"george" wrote in message
news:W1xrd.701208$8_6.529964@attbi_s04...
http://www.eurekalert.org/pub_releas...-wet113004.php

Two hundred and fifty million years ago, ninety percent of marine species
disappeared and life on land suffered greatly during the world's largest mass
extinction.


This kind of research demonstrates the true ignorance
of the human race and objective science in general.


Scene.....

Someone is slowly pouring sand onto a sand pile, eventually
the sand becomes too steep and an avalanche occurs.


Questions.

1) Does anyone really care ...exactly... which grain of sand was
responsible for the avalanche?

2) Or should we try to figure out how to predict the avalanche
so we can get the flippin 'ell out of the way or prevent them?


This 'science' paper posted by George cares about question one, which
event...precisely... is responsible for some extinction event.
Which is a question only the galactically stupid would care to answer.
Or children playing in a sandbox. Which fits you? Without knowing
the following concepts, none of you can claim to have even a /beginning/
understanding of nature or reality.


Behold the secret of life, the universe and everything


"I have watched the long gestation of Investigations with some
apprehension but more anticipation. Its reach is gigantic, from the most
primitive origins of life to the macroeconomics of innovation. What
comes up in its grasp is original and stimulating. This is a must read
for anyone interested in the outer edges of understanding of the world
around us."--Philip Anderson, Nobel Laureate, Princeton University"




The Fourth Law of Thermodynamics


"Tentative beginning image: Adaptive Agents in ecosystems and econosystems
must create coevolutionarily constructable systems. To do so, such agents
tune internal redundancy and couplings with one another to achieve this.
In doing so, such systems may, as if by an invisible hand, generically tune
to a self organized critical state."




INVESTIGATIONS
THE NATURE OF AUTONOMOUS AGENTS
AND THE WORLDS THEY MUTUALLY CREATE
STUART A. KAUFFMAN
http://www.santafe.edu/sfi/People/ka...Lecture-1.html
http://www.santafe.edu/sfi/People/ka...tigations.html

SCOPE OF LECTURES: Search for a possible "fourth law"
of Thermodynamics for Non-Equilibrium Systems.

Aim of Lecture 1: Coevolutionarily constructable communities
of adaptive entities tune the structure and couplings of their
"fitness landscapes" to a self-organized critical state.


(selected excerpts)


1.6) INTERLUDE 2: SELF ORGANIZED CRITICALITY.


Bak, Teng, and Wiesenfeld proposed a general model of self organized
critical systems. The canonical example is a table onto which sand is continuously
and slowly poured. As the sand piles up to its rest angle, sand slides, or
avalanches, begin to occur. As sand is added at a steady rate, one considers
the size distribution of the avalanches. Many small avalanches and few large
avalanches occur. If one plots the logarithm of the number of avalanches at
each size on the ordinate and the logarithm of the size of the avalanche on
the abscissa one obtains a straight line sloping down to the right. Thus, the
size distribution is a power-law, frequency as a function of size of avalanche.


Bak and coworkers argue that many phenomena are self-organized critical.
Power-law distributions arise at phase transitions, but typically require tuning
of parameters to achieve the phase transition. Here, no external tuning of
parameters is required. The system self organizes to a critical, poised, state.


Bak and coworkers assemble evidence that earthquakes, forest fire models, the
game of life, and other systems exhibit self-organized critical behavior.



1.7) SECOND CLUE ABOUT HOW ORGANISMS CONTROL THE
STRUCTURE OF THEIR SEARCH SPACES:


COEVOLUTION: ORDER, CHAOS, AND COEVOLUTION TO
THE EDGE OF CHAOS.


i. Coevolution. The frog and the fly. Adaptive moves by the frog - sticky tongue
- alter the fitness of the fly and DEFORM its landscape. Fly should develop
slippery feet, sticky-stuff desolver, ...


As frog population climbs towards the peaks of the frog landscape, the fly
landscape deforms, and vice versa. Coupled deforming landscapes.


Unlike a fixed fitness landscape with a potential function - fitness, hence
with local peaks as point attractors, coevolutionary systems are general
dynamical systems. Such systems may have point attractors - an
Ordered Regime, or a Chaotic Regime.


ii. The two major regimes:


a. Evolutionary stable strategies - the ordered regime. Here, coevolving
partners climb to local peaks that are MUTUALLY CONSISTENT.
Each species, "player" is better off not moving as long as other "players"
do not move. Analog of Nash Equilibrium in game theory, here with
constraint on search distance from current position. ESS, Evolutionary
Stable Strategies, adds condition of non-invadability by other variants.


b. The Red Queen - the chaotic regime.


Here, each coevolving partner chases peaks that move away from it faster
than it can climb, each clambering forever uphill on deforming landscapes.
The total system of species - "players" - flows through large regions of
space of possibilities.


iii. NKCS Model of Coevolving Species.


a. Each species "lives" on an NK landscape, and is assumed to be isogenic
- hence occupies a single point on the landscape.


b. NK landscapes of each species is coupled to that of S other species.
For each coupled pair, say frog and fly, each of the N sites in the frog is
affected by K sites within the frog, and by "C" sites within the fly.
Reciprocally, each site in the fly is affected by K sites within the fly
and C sites within the frog. The effects are modeled by expanding the
random fitness function of each site in the frog to look not only at the
alleles of its K internal sites but the corresponding alleles of the C sites
of the fly (and vice versa). Random fitness values between 0.0 and
1.0 are added to these new combinations. Thus, when the fly, moves
on its landscape by changing the allele at one site, that change affects
the fitness contributions of C sites in the frog. Adaptive moves by flies
deform the landscape of the frog population, and vice versa.


c. The ordered regime: When K is large relative to the product C x S,
a model ecosystem of S species reaches a "Nash Equilibrium" where
each species attains a local peak and is better off not moving to any
1-mutant variant as long as the others do not move. This is the
analogue of an ESS.


d. The chaotic regime: When K is small relative to the product of
C x S, all species continue to be able to find fitter 1-mutant variants.
The total system flows through the product space of the NK
"genotypes" over very long times.


e. The Coevolutionary Edge of Chaos. When the parameters of the
NKCS model are tuned, for example, N, C, and S are held constant,
and K is varied from 0 to N - 1, the coevolutionary system is initially
in the ORDERED REGIME then switches to the CHAOTIC REGIME
at a critical value of K, Kcrit.


Thus, the coevolutionary system passes a PHASE TRANSITION
BETWEEN ORDER AND CHAOS - the EDGE OF CHAOS.


1) Evidence for the edge of chaos: Time for model ecosystem to
"freeze" onto Nash equilibrium very very long (unobservable) for
K Kcrit. Time to freeze is short for K Kcrit. System is freezing
slowly over observed time scale at Kcrit. (Note relevance of time
scale of observation here.)


f. The Highest Mean Fitness is Found at the Edge of Chaos!


1) As K increases from 0 to N - 1, fitness increases then decreases.
Maximum occurs just when system begins to freeze slowly over
observed time scale.


iv. Coevolution to the Edge of Chaos: Evidence of a Self-Organized
Critical State as Attractor.


a. Model the evolution of coevolution by generalizing NKCS model
to allow species to alter ruggedness of their own landscape by increasing
or decreasing K, and to invade one another's "niches."


b. At each moment, for each species, one of four things may happen: 1.
Species remains unchanged in genotype. 2. Species makes an adaptive
move to a fitter 1-mutant variant. 3. Species increases or decreases K
of all N genes by 1. 4. Another species, godzilla, sends a copy, godzilla
prime, to try to invade species niche by playing with species S neighbors.


c. At each moment, "fittest thing" happens. Thus, species may remain
unchanged, move ON its landscape, Change Ruggedness of landscape.
Or, if godzilla prime plays better with S neighbors, the "home" species
is declared extinct, and godzilla prime is instantiated as a new species
in that niche.


Note that invasion by species allows those with good landscape ruggedness,
K, to reproduce - godzilla has a daughter species, godzilla prime.
So landscape ruggedness can itself evolve via replicators.
Thus, landscape ruggedness can evolve without "group
selection" and hence


AS IF BY AN INVISIBLE HAND!


Note too that species number is held fixed. Each extinction event is
matched by a corresponding speciation event.


d. Expectation of AVALANCHES OF EXTINCTION EVENTS.
When godzilla prime invades a niche, its own fitness in the new niche
is likely to be low. In addition, the S partners of the now extinct species
were "used to" playing with it, not with godzilla prime. They are likely
to be less fit than before when playing with godzilla prime. Thus, godzilla
prime and its S partners are likely to be less fit, so more likely to be
INVADABLE by other species. Hence we expect avalanches of
extinction events propagating from godzilla prime.


e. Results show:


1) That K does evolve to an INTERMEDIATE VALUE, OR RUGGEDNESS
then fluctuate in a narrow band. Thus landscape smoothness is itself evolvable,
as if by an invisible hand.


2) That mean fitness increases, (and may be maximized).


3) That the probability of extinction DECREASES, (and may be minimized).


4) Many small and fewer large AVALANCHES of extinction events
propagate through the system.


5) The extinction avalanches show a power-law distribution.
Frequency of avalanches at a given size (number of species that
went extinct in avalanche) decreases as a power of the size
of the extinction "event."



1.8) CONCLUSIONS:


This is the first model hinting that a coevolutionary system, in which selection
acts only at the level of the INDIVIDUAL, hence, as if by an invisible hand,
can tune landscape smoothness to an intermediate value. Organisms can
therefore plausibly tune the statistical structure of their search spaces!


This is the first hint that such a system may achieve an analogue of a
"self organized critical state."


This is therefore, a first hint that such a self organized critical state may
be a GENERAL attractor for complex adaptive systems able to tune
the structure and couplings among their landscapes..

This tentative attractor will emerge as part of the candidate "law"
for thermodynamically open, self-constructing, coevolutionarily
assemblable systems.





Jonathan

s