designer do?"
Surviving in nature requires building and continually refining an
internal model of it--something which requires constant "dialogue
with nature" to use Prigogine's phrase (Prigogine 1997). "What
makes this dialogue possible?" he asks.
In arguing time is real and connected with irreversible
processes, he responds, "A time reversible world would also be an
unknowable world. There is an interaction between the knower and the
known, and this interaction creates a difference between past and
future." Those who
believe time is an illusion would disagree.
We'll want to ask, "What is time?"
Poet and mystic William Blake imagined it might be possible
"to see a world
in a grain of sand." Given
renewed interest in hologram-like universes, it seems we'd want our TOE
to tackle "Does the universe somehow contain its whole essence in
every part?" Some
mystics equate the universe with God; others believe a living
consciousness pervades the universe, something they equate with the
Cosmic Mind, or God. We'll need to ask, but we're getting ahead of ourselves!
Certainly before we make that inquiry, we've want a full
explanation of consciousness and its relationship to life.
Speaking of life, a TOE should describe what forms it exists in
throughout the universe and explain its origin.
We'd like detailed instructions on how to make it from non-living
building blocks. Speaking
of building blocks, we'd like to know, "Of what fundamental stuff
is the universe made?" Are
matter and energy more fundamental than space?
Perhaps information or consciousness or vital spirit is more
important still? And what
exactly will happen to that inner essence I think of as myself after my
body dies? It seems our expectations of a TOE are so great there is no
end to the questions!
Building Information Concepts and Optimizing Principles into a
could call the fundamental mechanism by which information is exchanged,
how "it" becomes "bit," "handshaking" or
"pinging." Instead I'll relate it to action and Newton's Third Law:
forces come in pairs: action forces and reaction forces. Action forces and action, though related, are different.
Action refers to an amount of energy transferred in a process
multiplied by the time elapsed Δt.
Optimizing principles are laws in which some physical quantity
must be a maximum or minimum under certain conditions.
Action is such a quantity; entropy, a measure of disorder, is
one; free energy is another.
second law of thermodynamics says the entropy of an isolated system can
only remain constant or increase, the latter occurring where
irreversible processes are involved.
Life's processes, in tending toward increasing organization and
decreased entropy, seemingly violate this law, but that's only because
they are open systems.
For a larger system made up of living creature and surrounding
environment, the decrease in entropy in the living subsystem is offset
by increased entropy in the environment.
While the second law can be seen as a principle of maximum
entropy, it can also be connected to energy transfer and gradients.
In this form it prohibits a spontaneous transfer of heat from
lower to higher temperature regions.
By itself, heat doesn't move up the "temperature hill."
In general, one can view nature's inexorably driving matter
toward equilibrium as pushing it downhill toward stability, and
attempting to level any gradients that exist in the process.
As Eric Schneider puts it, "Nature abhors gradients"
In creating order and existing far from equilibrium, life
seemingly resists nature's leveling tendencies--but only if one focuses
on living system S.
Schneider believes that detailed energy accounting for both S and
surrounding environment E, shows that life represents a particularly
efficient way of carrying out nature's overall increasing entropy,
leveling, and seeking equilibrium mandate.
For inanimate matter, Verlinde's connecting forces with entropy
gradients, wonderfully illustrates this.
He traces the origin of gravity and inertia to nature's seeking
to maximize entropy.
Appreciating his argument requires understanding entropy from an
information theory perspective.
Inspired by Boltzmann's 1877 characterization of entropy in terms
of the number of possible microstates which are available for a
macroscopic system to occupy, in 1948 Claude Shannon conceived of
measuring information content in terms of binary digits (bits) needed to
While convention specifies thermodynamic entropy and Shannon's
information related entropy in different units, when calculated for the
same number of possible microstates or degrees of freedom, they are
Information is not just an abstraction, it has a real
representation, being encoded in atomic or molecular energy levels, spin
states, sequences of nucleotide bases, neural synaptic connection
patterns, etc. Rather than
information quantity, information transfer deserves attention.
As Bateson pointed out, "All receipt of information is
necessarily the receipt of news of difference" (Bateson 1979).
connect information transfer with energy and entropy transfers.
Suppose an electron's spin changes from up to down.
Not only is there an energy transfer associated with that event,
communicating the knowledge requires energy to successfully transmit it
through a background of noise.
Entropy changes as information is transferred.
Whereas a system in thermal equilibrium with the environment has
maximum entropy, its randomness suggests maximum uncertainty and
algorithmic incompressibility. There
is no discernable message for an observer trying to extract a signal
carrying information from such a source.
Generally speaking, the entropy of a system has decreased if its
state after the event (measurement, information transfer, etc.) is more
sharply defined (less uncertain) than before, and the entropy of the
surrounding environment has increased.
Where life is concerned, living systems have been described as
"sucking information out of the environment," and their
fitness determined by "the
most fit is the best informed."
Such systems pull energy from the environment and occupy low
entropy, minimum uncertainty states.
Whereas entropy is often associated with unorganized or useless
energy, free energy is connected with energy capable of doing useful
work. Like entropy, free energy has been interpreted in an
information theory context. Karl
Friston, a neuroscientist, has examined theories about how the brain
works (Friston 2010). He
writes, "if we look closely at what is being optimized, the same
quantity keeps emerging namely value (expected reward, expected utility)
or its complement, surprise (prediction error, expected cost).
This is the quantity that is optimized under the free energy
Friston's free energy gauges some difference of interest between
living system and environment. Grandpierre has used extropic energy in
making a similar assessment (Grandpierre
conception of gravity is based on equating an energy difference, between
a configuration of matter and an equilibrium configuration, with both
work done by a restoring force and the product of temperature and change
Depending on the interpretation, free energy, extropic energy,
entropic energy, traditional Lagrangian, or combinations of these are
appropriate as "energy difference" input to action principles.
Such principles can be generalized and made more applicable.
In this regard consider a generalized optimal action principle
(GOAP) that maximizes stability:
δ (generalized action) = δ
( ∫ (energy difference) dt ) = 0
variation δ requires generalized action be optimized, minimized or
maximized, over some path in some unspecified (real, phase, or
Applying the GOAP requires computing generalized action. Imagine system S changes state, moving along some path from
point 1 at time t1 to point 2 at time t2.
Computing the generalized action involves breaking the path up
into tiny time intervals, multiplying the energy difference for each one
by the tiny time duration, and summing (integrating) these products over
the path. If S is
non-living, the energy difference can be the total energy (the
Hamiltonian) minus the potential energy that exists between system and
environment stored in the conservative force field.
For living system S, the energy difference can be interpreted in
different ways. Whether generalized action is minimized or maximized
depends on the system being considered.
If boundaries are drawn to solely include a living system,
summing up the products of energy transferred from the environment by
time over a path representing the lifetime of the system will maximize
generalized action. If
system boundaries are drawn to include the surrounding environment,
generalized action is minimized. Entropy
is maximized, but in attaining equilibrium the associated energy
difference between the matter part of the system and the environment
fluctuates around zero. Many
will argue that for living systems in a steady state (homeostasis),
generalized action will be only locally minimized since the path will
not include the death of the organism (where it arrives at a true
disagrees. He believes life
represents the most efficient way to degrade energy.
His models suggest life's processes maximize entropy faster than
a system that did not include living creatures would
(Schneider and Kay 1994).
Complex adaptive systems (CAS) that learn from their
environment can be considered as minimizing generalized action.
This is accomplished by a system that represents an internal
model the CAS has of itself and of the environment.
Ideally the fit between system and environment is an increasingly
good one over time. I view Friston's free energy principle, based on his modeling
of the brain, as calculating generalized action based on (what he calls)
surprise or a quantity gauging system minus environment prediction error
expressed in information theory terms.
Then, minimal generalized action means minimal uncertainty,
meaning the most probable, most stable state.
In concentrating on the interaction or fit between system and
environment, the GOAP recognizes "physics is simple only when
analyzed locally" (Misner 1973).
Information transfer requires the hand-shaking of action/reaction
force pairs. We interpret Newton's Third Law to mean "if the system
pushes on the environment, the environment unavoidably and
instantaneously pushes back on the system."
Building Adaptive Mechanisms into a TOE
GOAP applied to living systems can quantitatively assess life adapting
to its environment. What
specific adaptive mechanisms are employed?
Classically, we think of genes experiencing mutations (changing
genotype) and expressing themselves (changed phenotype) in the structure
or behavior of the organism. Mutations
that enhance survivability and lead to more copies eventually establish
themselves within the population. Such
changes are called adaptations. In
this way, the fit between an organism and its environment improves.
This fit can vary due to environmental changes, forcing whole
populations to respond by adapting or dying out.
Mutations and resulting adaptations are usually thought of as
part of a slow process,
like a drunk setting off from a street corner in random walk fashion.
As we shall see, quantum random walks may speed up this process.
Complex adaptive systems (CAS) can speed up the learning about
the environment process. Where
such a system is positioned in a 3D fitness landscape determines what
adaptations it can make. Before
considering such a plot, note where life is found in a simpler diagram
(Fig. 1): at a medium distance from equilibrium where structures
experience a range of fluctuations, where maximum capacity for
adaptability lies (Macklem 2008). To
further distinguish living CAS from non-living systems, consider Fig. 2,
where stability (the inverse of biologists' fitness function) is plotted
vertically down from a 2D horizontal plane.
There, potential niches are identified using two variables
(expressed in energy units): a system's distance EX from thermodynamic
equilibrium, and the amount of information IX it exchanges with the
Life at the Edge of Chaos
Fig. 2 Fitness
In this plot, the deeper the valley, the greater the stability,
and the steeper the slope, the greater the selection pressure.
It pictures the stability the GOAP could conceivably specify
mathematically for complex systems.
For a CAS, the worst place to be is at the origin, where EX=0 and
IX=0, representing equilibrium (death).
The randomness here represents maximum uncertainty in terms of
extract a message, meaning information transfer between system and
environment is impossible. The
best place is in one of the not so deep valleys a medium energy distance
away from the origin and "on the edge of chaos."
Langton says this is where "information gets its foot in the
door in the physical world, where it gets the upper hand over
energy" (Lewin 1992).
A diversity of living systems--ecosystems, immune systems, neural
networks, and genetic landscapes--have been successfully modeled using
both simple binary networks and more advanced networks known as cellular
automata. One such model is
the Game of Life, invented in 1970 but recently in the news with a
discovery that prompted this headline: "The Life Simulator--a self
replicating creature that might tell us something about our own
beginnings" (Aron 2010).
Before life's origin can be nailed down, we need to agree on a
definition of it. By 1950
von Neumann decided living things differ from machines in that, unlike
machines, they not only reproduce themselves, but also do self-repair.
Biologists' definitions typically included homeostasis, meaning
systems work together to maintain the internal temperature, pressure,
nutrient levels, waste products, etc. within normal ranges.
The new field of cybernetics helped to broaden the conception of
life. Norbert Wiener was
fascinated by systems where "causes produce effects that are
necessary for their own causation" (Wiener, 1948).
With understanding of DNA and messenger RNA, it soon became
apparent that a nice closure existed in the genetic code and its
operation: "nucleotides code for proteins which in turn code for
nucleotides" (Prigogine 1997).
By the mid 1970s, Varela and Maturna had described the
organization typical of most adaptive systems (Varela 1974).
Using the term autopoietic system, they characterized it in
similar circular fashion, noting "the product of its operation is
its own organization." Besides
this conceptual closure, they recognized the importance of a boundary
(cell membrane, etc.) that provided physical closure.
To them, living things are built around three interwoven things:
an autopoietic pattern of organization, embodied in a so-called
dissipative structure, and involved in a structural coupling life
process they call cognition. Acts of cognition, they say, produce structural changes in
the system, which itself specifies which perturbations from the
environment trigger such changes. Just
as Prigogine, whose dissipative structures concept they borrowed, liked
to stress the dynamic, spontaneous aspect of life by emphasizing its
"becoming" rather than its "being," Maturna and
Varela, felt "to live is to know."
Stuart Kauffman has investigated NK genetic fitness landscapes.
Learning from complex binary network models with N nodes and K
inputs to each node, he located the boundary between order and chaos in
the K=2 region. Below that
periodic attractors became, as K decreased, more stable point
attractors. As K steadily
increased above 2 strange attractors and totally chaotic behavior
resulted. Using a more sophisticated model and random adaptive walks,
Kauffman let the genes of different organisms interact and found the
evolving genes of one organism altering the fitness landscape of other
organisms. He eventually
concluded, in the words of science writer Roger Lewin, "coevolving
systems working as CAS tune themselves to the point of maximum
computational ability, maximum fitness, maximum evolvability" (Lewin
In an effort to improve their fit with the environment, some CAS
have a mechanism for anticipation, based on pattern seeking and internal
models of the environment. According
to Holland, these take two forms, tacit and overt (Holland 1995). The first "simply prescribes a current action, under an
implicit prediction of some desired future state."
He cites "a bacterium [moving] in the direction of a
chemical gradient, implicitly predicting that food lies in that
contrast, more advanced CAS use both tacit and overt models. The latter
"is used as a basis for explicit, but internal, explorations of
alternatives, a process often called look ahead."
The internal model physically realized in neural network
connections in our brains is often employed for this purpose.
Some CAS maintain a dialogue with nature in which feedback
continually informs internal models by testing the predictions they make
with real experience outcomes. According
to Friston, the human brain employs Bayesian probability to continually
update probabilities of certain outcomes based on new information
(Friston 2010). Unlike most complex systems, human internal
models include not only system and effect of the environment on the
system, but also effect of the system on the environment.
It seems that neither top down or bottom up one-way processes are
typically found in nature's mechanisms, instead circular feedback loops
Dennett has characterized evolution over geological time in terms
of matter steadily relying less on random dumb luck and more on skill
(Dennett 1995). This skill,
in the form of pattern recognition programs sorting and winnowing to
gather information, storing it in structures that grow in complexity
over time, learning about the environment through feedback, has been
slowly acquired. Systems
able to take advantage of a fortunate position in a fitness landscape
learn faster and adapt better than others. Natural selection weeds out
those that don't. In the
long run, not just individual organisms, but whole ecosystems evolve in
a way that maximizes fitness and stability.
Building Understanding of Mysterious Information Transfer into a
mysteries involving living creatures.
By some accounts, green plants convert sunlight into chemical
energy with nearly 100% efficiency; birds find their way back to
preferred sites after journeys of thousands of miles; after intercourse,
humans are naturally drugged and their lethargic inactivity gives sperm
a better change of fertilizing an ovum.
To physicists used to thinking about forces causing certain
effects, these are troubling examples of life directing its future
behavior, of processes where goals seemingly initiated at higher levels
in a system's organizational hierarchy dictate what happens at lower
levels. They seemingly involve teleology and downward causation
mechanisms. They are
difficult to explain. Perhaps
the ultimate mystery for evolution to explain is consciousness,
described as an emergent phenomenon in a multi-leveled system in our
brain where "the top level reaches back down towards the bottom
level and influences it" (Hofstadter 1979).
The mysteries aren't confined to the living world--the quantum
world is full of them. Consider
something as simple as the famous double slit experiment of physics, in
which light shines on a screen containing two narrow slits.
Which slit do individual photons go through in producing the
interference pattern seen on a second screen?
It seems like they go through both of them simultaneously and
they are both particles and waves!
Many feel understanding this is the key to making sense of
quantum mechanics and explaining many of life's mysteries.
Before applying the GOAP to quantum systems, consider one
statistical physics approach to handling systems involving large numbers
of interacting particles. This
can involve isolating each particle Si of the system and
studying its Brownian motion. In the resulting random walk, each step, due to a collision
with a particle Ej in the environment, would be expected to
cover distance d12= the square root of N, where N=# of
collisions or steps between times t1 and t2.
An optimal action principle is used in quantum mechanics, with
the action appearing in the phase of wave functions.
Feynman's path integral formulation (Feynman 1965) supposedly
removes teleological concerns about how the particle knows the
"right" path to take. It
involves calculating the probability of the particle taking a particular
path, and doing this for all possible paths.
The process can be connected to quantum random walks, which have
significant advantages over classical random walks.
They are more efficient at moving particles: particle Si
taking N steps between times t1 and t2 would be
expected to travel distance d12=N, quadratically faster (Kempe
Quantum random walks seemingly allow systems to do something
analogous to what a good chess player does: analyze all possible moves
and pick out the best one before it is made.
Perhaps this can explain what puzzled physicist Roger Penrose
back in 1989, when he said, "There
seems to be something about the way the laws of physics work which
allows natural selection to be a much more effective process than it
would be with just arbitrary laws" (Penrose 1989).
How do the laws of physics explain this and other mysteries of
the quantum world?
In the last three decades, physicists in the tradition of Bohr
and Wheeler, have made progress in understanding how particles like
photons choose a particular path and how classical trajectories emerge
from the randomness of the quantum world.
One of their leading theories is Quantum Darwinism (Zurek 2003).
It uses an Environment Induced Selection rule, based on
minimizing uncertainty, to explain which of a multitude of possible
quantum system states are actually physically realized.
These quantum states, which actually survive to have more than an
imagined, virtual existence, are called pointer states.
The extent to which these states disseminate and are redundant
measures their fitness. Using
GOAP, and thinking of a ball naturally rolling to a stable, lowest
energy position of equilibrium in a gravitational field, I see pointer
states as follows. Of many
possible systems operating between fixed points between times t1
and t2, they represent those in which the generalized action,
based on the energy difference between them and the surrounding
environment, is minimal--meaning they are the most stable states.
Why is this called Quantum Darwinism?
I think of a Darwinian process as follows. Copies, some slightly
different, are made of the original initial system S; these copies' fit
with their environment varies; as time passes, and natural selection
does its work, the population of copies of S will reflect the fitness
(i.e. the most fit copies will survive and produce more copies).
Respecting a (no-cloning) theorem that forbids making copies of
pure quantum states, in Quantum Darwinism the most robust, most stable
pointer states replicate the most in the classical realm.
These interact with the environment, leading to slight variations
of them, and the testing by natural selection you'd expect.
general, the quantum state of the system is a superposition of many
individual states. The
coupling or coherence that exists between two of these individual states
can be likened to the interference effects seen between light waves
emanating from the two slits in the double slit experiment.
Just as forcing a photon through one slit or the other by
measuring its position destroys the interference effects, measurements
or interaction with the environment destroys coherence in quantum
systems. While Quantum Darwinism details how information about
decohering systems is coded in the environment, quantum
computing involves working with (initially) coherent quantum
systems to encode information and avoiding decoherence.
So each step of a quantum random walk is made without an
intermediate measurement, which would destroy information and its
advantage over its classical counterpart.
A quantum walk can be seen as a process in which a system learns
about its environment without provoking it.
Nature apparently employs quantum random walks, most notably in
its design of a key photosynthetic mechanism as the following news item
"Photosynthetic proteins are 'wired' together by quantum
coherence for more efficient light harvesting in cryptophyte marine
algae" says a report in Nature (Collini 2010).
It's referred to as nature's "quantum design for a light
trap." Seemingly the
photons involved explore all possible paths and pick the best one.
The previous week another group reported finding unexpected
long-lived quantum coherence at room temperature in photosynthetic
bacteria. (Engel 2010) They cite protection provided by a "protein matrix
encapsulating the chromophores," and assert "the protein
shapes the energy landscape and mediates an efficient energy transfer
despite thermal fluctuations."
How photons know the best path to take is one mystery physicists
seek to explain using quantum theory, another involves how the hundred
or more amino acids in proteins so quickly fold into the correct shape
to become biologically active. After
a stunning breakthrough in modeling why such folding depends on
temperature in such an unexpected way (Luo 2011), it seems clear that a
quantum approach is needed to understand this mystery. A third mystery
involves resolving the incompatibility between quantum field theories,
one being quantum electrodynamics in which the photon serves as field
particle, and the holographic principle. Basically field theories allow
an infinite number of degrees of freedom, whereas holography restricts
these to a finite number. According
to the latter, our universe has two alternate, but equivalent
descriptions (Bousso 2002). One
is provided by information that fills the 3D (or ND) volume of space,
the other by information stored on a 2D ([N-1] D) surface bounding the
volume. The descriptions
are equivalent, so the maximum amount of information that can be stored
in a region, or equivalently its entropy, depends on its surface area,
not volume. Could it be the
universe acts like a giant hologram with information transfer being the
Like holography, certain quantum phenomena suggest the
possibility of non-local information transfer.
From experimental tests of Bell's theorem, physicists
conclude that for two coherent, entangled particles, what happens
at one place to one of them can instantaneously affect the other, no
matter what distance separates them (Kuttner 2010).
Could it be in this virtual world of entangled photons, time does
not exist? A TOE could
clear up many mysteries involving information transfer, whether they
arise from seeming downward causation, quantum weirdness, etc.
A final one that deserves mention: how random matrix theory,
developed to model quantum fluctuations but increasingly applied to
diverse phenomena, hints at a "deeper law of nature" (Buchanan
Building Mechanisms for Improving Conceptual Models into a TOE
conceptualization process involves observing, abstracting, recalling
memories, discriminating, categorizing, etc.
As you grow, you steadily organize these concepts into conceptual
schemes, and put those schemes into a framework.
Gabora and Aerts seek to explain this worldview development
process using a model and a theory of concepts, known as SCOP, for State
COntext Property (Gabora and Aerts 2009).
They consider "how concepts undergo a change of state when
acted upon by a context, and how they combine."
After building a formalism that begins with a set of states the
concept can assume, and another set of relevant contexts, they identify
a theoretically possible (but in practice difficult to observe)
"ground state" of a concept as "the state of being not
disturbed at all by the context."
A context "may consist of a perceived stimulus or
component(s) of the environment...or entirely of elements of the
They add concept states together like a linear superposition of
quantum states, identify a "potentiality state...subject to change
under the influence of a particular context," and liken the change
of state associated with this to quantum state collapse.
I see their concept states as system states, and context states
as the environment. They go
on to define a cognitive state in an individual's mind as "a state
of the composition of all of [the] concepts and combinations of concepts
of the worldview of this individual," discuss how they employ SCOP
to study how "more elaborate conceptual integration" can be
achieved, and proclaim the worldview is "the basic unit of
evolution in culture."
I like the thought of competing worldviews.
Seems to me the competition will be decided on the basis of which
model best represents reality as measured by the ability to make useful
predictions over the time frame of interest.
And how well the conceptional system representation S fits the
real environment representation E.
The winner will be the worldview that minimizes the S-E
difference over the relevant path in conceptual space.
Perhaps a next step is translating that difference into energy,
or prediction error information counterpart, and applying the GOAP!
Putting it All Together
a recipe for using my imagined TOE to attack certain problems
Define the problem, gather data, define system and hierarchy.
Quantify system <===> environment relationship and build
Identify and attempt to quantify, uncertainties and
If modeling a system that learns from the environment like a CAS,
provide an internal model and provide for Bayesian updating.
Build in and quantify adaptive
mechanisms, feedback loops, autopoietic
This model will use the GOAP to optimize fit between system and
Refine the model by testing using related problems with known solutions.
Construct initial candidate (imagined optimum system) to use as input.
Create more by making slight alterations, combinations.
Test using Darwinian selection.
Let output dictate what steps need repeating, perhaps for another part
After many iterations, after runs for various subsystems if need be, the
model's output should converge on an optimum solution, specifying how
well the selected system adapts to the environment, gauged by
reproductive, perpetuative, or predictive success over time.
How fast steps 4)--6) above are carried out may depend on how (or if)
the model and the TOE use quantum techniques (quantum computing,
accessing virtual information, etc.).
At its core is optimization based on the Generalized Optimal
Action Principle (GOAP) and use
of Darwinian natural selection.
Given the amazing range over which these techniques are
potentially applicable, we might refer to the latter as Universal
Conceivably, it might be applied to quantum states (Quantum
Darwinism), genes (biological evolution), neural networks (brain),
conceptual frameworks (worldviews) or universes.
For the latter, Susskind cautions the cosmological natural
selection he describes doesn't involve competition among pocket
universes for resources. For
social problems, this recipe may not help--see "Dancing With
Systems" (Cook 2009)!
J. (2010) "The Life Simulator" in New Scientist, 19 June 2010
G. (1979) Mind and Nature: A Necessary Unity Dutton, New York, USA
R. (2002) "The Holographic Principle" in Reviews of Modern
Physics, 74: 825-874
M. (2010) "Random Matrix Theory" in New Scientist, 10 April
E. etal. (2010) "Coherently Wired Light-harvesting in
Photosynthetic Marine Algae at Ambient
Temperature" in Nature 463:
S. (2009) The Worldview
Literacy Book Parthenon
Books, Weed, NM USA
R. (1996) Climbing Mount Improbable
Norton, New York, USA
D. (1995) Darwin's Dangerous Idea
Simon & Schuster Publishers,
New York, USA
G. etal. (2010) "Long-lived Quantum Coherence in Photosynthetic
Complexes at Physiological
Temperature" arXiv:1001.5108v1 [physics.bio-ph]
R. (1963) Lectures in Physics, Vol. 1
Addison Wesley, Reading, MA USA
Feynman, R. , Hibbs, A. (1965) Quantum Mechanics and Path Integrals McGraw Hill, New York
K. (2010) "The Free Energy Principle: A Unified Brain Theory?"
in Nature Reviews Neuroscience advance online publication 13 Jan 2010
L, Aerts, D. "A Model
of the Emergence and Evolution of Integrated Worldviews" Journal of
Mathematical Psychology 53: 434-451
Grandpierre, A. (2007) "Biological Extension of
the Action Principle" NeuroQuantology,
J. (2003) "Theories of Everything and Hawking's Wave Function of
the Universe" in The Future
of Theoretical Physics and Cosmology Cambridge University Press, London, UK
D. Godel, Escher, Bach: an Eternal Golden Braid
Random House, New York 1979
J. (1995) Hidden Order: How Adaptation Builds Complexity Helix
Books, Reading, MA USA
J. (2003) "Quantum
Random Walks--An Introductory Overview" Contemporary Physics 44:
F. and Rosenblum, B. (2010) "Bell's Theorem" in The Physics
Teacher 48: 124-130
R. (1992) Complexity:
Life at the Edge of Chaos Macmillan Publishing Co. New York, USA
Luo, L. and Lu, J. (2011) "Temperature Dependence of Protein Folding Deduced from Quantum Transition" arXiv:1102.3748v1 [q-bio.BM]
P. (2008) "Emergent Phenomena and the Secrets of Life" in J.
Appl. Physiol 104:1844-1846
C., Thorne, K., and Wheeler, J. (1973) Gravitation W.H.
Freeman, San Francisco, USA
R. (1989) The Emperor's New Mind
Oxford University Press, Oxford, UK
I. 1997 The End of
Certainty The Free Press, New York, USA
E. (2004) “Gaia: Toward a Thermodynamics of Life” in Scientists
ed. Schneider, S. , etal. MIT Press, Cambridge, MA USA
E. and Kay, J. (1994) "Life as a Manifestation of the Second Law of
Mathematical and Computer Modeling 19 (# 6-8):
L. (2006) The Cosmic Landscape Little, Brown and Company, New York, USA
F., Maturna, H,, and Uribe, R. (1974) "Autopoiesis" in
BioSystems 5: 187-96
E. (2010) "On the Origin of Gravity and the Laws of Newton"
" arXiv:1001.0785v1 [hep-th]
N. (1948) Cybernetics MIT
Press Cambridge, MA USA
W. (2003) "Decohorence, Einselection, and the Quantum Origins of
the Classical" in Reviews of
Modern Physics 75: 715- 775
P. Cook, Project Worldview, Weed, NM 88354-0499 USA; firstname.lastname@example.org