Transcript Principles of Complex Systems: How to think like nature Part 2 Russ Abbott Sr.

Principles of Complex Systems:
How to think like nature
Part 2
Russ Abbott
Sr. Engr. Spec.
310-336-1398
[email protected]
 1998-2007. The Aerospace Corporation. All Rights Reserved.
1
Complex systems course overview
9:00–9:10.
9:10–9:25.
9:25–9:45.
9:45–9:55.
9:55–10:05.
10:05–10:15.
10:15–10:30.
10:30–10:45.
10:45–10:55.
10:55–11:00.
Introduction and motivation.
Unintended consequences – mechanism, function, and
purpose; introduction to NetLogo.
Emergence – the reductionist blind spot and levels of
abstraction.
Modeling; thought externalization; how engineers and
computer scientists think.
Break.
Evolution and evolutionary computing.
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.
2
Principles of Complex Systems:
How to think like nature
Evolution: how nature thinks
Russ Abbott
Sr. Engr. Spec.
310-336-1398
[email protected]
 1998-2007. The Aerospace Corporation. All Rights Reserved.
3
Peppered moths: evolution in action
• Originally, the vast majority of peppered moths
in Manchester, England had light coloration—
which camouflaged them from predators since
they blended into the light-colored trees.
• With the industrial revolution:
– Pollution blackened the trees.
– Light-colored moths died off.
– Dark-colored moths flourished.
• With improved environmental standards, lightcolored peppered moths have again become
common.
4
Try it out
File > Models Library > Biology > Evolution > Peppered Moths
Click Open
5
The evolutionary process
• There is a population of elements.
• The elements are capable of making copies
of themselves
– perhaps with variants (mutations) and
– perhaps by combining with other elements.
• The environment affects the likelihood of an
element surviving and reproducing.
• This results in “evolution by natural (i.e.,
environmental) selection.”
– Darwin likened it to breeding. The
environment plays the role of the breeder.
6
The nature of evolution
Nature is not necessary “red in tooth
and claw.” The dark and light moths
don’t compete directly with each other.
• “Survival of the fittest” doesn’t mean
survival of the strongest. It means
survival of those that best fit the
environment.
• There are no moth-on-moth battles.
• Nor do the dark moths attempt to
convince the light moths that it’s
better to be dark — or vice versa.
Moths (and their colors) are rivals, not
adversaries.
• It’s more like a race than a boxing
match.
• They are rivals with respect to their
ability to survive and acquire
resources from the environment.
Moth coloring confers survival value (fitness)—which depends on the environment.
• Hence Darwin’s “natural selection,” i.e., environmental selection.
• The environment selects the winners.
There may be multiple “winners.” All one needs is a niche, not domination.
7
Six time scales of evolution
• Biological evolution
is generally slow.
• Warfare: often super fast evolution.
• Social/economic/cultural systems
– IED tactics and counter tactics.
evolve at medium speeds.
– As rivals: a social system that does • Thought: thinking through options
well for its members thrives and
is even faster.
expands.
– Let one’s hypotheses die in
– As adversaries: social systems
one’s stead. —Karl Popper
sometimes compete for
resources—land in the past; now
other resources.
• Simulation:
computer
• Markets are evolution speeded-up.
modeling of
evolutionary
– Coke and Pepsi are rivals for
processes is
consumer dollars, not adversaries.
faster yet.
– They don’t attempt to kill each
other’s CEOs or to sabotage each
other’s delivery trucks.
8
Application to engineering problems
The Traveling Salesman Problem (TSP).
Connect the cities with a tour that is a
permutation of the cities.
A
20
• Starts and ends at the same city.
• Includes each city exactly once.
C
9
24
7
12
B
12
D
• The obvious tour will include the
sequence ACED-54 (or its reverse).
• No diagonals.
• The question is where to put B: ABCED55, ACBED-57, or ACEBD-56?
12
13
4
14
E
Why not n!
In this case the problem is easy to solve by inspection. In general, it’s
computationally explosive since there are (n-1)! possible tours.
9
Genetic algorithm approach
Create a population of random tours.
• AEBCD-59, ACBED-57, ADCBE-59, ACDEB-71, …
• In this case there are only 4! = 24 possible tours.
• Could examine them all. Usually that’s not possible.
20
A
An exchange (or reverse or mutation)
solves this problem in one step.
ACBED-57 → ABCED-55
C
9
24
7
12
B
12
D
12
13
4
14
E
Repeat until good enough or no improvement. But beware local optima.
• Select one or two tours as parents.
− Ensure that better tours are more likely to be selected.
• Generate offspring using genetic operators to replace poorer elements.
− Exchange two cities: ACDEB-71 → ACBED-57
− Reverse a subtour: ACBED-57 → AEBCD-59
− (Re)combine two tours: AEBCD-59 & ACBED-57 → AEDCB-71.
• Possibly mutate the result: ADCBE-59 → ACBDE-70
10
Try it out: TSP.jar
• After starting a run, double click in the display area to add a
city or on a city to remove it.
– New cities are added to the tour next to their nearest neighbor.
• Stop and restart for new random cities.
– The number of new cities will be the same as the number of old
cities.
• The differences between the current best and its predecessor
are shown by link color.
– New links are shown in green.
– Removed links are in dashed magenta.
• No “geographical” heuristics are used. Just the structural ones
shown on the previous slide.
11
Genetic algorithms: parameter setting/tuning
• The number of variables is constant.
– Both the TSP and the peppered moths examples illustrate genetic
algorithms.
• Peppered moths: one parameter (color) to set.
• TSP: N variables. As a parameter setting problem think of each
tour as consisting of N variables, each of which may contain any
city number. The additional constraint is that no city may repeat.
• Often there are hundreds of variables (or more) or the search space
is large and difficult to search for some other reason.
• There is no algorithmic way to find values that optimize
(maximize/minimize) an objective function.
Terrile et. al. (JPL), “Evolutionary Computation applied to the Tuning of
MEMS gyroscopes,” GECCO, 2005.
Abstract: We propose a tuning method for MEMS gyroscopes based on
evolutionary computation to efficiently increase the sensitivity of MEMS gyroscopes
through tuning and, furthermore, to find the optimally tuned configuration for this
state of increased sensitivity. The tuning method was tested for the second
generation JPL/Boeing Post-resonator MEMS gyroscope using the measurement of
the frequency response of the MEMS device in open-loop operation.
12
Genetic programming: design
• The number of variables (and the structure of the possible
solution) is not fixed.
• Original goal was to generate software automatically.
– Not very successful, but hence the name.
• Applied successfully to other design and analysis problems.
– Circuit design
– Lens design
Bongard and Lipson (Cornel), “Automated reverse engineering of nonlinear dynamical
systems,” PNAS, 2007.
Abstract: Complex nonlinear dynamics arise in many fields of science and engineering, but
uncovering the underlying differential equations directly from observations poses a
challenging task. The ability to symbolically model complex networked systems is key to
understanding them, an open problem in many disciplines. Here we introduce for the first time
a method that can automatically generate symbolic equations for a nonlinear coupled
dynamical system directly from time series data. This method is applicable to any system that
can be described using sets of ordinary nonlinear differential equations, and assumes that the
(possibly noisy) time series of all variables are observable. …
“Symbolic regression”
13
The Human-competitive awards: “Humies”
John Koza
• Each year at the Genetic and Evolutionary Computing Conference (GECCO), prizes
are awarded to systems that perform at human-competitive levels—including the
previous two slides.
– See http://www.genetic-programming.org/hc2005/main.html
• An automatically created result is considered “human-competitive” if it satisfies at
least one of the eight criteria below.
A. The result was patented as an invention in the past, is an improvement over a patented invention, or
would qualify today as a patentable new invention.
B. The result is equal to or better than a result that was accepted as a new scientific result at the time
when it was published in a peer-reviewed scientific journal.
C. The result is equal to or better than a result that was placed into a database or archive of results
maintained by an internationally recognized panel of scientific experts.
D. The result is publishable in its own right as a new scientific result — independent of the fact that the
result was mechanically created.
E. The result is equal to or better than the most recent human-created solution to a long-standing
problem for which there has been a succession of increasingly better human-created solutions.
F. The result is equal to or better than a result that was considered an achievement in its field at the time
it was first discovered.
G. The result solves a problem of indisputable difficulty in its field.
H. The result holds its own or wins a regulated competition involving human contestants (in the form of
either live human players or human-written computer programs).
14
Tom Lang:
Genetic Algorithm for Constellation Optimization (GACO)
• Finds optimal constellation orbits using a genetic
algorithm under multiple design constraints and
with multiple sensor types.
4 Satellite Constellations
Global Coverage, elmin = 0
4.5
For low number of sats, GA arrangement
is significantly better than Walker
Max Revisit Tim e/Orbital Period
4
3.5
3
2.5
GA
Walker
2
1.5
1
0.5
0
0
5
10
15
20
25
30
35
40
45
50
55
60
Earth Central Angle (degrees)
15
Principles of Complex Systems:
How to think like nature
Organizational innovation
Russ Abbott
Sr. Engr. Spec.
310-336-1398
[email protected]
 1998-2007. The Aerospace Corporation. All Rights Reserved.
16
Innovative environments
Net-centricity and the GIG
• Inspired by the web and the internet
• Goal: to bring the creativity of the web and the internet
to the DoD
Other innovative environments
• Market economies
• Biological evolution
• The scientific and technological research process
What do innovative environments have in common?
How can organizations become innovative?
17
The innovative process: exploration and exploitation
If I were to give an award for the single best idea anyone has ever had, I'd
give it to Darwin, ahead of Newton and Einstein and everyone else.
In a single stroke, the idea of evolution by natural selection unifies the
realm of life, meaning, and purpose with the realm of space and time,
cause and effect, mechanism and physical law. Daniel Dennett, Darwin's Dangerous Idea
Innovation, including human creativity, is always the result of an
evolutionary process.
• Generate new variants (e.g., ideas)—typically by combining and
modifying existing ones.
– This is a random process in nature.
The easy part!
– But random or not isn’t the point.
– The point is to generate lots of possibilities, to explore the landscape.
• (Select and) exploit the good ones
– Allow/enable the good ones to flourish.
The hard part!
18
Exploration and exploitation in nature
• Evolution.
• E. Coli navigation.
• The immune system.
• Ant and bee foraging.
• Termite nest building (to come).
• Building out the circulatory and nervous systems.
19
Exploration and exploitation:
like water finding a way down hill
Microbes attempting to get into your body must first get past your skin
and mucous membranes, which not only pose a physical barrier but are
rich in scavenger cells and IgA antibodies.
Next, they must elude a series of nonspecific defenses—and substances
that attack all invaders regardless of the epitopes they carry. These
include patrolling phagocytes, granulocytes, NK cells, and complement.
Infectious agents that get past these nonspecific barriers must finally
confront specific weapons tailored just for them. These include both
antibodies and cytotoxic T cells.
From a tutorial on the immune system from the National Cancer Institute.
Quite a challenge! We are very well defended. But we still get sick!
If there is a way, some will inevitably find it. (Murphy's law?)
The trick is to make the inevitability work for you, not against you.
20
Exploration, exploitation, and asymmetric warfare
• It is the nature of complex systems
and evolutionary processes that
conflicts become asymmetric.
• No matter how well armored one is …
• there will always be chinks in the
armor, … and something will
inevitably find those chinks.
• The something that finds those
chinks will by definition be
asymmetric since it attacks the
chinks and not the armor.
21
Exploration and exploitation:
groups and individuals
• Successful group exploration typically requires
multiple, loosely coordinated, i.e., autonomous,
individuals.
– That’s because nature is not regular; one can’t
fully plan an exploration.
– If one knew in advance what the landscape
looked like, it wouldn’t be an exploration.
• Much exploration is wasted effort.
– One may hit the jackpot while the others find
nothing.
22
Exploration and exploitation:
groups and individuals
• For a group to benefit from the discoveries of
individuals, there must be mechanisms to bring the
discoveries back and allow the group them to use them.
– Mechanisms to internalize successful/promising
discoveries must be built into a group’s process.
– This frequently requires “creative destruction,” which
may be more difficult to accept—especially if it’s your
job that is being destroyed.
– Markets are how we integrate creative destruction into
society.
It’s amazing how well we have tamed destruction.
It’s now an accepted part of our normal processes.
Joseph Schumpeter,
Capitalism, Socialism, and Democracy
Recall ant foraging and pheromone following.
23
How does this apply to organizations?
To ensure innovation:
Exploration: creation and trial
• Encourage the prolific generation and trial of new
ideas.
Exploitation: establish the successful variants
• Allow new ideas to flourish or wither based on
how well they do—rather than for political reasons.
Sounds simple doesn’t it?
24
Innovation in various environments
New ideas
aren’t the
problem.
Biological
evolution
Entrepreneur
Bureaucracy
Trying them out
Initial funding
Prospect of
failure
Capitalism in
the small.
Nature always
experiments.
Most are failures,
which means
death. (But no
choice given.)
Little needed
for an Internet
experiment.
Perhaps some
embarrassment,
time, money; not
much more.
Proposals,
competition,
forms, etc.
Approvals
Getting
good ideas
Establishment
established
None.
Bottom-up
resource
allocation
defines
success.
Few.
Entrepreneur
wants rewards.
Bottom-up
resource
allocation.
When 100%
Managers have
Mission Success
Far too
other priorities.
is the group goal,
many.
Top-down
who wants a
resource
We save ourselves
failure in his/her by spin-doctoring
allocation.
personnel file? and benign neglect
25
Garages and laboratories, workbenches, and scribbled
napkins are filled with brilliant ideas unmatched with
determination, resources, and market sensibilities.
Jack Russo, Silicon Valley intellectual-property lawyer.
• In 1999, when Nathan Myhrvold left Microsoft (formerly CTO; brilliant, but
he missed the importance of the web) he set himself an unusual goal.
• He wanted to see whether the kind of insight that leads to invention could
be engineered.
• He formed a company called Intellectual Ventures.
• He raised hundreds of millions of dollars.
• He hired the smartest people he knew.
• It was not a venture-capital firm.
– Venture capitalists fund existing insights. They let the magical process that
generates new ideas take its course, and then they jump in.
• Myhrvold wanted to make insights—to come up with ideas, patent them,
and then license them to interested companies.
Malcolm Gladwell (May 12, 2008) “In the Air,” The New Yorker, http://www.newyorker.com/reporting/2008/05/12/080512fa_fact_gladwell
Matt Richtel (March 30, 2008) “Edison ...Wasn’t He the Guy Who Invented Everything?,” New York Times,
http://www.nytimes.com/2008/03/30/weekinreview/30richtel.html
26
Planned invention?
• When Myhrvold started out, his expectations were modest.
• Although he wanted insights like Alexander Graham Bell’s, Bell was
clearly one in a million, a genius who went on to have ideas in an
extraordinary number of areas—sound recording, flight, lasers,
tetrahedral construction, and hydrofoil boats, to name a few.
• Invention has its own algorithm—some combination of genius, obsession,
serendipity, and epiphany. How can you plan for that?
• The original expectation was that I.V. would file a hundred patents a year.
• It’s filing five hundred a year and has a backlog of three thousand ideas.
• It just licensed off a cluster of patents for $80,000,000.
• Its ideas are not trivial.
–
–
–
–
Why aren’t we doing this?
Improved jet engines
New techniques for making microchips
A way to custom-tailor the mesh “sleeve” used to repair aneurysms
Automatic, battery-powered glasses, with a tiny video camera that reads the
image off the retina and adjusts the fluid-filled lenses accordingly, up to ten
times a second.
27
VC arithmetic
1000
100
Ideas
10
H
om
e
ru
n
de
d
Fu
n
ng
Pr
om
isi
R
It’s hard work to harness
the power of innovation.
aw
1
28
Practical organizational innovation
Hamel and Skarzynski: an innovation architecture.
Do we have
an innovation
architecture?
•
An innovation pipeline for managing and opportunities
•
A core set of people trained in the processes of innovation
•
A systematic process for generating and managing strategic insights
•
The right evaluative criteria at every stage of the development process
to prevent potentially valuable ideas from being killed off prematurely
•
Ideas that are sufficiently radical to deliver breakthroughs
•
Mechanisms for rapidly reallocating resources behind new opportunities
•
Mechanisms to manage growth opportunities with different timescales
and risk profiles
Most companies want to make money.
What metrics would we use?
•
Metrics to measure innovation performance
•
Linkages between innovation and management compensation
•
A self-sustaining enterprise capability and a tangible core value
Prediction (20 years). To survive outside a protected environment an
organization will need a successfully functioning innovation architecture.
Corollary. Some organizations will focus on preserving their environments.
29
Successful innovative organizations: W.L. Gore, Best
Buys, Whole Foods, GE, Whirlpool, P&G, CEMEX, Google
• Lower levels discover opportunities through exploration.
– New initiatives often grow from the “edges,” where perception occurs.
– Constrained by “rules of engagement,” which protect them from harm.
– Must be possible for initiatives to originate at all levels—even the top.
• Higher/broader levels provide perspective, impose constraints,
shape direction, and add or withhold resources as events develop.
– They do not primarily issue commands.
• This is primarily a bottom-up model of resource allocation.
– Decisions about increasingly significant commitments are made at
increasingly higher/broader levels. If the entire organization commits,
becomes an organization/organism-level goal.
• Top-level strategy: stay healthy and build skills, resources, and
capabilities that can be recruited/applied/committed when needed.
Just what your mother always told you: eat right, exercise,
get plenty of sleep, study hard, practice, and save money.
30
Innovation in the military
• Our military is deliberately mission driven—where the
missions are determined by civilian authority.
• We don’t want our military to take the initiative to find
new missions for itself.
• What kinds of innovation does it make sense for the
military to attempt?
– Innovation that make it more effective at doing what it
is charged to do.
– How can success be made self-validating in the way
making money or reproducing are?
• Innovations that save lives (e.g., anti-IED techniques)
are self-validating and are adopted relatively quickly.
• Need a way to aggregate resources/success bottom-up.
– Establish a military-specific innovation architecture.
31
Principles of Complex Systems:
How to think like nature
Design: platforms, stigmergy, systems
of systems, and distributed control
Russ Abbott
Sr. Engr. Spec.
310-336-1398
[email protected]
 1998-2007. The Aerospace Corporation. All Rights Reserved.
32
The basic contrast
Hierarchical control and
functional decomposition
Agent interaction and
stigmergic/platform design
• Independent components,
which perform their
functions without interacting
and produce results to be
used at the next higher level.
• Independent components,
which interact both directly
and indirectly through an
environment (platform).
• A tree structure.
– Each node a potential
single point of failure.
• Focus is on component
functionality.
• The platform tends to be
distributed.
• Focus on communication
mechanisms. The more
useful the platform, the
more productive the system.
– Telephone vs. Internet. Both
are platforms. The Internet
is more powerful.
33
How would you gather wood chips into a pile?
Probably not like this.
File > Models Library > Biology > Termites
Click Open
34
Termite rules
• Wander about aimlessly (randomly) until
you bump into a wood chip.
– If you are not holding a wood chip
• Pick up the new chip.
• Move away from your current location.
• Go back to wandering about aimlessly.
– If you are holding a wood chip
Wikipedia commons
• Put down your chip in a nearby empty space.
• Move away from your current location.
• Go back to wandering about aimlessly.
Net effect: wood chips are deposited near other
wood chips, eventually forming a single pile.
Run the program and watch what happens.
Wikipedia commons
Exercise: prove that this will always happen
35
Organizational/system structure:
What’s wrong with this picture?
Downward pointing arrows: commands.
Upward pointing arrows: results/reports.
Can be implemented with
point-to-point communication links.
No horizontal communication.
No dashed lines. (Is that good?)
It’s not accurate as a
communication or
operational structure.
It may represent how authority is delegated,
and it may represent how responsibility is assigned,
but it doesn’t represent how communication occurs
or how organizations really work.
36
Organizational/system structure:
What’s wrong with this picture?
Downward pointing arrows: commands.
Upward pointing arrows: results/reports.
Can be implemented with
point-to-point communication links.
No horizontal communication.
No dashed lines. (Is that good?)
It’s not accurate as a
communication or
operational structure.
Functional decomposition
Recall Wanda Austin’s warning
not to combine small stovepipes
into large stovepipes.
It may represent how authority is delegated,
and it may represent how responsibility is assigned,
but it doesn’t represent how communication occurs
or how organizations really work.
37
A somewhat more realistic picture
The focus is on interaction among
participants in the organization.
Everything is both an
entity and a group.
David Sloan Wilson,
Evolution for Everyone
38
From point-to-point links to platforms
The communication system
(even if just a telephone system)
is the start of net-centricity
Need more than fixed point-topoint communication channels
Becomes reified as an
additional component—not
just a collection of interfaces.
But a network/platform
may do nothing on its own.
Platform”
“
Must distinguish between
communication structure
and command hierarchy.
Enabling communication neither
eliminates responsibility nor
undermines command intent.
As a common
resource, how does it
fit into the hierarchy?
How is it governed?
The fundamental question
How will the organization
use the network/platform?
39
Layered architectures — not functional decomposition
Each layer is a platform that
a) is built on the layers below it
b) enables higher level layers
to be built on top of it
c) is vulnerable to disruption.
Asymmetric
warfare
Applications, e.g., email, IM, Wikipedia
WWW (HTML) — browsers + servers
Presentation
Session
Transport
Network
Physical
40
How does Aerospace send mail to Aerospace?
A system of (many!) systems.
• Aerospace, USPS, commercial airlines,
airports, traffic, road maintenance, …
El Segundo
mail routes
(carts)
Sort &
route
Aerospace
USPS
(trucks)
Many interlinked processes.
Sort &
route
LAX ↔ IAD
(commercial
aircraft)
Sort &
route
The various systems
provide platforms for
each other.
Aerospace
USPS
(trucks) Sort &
route
Infrastructure
Chantilly
mail routes
(carts)
41
Multi-sided software platforms
Invisible Engines: How
Software Platforms Drive Innovation and Transform
Industries, MIT Press. (freely downloadable)
• Evans, Hagiu, and Schmalensee (2006)
– Operating systems, the web browser.
• Markets/mechanisms that connect disparate groups.
– A stock exchange matches buyers and sellers.
– A credit card system matches merchants and cardholders.
– Shopping centers, dating websites, TV channels, TV talk shows,
Amazon resellers, telephone & telegraph systems.
– Large retail stores (Wal-Mart, supermarkets) “rent” shelf space.
• Not your usual business model: buy; add value; sell.
• The value to each group increases as the size of the
other group(s) grow. (Also known as network effect.)
42
Platforms as refactorings
A multi-sided platform may be understood as the
standardization and factoring out (refactoring) of a
hard part of an interaction and providing it as a service.
The hard part
is done by the
platform.
 USPS: sending & receiving materials.
 Credit card: paying and being paid.
 Dating service: finding the other
party and making an initial contact.
 Robert’s Rules of Order: the
interaction protocol
43
Standards as (ephemeral) platforms
• Since a platform is a level of abstraction, it can be
characterized by a specification.
• The specification can then serve as the definition of
the platform, e.g., HTML, SQL, … .
• Multiple vendors can be encouraged to compete to
implement it.
• Defangs platform owners.
• Empowers platform users.
• Some platforms are single-sided: programming
languages, automobile & public transportation
system, (woodworking, etc.) tools. Have similar value.
44
Platforms as infrastructure and environments
• Sometimes platforms define an environment.
• The free market economic system is defined primarily
by two platforms.
– The monetary and banking system.
• Factors out the economic notion of value. Allows value to be
abstracted, stored, exchanged with minimal overhead.
– The legal and judicial system.
• Factors out agreements (contracts) and enforcement
mechanisms. Overhead not so minimal (lawyers) — but
better than hiring your own “enforcers.” Used to rely more on
reputation. Still do in eBay.
• Much too important to be controlled privately.
• In general, the set of platforms available in an
environment is the environment’s infrastructure.
45
Governance and change
• Once a platform has been established, what
mechanisms are available so that it can evolve as
needed?
– Since its use is embedded in the workings of many
users, it’s difficult to change.
– Since its use is central to the survival of many users
it must be able to change as needed.
• Platforms
– Open at the bottom. It’s the interface that matters,
not the implementation.
– Open at the top. New uses are encouraged.
– Stable but slowly changing.
• Must be stable enough to be relied on but flexible
enough to change as needed.
46
An unusual platform-based design
• E. coli can produce lactase which digests lactose.
• But for efficiency sake it should produce lactase
only when lactose is present.
• Imagine that you were asked to design a system
that would produce a product only under certain
conditions.
• How would you do it?
47
A (quasi-top-down) functional analysis solution
Lactose
sensor
Off
Switch
On
Lactase
production
system
The unasked questions
• How does one know one can
build these pieces?
• What enables the interfaces?
• What holds it all together?
Is it really top-down?
Assumes platforms of
components and framework.
Engineers can always design
a lower level if needed.
48
RNA polymerase
can’t bind to DNA.
Transcription
blocked.
E. coli lets lactose flip its own switch
lac operon
Three lac genes
RNAP
Repressor
Lactose itself binds to
the repressor, pulling
it out of the way.
Where’s the platform?
The DNA → protein
processing system.
Like the Aerospacedelivers-mail example.
lacX
lacY
lacA
RNAP
lacX
lacY
RNAP lacX
lacA
lacY
lacA
RNA polymerase can now bind
to DNA. Transcription enabled.
The genes are expressed.
• It’s often said that a first step in systems engineering is to agree on the system boundaries.
• What are the system boundaries in this case?
See movie http://pages.csam.montclair.edu/~smalley/LacOperon.mov.
49
Principles of Complex Systems:
How to think like nature
Organizations: how nature builds
systems; the wisdom of crowds
Russ Abbott
Sr. Engr. Spec.
310-336-1398
Innovation required
individual autonomy.
What do groups add?
[email protected]
 1998-2007. The Aerospace Corporation. All Rights Reserved.
50
“Self-organizing” groups
• Craig Reynolds wrote the first flocking
program two decades ago:
http://www.red3d.com/cwr/boids.
• Here’s a good current interactive
version:
http://www.lalena.com/AI/Flock/
– Separation: Steer to avoid crowding birds of the
same color.
– Alignment: Steer towards the average heading of
birds of the same color.
– Cohesion: Steer to move toward the average
position of birds of the same color.
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“Self-organizing” groups:
how nature builds systems
• The bird, termite, and ant models illustrate emergence (and
multi-scalarity). (See video Debora Gordon on ant colonies.)
• In both cases, individual, local, low-level rules enabled “the
group” to achieve “emergent” higher level results.
– The birds flocked.
– The wood chips were gathered into a single pile.
– The food was brought to the nest.
• These systems are the product of the evolution of individual
actions that resulted in coordinated benefits.
Emergence is successful group design.
Virtually everything
is both an entity
and a group.
• Group exploration extends the
perceptual reach of any individual.
• Group behavior extends the functional
capability of any one individual.
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Breeding groups
Traditional evolutionary theory says there is no such
thing as group selection, only individual selection.
Bill Muir (Purdue) demonstrated that was wrong.
http://www.ansc.purdue.edu/faculty/muir_r.htm
• Chickens are fiercely competitive for food and water.
• Commercial birds are beak-trimmed to reduce
cannibalization.
• Breeding individual chickens to yield more eggs
compounds the problem. Chickens that produce
more eggs are more competitive.
• Instead Muir bred chickens by groups.
Wikipedia commons
• At the end of the experiment Muir's birds' mortality rate was 1/20 that of
the control group. His chickens produced three percent more eggs per
chicken and (because of the reduced mortality) 45% more eggs per
group.
• Group (and more generally multi-level) selection is now accepted as valid.
Groups are entities. You and I are both entities and cell colonies.53
Why groups? Two steps.
• Perhaps groups formed initially because they increased survival
value. A team will generally beat an individual of approximately the
same skill level.
– This is not so much emergence as power in numbers.
• But then groups found that coordination,
specialization, and coordinated specialization
enabled emergence.
– Consider any multi-cellular organism, or any
organism with multiple organs, or any society
with any sort of specialization, or any social
grouping with coordinated and/or specialized
roles.
– These groups exemplify real emergence.
Entirely new capabilities appear.
•
•
Wind instruments can play melodies.
Piano and guitar can play chords as well.
54
David Sloan Wilson on social groups
Moral systems are interlocking sets of values, practices,
institutions, and evolved psychological mechanisms that
work together to suppress or regulate selfishness and
make social life possible. —Jonathan Haidt
• What holds for chickens holds for other groups as well: teams, military units,
corporations, religious communities, cultures, tribes, countries.
• Successful groups are those that minimize within-group conflict and organize
to succeed at between-group conflict. We evolved to be pro-social within groups but
xenophobic between groups. – Michael Shermer
• Groups with mechanisms for working together can often accomplish far more
(emergence) than the sum of the individuals working separately.
– Corporations, military organizations; reproduction; mitochondria and “us.”
• But if a group good is also an individual good (e.g., money, security), the
group must have mechanisms to limit cheating (free-ridership).
• Group traits (although they are carried as rules by individuals) evolve
because they benefit the group. (E.g., insect behavior.)
• These traits may be transmitted genetically (by DNA). They may also be
transmitted culturally (by training/parenting/indoctrination/mentoring/…).
– Human groups can be more complex because it’s not all built-in.
55
Experimental “games”
C
C
D
3/3
0/5
• Prisoner’s Dilemma.
D
5/0
1/1
– One shot. Defect is the only rational strategy.
– Iterated.
• Tit-for-tat: Cooperate initially and then copy the other guy.
• Pavlov: repeat on success; change on failure. (More robust.)
A far-from-equilibrium system. New energy is supplied “for free.”
• Ultimatum Game. Proposer must offer to divide $100—e.g., from TAI.
Responder either accepts the proposed division or rejects it—in which
case neither gets anything.
– Only rational strategy: proposer offers as little as possible; responder
always accepts.
– Real experiments (world-wide). Responder rejects unless offer ~1/3.
– Some societies are different, e.g., where giving a gift means power.
– What would you offer/accept? Try it. (Played anonymously. Write offer.)
• Try it table against table. Each table prepares an offer.
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Homo economicus vs. strong reciprocity
Homo economicus: individual selection
• Agents care only about the outcome of an economic interaction
and not about the process through which this outcome is
attained (e.g., bargaining, coercion, chance, voluntary transfer).
• Agents care only about what they personally gain and lose
through an interaction and not what other agents gain or lose (or
the nature of these other agents’ intentions).
• Except for sacrifice on behalf of kin, what appears to be altruism
(personal sacrifice on behalf of others) is really just long-run
material self-interest.
• Ethics, morality, human conduct, and the human psyche are to
be understood only if societies are seen as collections of
individuals seeking their own self-interest.
Moral Sentiments and Material Interests: The Foundations of Cooperation in Economic Life
Herbert Gintis, Samuel Bowles, Robert T. Boyd, and Ernst Fehr (eds), MIT Press, 2005.
57
Homo economicus vs. strong reciprocity
Strong reciprocity: group selection
• A predisposition to cooperate with others, and to punish (at personal cost, if
necessary) those who violate the norms of cooperation
– even when it is implausible to expect that these costs will be recovered
at a later date.
• Strong reciprocators are both
conditional cooperators
They behave altruistically as long as others are doing so as well.
and
altruistic punishers
They apply sanctions to those who behave unfairly even at a cost to
themselves.
Socialization: norm internalization.
There's no such thing in biology, economics, political science, or anthropology.
Humans can want things even when they are costly to ourselves because
we were socialized to want them
to be fair, to share, to help your group, to be patriotic, to be honest, to be
trustworthy, to be cheerful.
58
Wise crowds: more than the sum of their parts
Web wise crowd platforms
• Wikis
Traditional wise crowds
• Teams
• Juries
• Democratic voting
• Mailing lists
• Chat rooms
• Prediction markets
•
•
•
•
Condorcet Jury Theorem (18th century) example
Five people (a small crowd).
Each person has a 75% chance of being right.
Probability that the majority will be right: ~90%
With 10 people: ~98%. Simple if you think about it.
59
Wise crowd criteria
James Surowiecki, The Wisdom of Crowds
Diverse: different skills and information brought to the table.
• Decentralized and with independent participants:
Participant
autonomy.
• No one at the top dictates the crowd's answer.
•
•
Each person is free to speak his/her own mind and make own decision.
Distillation mechanism: to extract the essence of the crowd's wisdom.
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Example from The Difference
Which person from the following list was not a member of the Monkees (a 1960s pop
band)?
Diverse groups of problem solvers outperformed the groups of the
(A) Peter Tork
best individuals at solving problems. The diverse groups got stuck
(B) Davy Jones
less often than the smart individuals, who tended to think similarly.
(C) Roger Noll
(D) Michael Nesmith
Imagine a crowd of 100 people with knowledge distributed as follows:
• 7 know all 3 of the Monkees
• 10 know 2 of the Monkees
• 15 know 1 of the Monkees
• 68 have no clue
In other words, less than 10 percent of the crowd knows the answer, and over twothirds are culturally deprived of any Monkees knowledge. We assume individuals
without the answer vote randomly. The Condorcet Jury Theorem, then, doesn’t apply
because only a small minority knows the answer. Still, the crowd will have no problem
getting the right answer.
• The 7 who know all the Monkees vote for Noll;
• 5 of the 10 who know 2 of the Monkees will vote for Noll;
• 5 of the 15 who know 1 of the Monkees will vote for Noll; and
• 17 of the 68 clueless will vote for Noll.
So Noll will garner 34 votes, versus 22 votes for each of the other choices.
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A wise crowd as assistant and companion
62
Distillation mechanism: prediction markets
Statement issued by 25 world-famous academics. May 2007.
Including: Kenneth Arrow, Daniel Kahneman, Thomas Schelling,
Robert Shiller, Cass Sunstein.
Abstract: Prediction markets are markets for contracts that yield
payments based on the outcome of an uncertain future event, such as a
presidential election. Using these markets as forecasting tools could
substantially improve decision making in the private and public sectors.
We argue that U.S. regulators should lower
barriers to the creation and design of prediction
markets by creating a safe harbor for certain
types of small stakes markets. We believe our
proposed change has the potential to stimulate
innovation in the design and use of prediction
markets throughout the economy, and in the
process to provide information that will benefit
the private sector and government alike.
63
Often Beats Alternatives
• Vs. Public Opinion
– I.E.M. beat presidential election polls 451/596 (Berg et al ‘01)
– Re NFL, beat ave., rank 7 vs. 39 of 1947 (Pennock et al ’04)
• Vs. Public Experts
– Racetrack odds beat weighed track experts (Figlewski ‘79)
• If anything, track odds weigh experts too much!
– OJ futures improve weather forecast (Roll ‘84)
– Stocks beat Challenger panel (Maloney & Mulherin ‘03)
– Gas demand markets beat experts (Spencer ‘04)
– Econ stat markets beat experts 2/3 (Wolfers & Zitzewitz ‘04)
from Robin Hanson
• Vs. Private Experts
– HP market beat official forecast 6/8 (Plott ‘00)
– Eli Lily markets beat official 6/9 (Servan-Schreiber ’05)
– Microsoft project markets beat managers (Proebsting ’05)
64
Split off from
TradeSports
Prediction markets
Contracts: Intrade (Ireland-based): real money or play money.
But, there is evidence that prediction markets are not efficient.
Panos Ipeirotis
Slate’s
Election
Market
Page
65
Concerns and Myths
• Self-defeating prophecies
from Robin Hanson
• Decision selection bias
• Price manipulation
• Rich more “votes”
• Inform “enemies”
• Share less info
The prediction markets got
both the New Hampshire and
California primaries wrong.
• Combinatorics
• Risk distortion
• Moral hazard
• Alarm public
• Embezzle
• Bubbles
• Bozos
• Lies
• Crowds don’t always beat experts.
• People will not work for trinkets.
• High accuracy is not assured.
66
Other distillation mechanisms:
making the crowd’s “wisdom” “actionable”
A hard problem. Yet evolution
and markets do it automatically.
• Elections, polls, etc. Traditional. Many possible
processes, e.g., transferrable ballots, etc.
– Expression of preferences.
– Many online options (and more options).
• Collaboration: wikis and other collaboration tools
(shared spaces), mailing lists, chat rooms, etc.
– Explicit: Generation of new “work products.”
• Here’s a (long!) list of collaborative work environments.
– Implicit: Google’s page rank, “reputations” (e.g., eBay),
“recommendation engines” (e.g., Amazon)
67
Principles of Complex Systems:
How to think like nature
Remember this …
Russ Abbott
Sr. Engr. Spec.
310-336-1398
[email protected]
 1998-2007. The Aerospace Corporation. All Rights Reserved.
68
Complex systems
• Emergence: the creation of a new entity, one which has new properties (often a
group or a system), through the interaction among multiple autonomous elements.
– Multiscalarity: everything is both an entity and a group.
• A level of abstraction has both a specification (requirements) and an implementation.
– Throwing away the specification once an implementation exists produces a
reductionist blind spot.
– It’s the specification (of the interface) that ensures loose coupling.
• Interaction—even (or especially) intra-system—occurs through an environment.
– An environment that provides functionality that facilitates interaction is a platform.
– Architectures: agents and platforms vs. stovepipes and functional decomposition.
– Platform governance becomes a fundamental issue. Who owns it, runs it, controls it?
• Evolutionary processes are unavoidable—leading to unexpected consequences.
They are also the source of all creativity.
– Their essence combines exploration with exploitation of discoveries.
– Organizations can plan to be innovative.
• Groups are natures way to build systems.
– We can build powerful groups because we evolved to live in groups and we can learn.
– How can a group’s wisdom be distilled as action? Bottom-up resource allocation.
• Nature and markets have self-validating criteria: reproductive success and profits.
•
By looking carefully you can see the world in a grain of sand.
69