ISSS 2006 at Sonoma State University

Panel, "The Dynamics of Complex Systems", ISSS 2006 at Sonoma State University, Monday, July 9, 2006, 9:35 a.m.

ISSS Sonoma 2006, Plenary: "The Dynamics of Complex Systems"

These participant's notes were created in real-time during the meeting, based on the speaker's presentation(s) and comments from the audience. These should not be viewed as official transcripts of the meeting, but only as an interpretation by a single individual. Lapses, grammatical errors, and typing mistakes may not have been corrected. Questions about content should be directed to the originator. These notes have been contributed by David Ing (daviding@systemicbusiness.org) at the Systemic Business Community ( http://systemicbusiness.org ).

Introduction:

Ralph Abrama, Professor Emeritus, Mathematics, U.C. Santa Cruz
Yaneer Bar Yam, New England Complex Systems Institute
George Richardson, Systems Dynamics Group
Geoffrey West, President, Santa Fe Institute

[Debora Hammond]

Bringing together people from many systems organizations

Today, complexity organizations

Each speaker will be about 20 minutes, then break

Cluster in groups of 4 to 6
How does the concept of complexity fit into your own life?

[introduction by the autopoetics]

[Ralph Abraham]

30 years ago, talked with Fritjof Capro: chaos is okay

Beginning of the chaos revolution

Chaos is not okay was a serious impediment to development of systems

History: evolutionary processes with plateaus, birfurcations and complex dynamics that happen infrequently

Birfurcation: e.g. quantum leap
Ervin Lazslo, The Birfurcation Age: dynamical literacy, okay to use mathematical words, without being fully literate in those terms
We need new words for new ideas
The fact that they've been developed exquisitely in mathematics, doesn't means that we can't use them without math anxiety

Moved into amateur world history

1990 book: chaos was one the big events

Chaos was not okay for 6000 years

Many philosophical ideas that couldn't be published, because the main idea was not okay
In 1970s, papers in the closet, because they had the idea of oscillation
There was a time when circular thinking wasn't okay: had to be experimental error, wouldn't allow renewal of grants

1974: Chaos avante-garde

"Chaos" was used in the title of a paper in the proceedings of the National Academy
A flood of new ideas, which came out of the closet, now shared

This explosion needs some order

Something like ISSS is important for the creation of the future
As ISSS shrank year by year, now see a turnaround
ISSS towards its true destiny: previous revolutions have been hampered by the communication channel
Now have the worldwide web, instantaneous communications
ISSS as a society of societies, if we apply our own intelligence towards our own process

Advertising for chaos theory

Empowered by the new mathematical idea
This could become the neural network for the emergence order out of the philosophical chaose in our society
Complexity and sustainability are coupled
Cognitive strategies

Advertise agent-based modeling

Will allow social sciences to have the leap forward that physics and biology has had

[Debora Hammond]

NECSI: similar goals to ISSS

[Yaneer Bar Yam]

Focus on policy issues

Values: what we should achieve
Effective: the scientific side

Organization and function

Book on what we've been trying to do to create scientific context: Making Things Work

Looking for postdocs

Free markets versus central control

Used to be dogmatic, now pragmatic issue
When you have a system with a large of interdependence, and try to put someone in control, there's a communication issue
Bandwidth issue

Classical example: Soviet Union

Gorbachev realized couldn't manage agriculture centrally
Had 5 year plan, originally belived a free market was wasteful
This didn't work
Supermarket would have 100 kinds of foods, but poor availability, a lot of spoilage
In a stable environment, planning works

Graph: Complexity as a function of scale

A lot of people doing the same thing: a Roman phalanx, all going the same direction
Green curve, same behaviour at all scales
Compare to people in a mall: at the whole, not doing much, but a lot of fine-scale behaviour
Somewhere in the middle, a dance

Which system for moving at large scale:

A wolf or a baseball player
A wolf has 4 feet on the ground, a baseball player gives up 2 hats to hold a bat

Military examples

Send in large forces, or send in special forces
Tanks from Russian are rusting in Afghanistan

Health care system

Expensive, and not doing very well
Instead of worrying about people dying in Iraq, 50,000 to 100,000 people are dying here from medical errors
That's an airplane flying into the ground every day
It's now worse, doubled
People given wrong prescriptions
Documented errors are 8% to 10%, believe that it's actually 30% to 40%

Medical system 100 years ago

Patient doctor, with straightforward payment
Today: money flow from employer to insurer to doctor
From the point of view of scale and complexity: patient-doctor is complex, but insurer-doctor is a rate paid once per month
Hooking up a large scale process to the highly complex process? Turbulence
Employer-insurer controls the process: it goes up 8% per year
Doctors have to make decisions based on aggregate measures
Managed care: time is managed
This doesn't work

How to solve the problem?

Separate two different aspects of the system
Different organizations for different tasks
When things don't work, some people point to self-motivation, but look at aggregate behaviour
Public or private financing isn't the issue; organizational issue is
Healthy people can be handled in large scale: inoculations, etc, doesn't require doctors
Thus, need a large scale clinic
e.g. Walmart
Public health as things that can be do at a population level
Then leave the doctors to do judgement-based work

Education system reform

It works so well for health care, we'll do the same thing for education: centralized management
Standardized testing
In military, learn quickly from mistakes, but in education it takes decades
Focus on uniformization, industrial era
Destroying what we want our system to be
This is a scientific statement about what is effective

Create diversity and create effectiveness

Dictatorship, anarchy and democracy: three old frames
Niche selection

Have done other work:

Third world development
9/11 report: one recommendation to create a czar of intelligence, but see what happened to the czars
Have to get beyond centralization

Global systems

Ethnic violence

Four color map of the world: Christian, Islam, Far Eastern, and indigenous

Violence is at the boundary of the Islamic world (with drug wars in South America)
Not values, it's a statement
Boundaries are better defined

Model: separation of types

Preference of going to more of the same types
Like matter and vacuum in space, somewhat like oil and water
Scaling behaviour
Universal law, applied to social systems
Then violence doesn't occur when the system is fully mixed, or fully separated
Detect populations of that size: wavelet (Mexican hat), can see where violence is likely

History: map of Yugoslavia

Agent representation
Compare to newspapers: close

Work on pandemics: disease propogation

Different types of disease, pathogens
Over time, people die, exhaust local resources
Added long range links from people
System is unstable, transition to extinction
Even though transportation is a problem, there is a threshold over which the system becomes unstable

[10:25]

[George Richardson]

Systems Dynamics Society, Albany

[George Richardson]

Dynamic complexity

Want to get to simpler level, but we don't do very well with them

Real world, have information feedback, make decisions

Information influences mental models
Mental models influences strategy, strucutre, decision rules
Lots missing
Decisions made partially, principal-agent issues
Mental models have misperceptiions, errors
We're bad at inferring dynamics from mental models

Instead: we build virtual worlds

A virtual world creates a loop that is faster than the real world
Can vary complexity and do controlled experimentation, to learn faster

Dilbert

Dynamic complexity (from Sterman)

Change over time, tighly coupled, governed by feedback, nonlinear, history-dependent (stuck in path), self-organizing, adaptive, counterintuitive, policy resistant, characterized by tradeoff

Systems dynamics: try to move from specific events and decisions, to patterns over time

Policy structure
Self-reinforcing and self-balancing
Let's hunt for feedback loops (like cruise control) that will automatically compensate for initiatives
Try to think in stocks and flows: at the aggregate end of the agent-based models

Systems dynamics process

Two kinds of evaluation: strcuture validating, and behaviour validating over time

We don't come all of out the same systems background

Feedback
6 lines of thinking
Servomechanics and cybernetics

Forrester: closed boundary, then structure feedback loops, find stocks, find flows, look at decision-making goals

Most interested in the closed causal boundary:

Some confusion: aren't closed systems, they exchange material and energy
They're closed mental boundaries, closed causal boundaries
All causality forms loop

Systems thinking as trying to uncover endogenous sources of systems behaviour

Not interested in the exogenous, from outside the system

e.g. New York City population

Every city peaks and declines, some come back
Forrester: the first non-corporate study of systems dynamics

Global atmospheric methane

Global average temperature

Strive for dynamic insights

Stocks and flows

Things that are simple, but we don't do very well
Jack Homer: patterns of drug prevalence in the 1980s-1990s
1980s, we were winning the war on drugs, people were reporting less drug use
Asked have you used within the last month
Homer asked, have you ever used drugs
Had ever used date declined
Have you ever used: it's a bathtub
Past users: deaths and people leaving the United States
Homer figured out a misrepresentation, a mendacity multiplier, found that we weren't winning the war on drugs, the use was going up

Nick Pudar, General Motors

Two successes, recently, one in leasing
Asked by a chief to say something about leasing
GM thinks: builds cars, goes into new car inventory, then purchase/lease and car goes away
Says that GM is not in the used car business
More realistically, there's a used inventory
GM was interesed in the leasing dynamic: more lucrative to lease cars on shorter terms
Pudar found as the trade cycle gets shorter, the lease value of cars gets higher, thus impacts new car sales
e.g. Ford in 1997 price of cars the same, because had to compete with leased cars
Thus GM increased lease lengths before anyone else did

Stocks and flows in global warming

Sterman has done some serious work on this: what happens if we reduce CO2 production, not be reducing economic activity, but its environmental intensity
Shutting off CO2 production: means that CO2 production should peak, and then causes a drop
What should happen: temperature should continue to rise, and may never turn around
The outgoing global heat energy has to rise to be more than inflow: there's a time lag
Kyoto, dropping 10% to 20%: have to drop by 50% to match the outflow, thus it will keep going

(skipping over slides)

Three counties in New York State on welfare reform

Federal max five years in lifetime on welfare
But state has to care for poor, in constitution
Did three policy mixes: invest to speed people out, in middle or at edges
Classic worse before better
People in welfare, then in marginal jobs
For 1.5 years, people on family assistance decreases, but then reversal, and end up with more families of risk
Why? Swamping the downstream

(10:55)

Geoffrey West, president of the Santa Fe Institute

Lots of work on scaling in social systems

[Geoffrey West]

(using overhead slides)

Will address fundamental challenge: really coming to grip with the science under complex systems, and put it into the more traditional mathematical predictive framework

Spent almost entire career in high energy physics: reductionistic

Last 5 to 10 years, took some of those ideas into biology, and questions of social organization
To what extent can biology and social systems be put into a mathematical framework?
Social organization as an extension of biology? Just a metaphor? Substance?

Strongly influenced by greatest discovery in 300 to 400 years: the language of the universe is mathematics

Search for finding the appropriate mathematics
Physics deals with the simplest

Simplicity and complexity

Life is the most complex system:

Simplified meta

Forests

Is there structure there that we can understand

Map of Santa Fe

Diagram of a rat's muscle

Can we estimate a theory that is mathematical and predictive? Daunting

A window, through simplicity

Metabolic rate: how much energy does it take to keep a system alive

Metabolic rate versus body mass
Simplicity in scaling
Every subsystem of subsystem is involved in its environmental niche
Power law; slope (gradient) is 3/4 in log scale
Remarkable, a non-linear relationship: all mammals are made of the same thing
Doubling the number of cells doesn't mean doubling the metabolic rate
Same pattern down to mammalian cell, mitochondria, respiratory enzyme

Need to operate about the same as a light bulb: 80 to 90 watts, similar to 2000 calories

Extraordinary efficiency in humans
Metabolic rate per unit mass of human tissue
It costs less to support a unit mass of tissue on large animals
Small may be beautiful, but large is always more efficient

If you look an any other physiological rate, e.g. heart rate, get a simple power law

Heart rate vs. body weight
Similar with life span

Kleiber's law: Body mass is related to Metabolism ** (3/4)

Special number 4

Heart rate vs. body rate, 1/4 power law

Increase weight by 10,000, metabolic rate by 1000
All scales, all rates (including the rate of evolution) have 1/4 power law
Life span: changes with time, by increasing 1/4 power
Life span * heart beat = number of the heart beats in a life time, which is approximately invariant
Pace of size decreases with size

This work was done with ecologist Jim Brown, and Brian Equist

All of these laws are actually reflections of the generic universal properties that sustain all of life

Four principles

(1) Life at all scale is sustained by hierarchical branching networks.

These networks have to be spaced right: every local site of the organism, at whatever scale

(2) The kernel unit of the network are invariant (e.g. mitochondria): system works on evolving the network, not the kernel
(3) There's a finitude of possible networks, and some are optimized

We, as mammals, one minimizes the amount of energy to keep blood going
Minimization principles are the basis for writing down equations of motion
Can then determine

(4) ?

4=3+1

Three dimensions, and then space has to go everywhere (i.e. fractal)
Radius, length, pulse rate, there's a formula, can get one for the average rhinoceros
Economy of scale, dissipation of energy, but dissipation decreases with scale

Network is primary

Network controls the output of cells
Average metabolic power decreases with size
Truly not reductionist: cell doesn't determine properties of the organism, it's the other way around
The bigger animal, the less each cell has to do
If remove the network, so that it no longer controls the cell (e.g. cultivate in vitro) they will be the same

Growth rate of a rat: sigmoidal curve

Network, resources come down
Some energy comes in to feed the cells
Growing phases, makes new cells
General model for growth, with all parameters determined
Plot: everything grows at the same rate, mammals, insects, etc.: a unity of life

How is cancer different from the growth of normal human tissue?

Social organizations

We have evolved to the right metabolic rate, the right size
10,000 years ago, we started to create social organization, changing our idea of metabolic rate
To stay alive, need 10,000 watts, e.g. in a building, air conditioning -- which means that we're each larger than a blue whale
Populations are adjusted according to 1/4 power law
Violating those laws screws up the system

Are there analogs to these power laws, in social organizations?

Indicators: salaries, taxes, length of electrical cables ... versus city size
For those variables that are biological (e.g. functioning of city, city cables, plumbing), powers are less than one
For those social (e.g. GDP) with no analog in biology have powers greater than 1

Thus, a taxonomy of scaling

Linear: The bigger you are, the more you want, fundamentally greed (non-innovative)

In biology, the rate of living is determined

Pace of life slows with biological
e.g. walking speed, things in cities move faster

Growth, it's the same as in biology

Some energy into replacement, some into growth
Previously, saw growth and then levels off
Social organizations have no slacking off, can continue to grow, but not forever
Can go to infinity in a finite period of time: will run out of resources
Thus need to innovate, e.g. create steam engines, so that reset the clock with cycles of innovation
On a treadmill, always have to innovate
But the time between innovations gets shorter and shorter
Time between changes accelerates, can't continue this, will lead to collapse

e.g. population of New York City increases, but resets the clock

We are victims of this phenomenon

Even trivial increases in software come faster
This becomes more and more difficult at age 65

x x

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