Transcript Principles of Complex Systems How to think like nature Russ Abbott Does nature really think?

Principles of Complex Systems
How to think like nature
Russ Abbott
Does nature really think?
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Complex systems overview
Part 1.
Introduction and motivation.
Overview – unintended consequences, mechanism,
function, and purpose; levels of abstraction, emergence,
introduction to NetLogo.
Emergence, levels of abstraction, and the reductionist blind
spot.
Modeling; thought externalization; how engineers and
computer scientists think.
Lots of echoes and
Part 2.
repeated themes from
Evolution and evolutionary computing.
one section to another.
Innovation – exploration and exploitation.
Platforms – distributed control and systems of systems.
Groups – how nature builds systems; the wisdom of crowds.
Summary/conclusions – remember this if nothing else.
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Principles of Complex Systems:
How to think like nature
What “complex systems” means,
and why you should care.
Russ Abbott
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What we will be talking about.
Santa Fe Institute was founded in 1984.
“Complex systems” refers to an
anti-reductionist way of thinking
that developed in the 1980s in
Biology, Computer Science,
Economics, Physics, and other
fields. (The term complexity is
also used this way.)
– It is not intended to refer to a
particular category of systems, which
are presumably distinguished from
other systems that aren’t “complex.”
Isn’t that true
of all systems?
But if I had to define what a
“complex system” is …
– A collection of autonomous
elements that interact both with
each other and with their
environment and that exhibits
aggregate, ensemble, macro
behaviors that none of the
elements exhibit individually.
System: a construct or collection of
different elements that together
produce results not obtainable by the
elements alone. — Eberhardt Rechtin
You are in the business of
Systems Architecting of Organizations:
building “complex systems.” Why Eagles Can't Swim, CRC, 1999.
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A satellite in a geostationary orbit:
one of the simplest possible “complex systems”
Fixed with respect to the earth as a reference frame.
An “emergent” property
But nothing is tying it down.
No cable is holding it in place.
What is the environment?
3D space
period of the orbit = period of the earth’s rotation
Typical of complex system mechanisms.
Multiple independent or quasi-independent processes
— which are not directly connected causally (agents) —
interact within an environment to produce a result.
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Complex systems terms
• Emergence. A level of abstraction that can be
described independently of its implementation.
• Multi-scalar. Applicable to systems that are
understood on multiple levels simultaneously,
especially when a lower level implements some
functionality at a higher level.
Specification vs. implementation. Familiar?
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Why should you care?
Anne-Marie is keeping it hot here.
• Because it’s a hot (still warm?) topic. Most corporate executives have heard
the term. Some of them think it’s important.
– Rumsfeld’s inspiration for transformation in the military grew out of this way
of thinking. The field is not new. It’s at least 2 decades old.
– The Command and Control Research Program (CCRP) in the Pentagon
(Dave Alberts) is successfully promoting this style of thinking within the DoD.
– Complex systems thinking is a generalization of and the foundation for netcentric thinking—and the way the world has changed as a result of the web.
• You should understand what they are talking about
– So that you can explain it to them.
• Because it offers a powerful new way to think about how systems work.
• Because large systems—and especially systems of systems (another
important buzz-word)—tend to be complex in the ways we will discuss.
• Because the ideas are interesting, important, and good for you.
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What is a System of Systems?
Functional decomposition
Small stovepipes
to large stovepipes – NO
Level of
abstraction
Loosely coupled and tightly
integrated – YES
Platforms
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Planning Complex Endeavors (April 2007)
by David S. Alberts and Richard E. Hayes
Alberts’ term for what a
complex system does.
The Command and Control Research Program (CCRP)
has the mission of improving DoD’s understanding of
the national security implications of the Information Age.
• John G. Grimes, Assistant Secretary of Defense (NII) & Chief
Information Officer
• Dr. Linton Wells, II, Principal Deputy Assistant Secretary of
Defense (NII)
• Dr. David S. Alberts, Special Assistant to the ASD(NII) & Director
of Research
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From the forward by John G. Grimes
As this latest book from the CCRP explains, we can no longer be
content with building an “enterprise-wide” network that stops at the
edges of our forces, nor with a set of information sources and
channels that are purely military in nature. We need to be able to work
with a large and diverse set of entities and information sources. We
also need to develop new approaches to planning that are better
suited for these coalition operations.
The implications are significant for a CIO as it greatly expands the
who, the what, and the how of information sharing and collaboration.
What is this “new
way of thinking?”
It also requires a new way of thinking about
effectiveness, increasing the emphasis we place on agility, which, as
is explained in this book, is the necessary response to uncertainty and
complexity.
From Chapter 1. Introduction
Platforms
The economics of communications and information technologies has created enormous
opportunities to leverage the power of information and collaboration cost effectively by adopting
Power to the Edge principles and network-centric concepts.
Exploration and
exploitation
Fine to use these
terms, but what do we
really mean by them?
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From Chapter 2. Key Concepts
Complicated Systems
Systems that have many moving parts or actors and are highly
dynamic, that is, the elements of these systems constantly interact
with and impact upon one another. However, the cause and effect
relationships within a complicated situation are generally well
understood, which allows planners to predict the consequences of
specific actions with some confidence.
I think this misses the point. Most systems and interactions are (eventually) “well
understood.” Complicated systems are often fully entrained with one locus of control.
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From Chapter 2. Key Concepts
Both chaos and phase transitions
Complex Endeavors (Systems)
Complex endeavors involve changes and behaviors that
cannot be
I disagree—although
sometimes the only
way to predict it is to
run (a model of) it.
But that’s true of the
3-body problem too.
predicted in detail, although those behaviors and changes can be
expected to form recognizable patterns. Complex endeavors are also characterized by
small differences in initial conditions or
relatively small perturbations (seemingly tactical actions) are associated with very
large changes in the resulting patterns of
behavior and/or strategic outcomes.
circumstances in which relatively
Some complex situations develop into complex adaptive systems (CAS), which tend to
be robust—to persist over time and across a variety of circumstances. These are often
observed in nature in the form of
biological or ecological
systems. However, while these systems are thought of as robust, they can be
pushed out of balance even to the point of collapse through cascades of negatively
reinforcing conditions and behaviors. Such perturbations are what ecologists fear when
a habitat is reduced to an isolated geographic area or when invasive, nonnative
species are introduced.
Note biological reference
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Like many networks, this complex system
involves many independently operating
elements (the trains) that together enable one to
get from any one station to any other without a
massive number of point-to-point connections.
Simon Patterson is fascinated by the information which orders our lives. He humorously dislocates and subverts sources of information
such as maps, diagrams and constellation charts; one of his best known works is The Great Bear, in which he replaced the names of stations
on the London Underground map with names of philosophers, film stars, explorers, saints and other celebrities. By transforming
authoritative data with his own associations he challenges existing rationales.
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The world in a grain of sand
To see the world in a grain of sand,
and heaven in a wild flower,
to hold infinity in the palm of your hands,
and eternity in an hour. –William Blake
Do you understand what that means? It’s
profound, but it uses poetry to make its point.
A primary objective of this talk is to say in as plain a
way as possible what many people have been
groping for when talking about complex systems.
Many of the ideas may seem like common sense.
It’s just that we’ll be looking at them more closely.
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Introduction to Complex Systems:
How to think like nature
Unintended consequences;
mechanism, function, and purpose
Russ Abbott
This segment introduces
some basic concepts.
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A fable
• Once upon a time, a territory in India had too many snakes.
• To solve this problem the government instituted an incentivebased program to encourage its citizens to kill snakes.
• It created the No Snake Left Alive program.
– Anyone who brings a dead snake into a field office of the Dead
Snake Control Authority (DSCA) will be paid a generous Dead
Snake Bounty (DSB).
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The DSCA mechanism
Receive dead snake
certificate. Submit
certificate to DSCA.
DSCA
Receive
money.
Catch, kill, and submit
a dead snake.
Dead snake
verifier
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A fable (continued)
• A year later the DSB budget was exhausted. DSCA had paid
for a great many dead snakes.
• But there was no noticeable reduction in the number of
snakes plaguing the good citizens of the state.
• What went wrong?
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The DSCA mechanism
Receive dead snake
certificate. Submit
certificate to DSCA.
DSCA
What would you do if
this mechanism were
available in your world?
Receive
money.
Start a
snake
farm.
Catch, kill, and submit
a dead snake.
Dead snake
verifier
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Moral: unintended consequences
• A mechanism is installed in an environment.
• The mechanism is used/exploited in unanticipated ways.
• Once a mechanism is installed in the environment, it will
be used for whatever purposes “users” can think to make
of it …
– which may not be that for which it was originally intended.
The first lesson of complex systems
thinking is that one must always be
aware of the relationship between
systems and their environments.
A second lesson of complex
systems thinking is that
energy and energy flows are
fundamental—and money is a
proxy for energy.
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Mechanism,
Parasites that control their hosts
function,
and
• Dicrocoelium dendriticum causes host ants
purpose
to climb grass blades where they are eaten
by grazing animals, where D. dendriticum
More
lives out its adult life.
indirect
effects
• Toxoplasma gondii causes host mice not to
fear cats, where T. gondii reproduces.
• Spinochordodes tellinii causes host insects
to jump into the water and drown, where S.
tellinii grows to adulthood.
It’s amazing how far exploitation of
environmental mechanisms can go.
Talking about
dynamic entities.
Can’t have any of
this without energy.
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Locomotion in E. coli
• E. coli movements consist of
short straight runs, each lasting
a second or less, punctuated by
briefer episodes of random
tumbling.
• Each tumble reorients the cell and sets it off in a
new direction.
Exploration
Exploitation
• Cells that are moving up the gradient of an attractant
tumble less frequently than cells wandering in a
homogeneous medium or moving away from the source.
• In consequence, cells take longer runs toward the source
and shorter ones away.
Gain benefit
Harold, Franklyn M. (2001) The Way of the Cell: Molecules,
Organisms, and the Order of Life, Oxford University Press.
Microcosm, Carl Zimmer
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Mechanism, function, and purpose
• Mechanism: The physical processes within
an entity.
– The chemical reactions built into E.coli that result in its
flagella movements.
– The internal bureaucratic DSCA mechanism.
– The chemical mechanisms stimulated by the parasites.
• Function: The effect of a mechanism on the
environment and on the relationship between
an entity and its environment.
Wikipedia Commons
– E. coli moves about. In particular, it moves up nutrient
gradients.
– Snakes are killed.
– The actions of the hosts.
• Purpose: The (presumably positive)
consequence for the entity of the change in
its environment or its relationship with its
environment. (But Nature is not teleological.)
– E. coli is better able to feed, which is necessary for its
survival.
– Snake farming is encouraged?
– Survival and reproduction.
Socrates
Compare to Measures of Performance, Effectiveness, and Utility23
Teleology: building “purpose”
Nature
Designed
E.g., E. coli locomotion to food
E.g., Reduce snake population
• Evolve a new mechanism
• Identify a purpose (need)
• Experience the resulting
• Imagine how a function can
achieve that purpose
functionality
• If the functionality enhances
survival, keep the mechanism
• “Purpose” has been created
(and by definition achieved)
implicitly
Most of the design steps
require significant
conceptualization abilities.
• Design and develop a
mechanism to perform that
function
• Deploy the mechanism and
hope the purpose is achieved
In both cases, the world will be changed by the
addition of the new functionality.
The purpose is more likely to be achieved by
nature since it’s only a purpose if it succeeds.
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Nature
Mechanism
Function
Purpose
Design
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NetLogo: let’s try it
File > Models Library > Biology > Ants
Click Open
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Two levels of emergence
• No individual chemical reaction—or line of
code—inside the ants is responsible for
making them follow the rules that describe
their behavior.
• What the internal chemical reactions
together do is an example of emergence.
• No individual rule and no individual ant is
responsible for the ant colony gathering
food.
Colony results
Ant behaviors
Ant chemistry
Each layer is a
level of abstraction
• That the ants together bring about that
result is a second level of emergence.
Notice the similarity to layered
communication protocols
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Two levels of emergence
Applications, e.g., email, IM, Wikipedia
• No individual chemical reaction inside the
WWW
(HTML) — for
browsers
+ servers
ants
is responsible
making
them follow
the rules that describe
their behavior.
Presentation
• That the internal Session
chemical reactions
together do is an
example of emergence.
Transport
• No individual rule
and no individual ant is
Network
responsible for the ant colony gathering
Physical
food.
• That the ants together bring about that
result is a second level of emergence.
Colony results
Ant behaviors
Ant chemistry
Each layer is a
level of abstraction
Notice the similarity to layered
communication protocols
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Principles of Complex Systems:
How to think like nature
Emergence: what’s right and what’s
wrong with reductionism
Russ Abbott
Presumptuous?
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Emergence: the holy grail of complex systems
How macroscopic behavior arises from microscopic behavior.
Emergent entities (properties
or substances) ‘arise’ out of
more fundamental entities and
yet are ‘novel’ or ‘irreducible’
with respect to them.
Stanford Encyclopedia of Philosophy
http://plato.stanford.edu/entries/properties-emergent/
Plato
The ‘scare’ quotes identify
problematic areas.
Emergence: Contemporary Readings in Philosophy and Science
Mark A. Bedau and Paul Humphreys (Eds.), MIT Press, April 2008.
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The reductionist challenge
Well, I admit that I don’t know why. I don’t
even know how to think about why. I expect to
figure out why there is anything except
physics the day before I figure out why there
is anything at all.
Why is there anything except physics? — Fodor, 1998
If a higher level explanation can be related to
physical processes, it becomes redundant since
the explanatory work can be done by physics.
— Maurice Schouten and Huib Looren de Jong,
The Matter of the Mind, 2007
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Emergence
2008
Mark Bedau
Paul Humphreys
• Phenomena that arise from and depend on some more basic phenomena yet
are simultaneously autonomous from that base.
• When we finally understand what emergence truly is [we will know] whether
there are any genuine examples of emergence.
• How should emergence be defined? … irreducibility, unpredictability,
conceptual novelty, ontological novelty, supervenience?
• In what ways are emergent phenomena autonomous from their emergent
bases? … irreducible to their bases, inexplicable from them, unpredictable
from them, supervenient on them, multiply realizable in them?
• Does emergence necessarily involve novel causal powers, especially
powers that produce “downward causation?”
• Emergence … is simultaneously palpable and confusing.
• The very idea of emergence seems opaque, and perhaps even incoherent.
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Cosma Shalizi
http://cscs.umich.edu/~crshalizi/reviews/holland-on-emergence/
Someplace … where quantum field theory
meets general relativity and atoms and void
merge into one another, we may take “the
rules of the game” to be given.
Call this
emergence
if you like.
It’s a fine-sounding
word, and brings to
mind southwestern
creation myths in
an oddly apt way.
But the rest of the observable, exploitable order
in the universe
benzene molecules, PV = nRT, snowflakes, cyclonic
storms, kittens, cats, young love, middle-aged
remorse, financial euphoria accompanied with acute
gullibility, prevaricating candidates for public office,
tapeworms, jet-lag, and unfolding cherry blossoms
Where do all these regularities come from?
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[Starting with the basic laws of
physics] it ought to be possible
to arrive at … the theory of
every natural process, including
life, by means of pure deduction.
— Einstein
All of nature is the way it is …
because of simple universal laws,
to which all other scientific laws
may in some sense be reduced.
There are no principles of
chemistry that simply stand on their
own, without needing to be
explained reductively from the
properties of electrons and atomic
nuclei, and … there are no
principles of psychology that are
free-standing. — Weinberg
Living matter, while not
eluding the ‘laws of
physics’ … is likely to
involve ‘other laws,’
[which] will form just as
integral a part of [its]
science. — Schrödinger
The ability to reduce everything to simple fundamental laws
[does not imply] the ability to start from those laws and
reconstruct the universe. — —
Anderson
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The fundamental dilemma of science
Are there autonomous
higher level laws of nature?
Emergence
Fodor cites Gresham’s law.
The functionalist claim
The reductionist position
How can that be if everything can be reduced
to the fundamental laws of physics?
My answer
It can all be explained in terms
of levels of abstraction.
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Game of Life Gliders
A 2-dimensional cellular automaton. The Game of Life
rules determine everything that happens on the grid.
• A dead cell with exactly three live neighbors becomes alive.
• A live cell with either two or three live neighbors stays alive.
• In all other cases, a cell dies or remains dead.
The “glider” pattern
Nothing really moves. Just cells going on and off.
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The Game of Life
File > Models Library > Computer Science > Cellular Automata > Life
Click Open
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Gliders
• Gliders are causally powerless.
– A glider does not change how the rules operate or which cells will be
switched on and off. A glider doesn’t “go to a cell and turn it on.”
– A Game of Life run will proceed in exactly the same way whether one
notices the gliders or not. A very reductionist stance.
• But …
– One can write down equations that characterize glider motion and
predict whether—and if so when—a glider will “turn on” a particular cell.
– What is the status of those equations? Are they higher level laws?
Like shadows, they don’t “do” anything.
The rules are the only “forces!”
Good GoL website
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Game of Life as a Programming Platform
• Amazing as they are, gliders are also trivial.
– Once we know how to build a glider, it’s simple
to make as many of them as we want.
• Can build a library of Game of Life patterns and
their interaction APIs.
What does it mean to compute with shadows?
By suitably arranging these patterns,
one can simulate a Turing Machine.
Paul Rendell. http://rendell.server.org.uk/gol/tmdetails.htm
A second level of emergence.
Emergence is not particularly mysterious.
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Downward causation entailment
• The unsolvability of the TM halting problem entails the
unsolvability of the GoL halting problem.
– How strange! We can conclude something about the GoL because
we know something about Turing Machines.
– Yet Turing Machines are just shadows in the GoL world.
– And the theory of computation is not derivable from GoL rules.
“Reduce” GoL unsolvability to TM unsolvability
by constructing a TM within the GoL.
Paul Davies, “The physics of downward causation” in Philip Clayton
(Claremont Graduate University), Paul Davies (Macquarie/NSW/Arizona
State University), The re-emergence of emergence, 2006
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A GoL Turing machine …
• … is an entity.
– Like a glider, it is recognizable; it has reduced entropy; it
persists and has coherence—even though it is nothing but
patterns created by cells going on and off.
• … obeys laws from the theory of computability.
Reductionism holds. Everything that happens on a GoL grid is
a result of the application of the GoL rules and nothing else.
Computability theory is independent of the GoL rules.

… is a GoL phenomenon that obeys laws that
are independent of the GoL rules while at the
same time being completely determined by the
GoL rules.
Just as Schrödinger said.
Living matter, while not eluding the ‘laws of physics’
… is likely to involve ‘other laws,’ [which] will form
just as integral a part of [its] science. — Schrödinger
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