To say that I enjoyed this book is not an accurate statement. I found it interesting and irritating. I’ve been telling people that it’s a book I loved to hate, although even that’s not correct. Reading it was difficult but I was pulled along by insights that appeared every ten pages or so. It’s basically a one concept book – the prevalence of what he calls “Black Swans” in a lot human systems that occur more frequently that Gaussian statistics would lead one to believe, with large impact. He defines Black Swans, “A Black Swan is a highly improbable event with three characteristics: It is unpredictable; it carries a massive impact; and after the fact, we concoct an explanation that makes it appear less random, and more predictable than it was.”
He draws together knowledge from many different fields to build his model, and that’s a good thing. However, he quite often uses uncommon words and references that sometime make his writing hard to understand.
Taleb has four sections to the book: how we seek validation, we just can’t predict, those gray swans of extremistan and the end. (Mediocristan and Extremistan are his two words for the tame, quite and uneventful, and Black Swan generating provinces.) He writes constantly about the unpredictable and the unhooking of cause and effect.
Unfortunately for anyone writing about what he calls extremistan, by the very nature of writing is using the principles of mediocristan, and in this case the structure of the book roughly follows the four causes of reality described by Aristotle.
The author falls victim to some of the very things he’s trying to write against. The Western world has known for some time, and the Eastern world for a long time, that the two conditions do not exist in an either-or, but a both-and world. Mediocristan and extremistan exist at the same time. However, it is wise to not ignore one over the other.
Taleb often writes against the uses of story or narrative. However, he validates all of his assertions through anecdotes himself.
Some of the statements in the book that frustrated me:
* His writing makes it seem that Gaussian statistics is to blame. It’s not; it’s the application of Gaussian statistics to human systems that is wrong. Gaussian statics work very well for the physical systems they were created for.
* He states repeatedly that all important progress is created by Black Swans, and that’s not true. For every Black Swan there are thousands (perhaps millions) of incremental and distinctive changes that keep the engines of society running before they “run out of gas”, and there’s a need for a Black Swan.
* The future is unpredictable and futurists are charlatans. This not true in general. For some “utility functions” the rate of change function for the “utility function” can be determined, and that function remains the same for many years allowing predictability.
* Black Swans are not random, but they are not predictable.
* The probability of an event occurring, including Black Swans, can be determined, but the time when the event might happen cannot.
Everything the author writes about can be explained with the help of complexity science.
I agree with the author that random events or small changes in initial conditions can shape the outcome of a process. This after all was how complexity science was discovered by Lorenz in 1961, and famously named the “butterfly effect”. This is right at the heart of two versions of how the future is created. Is it out of the impact of significant events or important persons? Or, is the result of societal forces? Would we have the information industry we have if we did not have Bill Gates, Steve Wozniak and Steve Jobs? Of course we would. Others would have emerged to move the technology and industry along because they both have a utility for society.
And, the author is correct in that we are cause seeking beings. We couldn’t have forecast that any one of the three people mentioned above would have accomplished what they did. But, after the fact, we create a history of how they accomplished what they did, and then tell others to emulate that story. However, we did forecast correctly the widespread diffusion of semiconductor and information technologies.
We can’t forecast from a part of a tree seed what specific leaf will result. But, we can start with a specific leaf and trace it back through the twigs, branches and trunk back to the base of its trunk.
The most valuable part of the book is on risk and investment strategy. In it, he balances mediocristan and extremistan.
My guess is that he was writing this book for the financial idiots who got us into our current economic malaise with their repeated use of modified Ponzi schemes, ignorance of history, misuse of statistics and blindness to Black Swans. For that I applaud his efforts. I wish that he could have been more influential, and influenced the process in its early stages before it produced the Black Swan we are living in, and that our children and grand children will have to pay for.
The Black Swan: The Impact of the Highly Improbable
Nassim Nicholas Taleb, Random House, 2007, 366 p
Tuesday, May 5, 2009
Sunday, May 3, 2009
Chaos*Gaia*Eros
This is a fascinating and insightful book. It is off the mainstream topic of what is now called complexity science (then called chaos theory), but an amazing piece of work. What Abraham, a world renown chaos theorist and professor emeritus of mathematics at the University of California, Santa Cruz, noticed was the apparent parallels between recent developments in physics and the history of consciousness and the rediscovery of the three forces that drive it: chaos, gaia and eros.
The characteristic features of this tradition are the trinity:
* Chaos, the creative void, source of all form
* Gaia, the physical existence and living spirit of the created world
* Eros, the spiritual medium connecting Chaos and Gaia; the creative impulse
According to the author: “One of the main goals of this book is to introduce the concept of dynamical historiography, the application of the mathematical theory of dynamical systems, chaos, and bifurcations to the patterns of history. It is hoped that from the future development of this mode of inquiry we may evolve a better understanding of ourselves and our evolutionary challenges.”
Abraham writes in the introduction, “Since 1960, I've been working in the area of dynamical systems theory, a classical branch of mathematics created by Isaac Newton in the seventeenth century. In the midst of the cultural upheavals of the 1960s, great inroads were made in the field of dynamical systems, due in part to the computer revolution. Dynamical systems theory deals with moving systems, such as the solar system, and the patterns they trace in space and time. Newton discovered mathematical laws that such systems obey, and constructed mathematical models that are abstract analogues of their space-time patterns. His discovery has been credited as one of the greatest intellectual contributions ever made by a single person.
About a century ago, dynamical systems theory was revolutionized by Henri Poincare, the great French mathematician, when he discovered models for highly complex motions (which later came to be called strange attractors).l By the late 1960s, numerous examples of strange attractors had been discovered in computer simulations.
In 1972 I traveled to the Institut des Hautes Etudes Scientifiques, in France, to visit with Rene Thorn. In his book on morphogenesis, the study of pattern formation, Thorn introduced a new language for the application of dynamical systems theory, which included the terms attractor; basin of attraction, and catastrophe. I was interested in pursuing these ideas with him, but when I arrived, Thorn was onto something new. He showed me a book of photographs by Hans Jenny, an amateur scientist from Basel. The photographs showed forms created by sound vibrations in sand, powder, and water. They were suggestive of galaxies, plants, brain waves, memories, hallucinations, and abstract works of art. A theory of morphogenesis, in which the mysteries of creation were seemingly revealed, was projected wordlessly by the book. My mind reeled with new possibilities for the application of dynamical systems theory to nature and society.
That summer I went to India on holiday and soon found myself living in a cave in the jungle of the Himalayas, a mile above sea level. The cave had been inhabited for centuries by jungle yogis, and in it I experienced a number of illuminations on the concepts of vibration in Hindu philosophy, and on harmony and resonance concepts in mathematics, music, and mysticism.
When I returned to California in 1974, I began a program of research and teaching on vibrations, chaos, computation, and computer graphics, delving deeply into the histories of these subjects, going ever backward-to the Baroque, to the Renaissance, to ancient Greece, and beyond. Soon after my return, I found other people who shared these interests, including Terence McKenna and the late Erich Jantsch. Erich was a missionary of general evolution theory, a whole systems theory evolving from the work of a number of twentieth century scientists interested in conceptualizing a science of the all-and-everything. The theory offered a strategy by which to understand the structure of history through the kind of mathematical model introduced by Rene Thom. Here, my Himalayan cave illuminations could be abstracted and applied to society, to the history of consciousness (and unconsciousness), and therefore to the future.
I've spent the last twenty years exploring a broad range of applications for these concepts, on which this book is a meditation. It offers a conceptual model for history, constructed from the mathematical tools conceived by Rene Thom and applied in the style of Erich Jantsch. Such a model may be crucial for understanding our history and as an aid in creating our future.
The Chaos Revolution, Gaia Hypothesis, and Erodynamics
During the 1970s paradigm shifts within the sciences began to emerge into public view. Around 1973 new dynamical models were applied to turbulent fluid motions (for example, boiling water, and a dripping faucet), but it was not until 1975 that these models were connected with the word chaos. The terms strange attractor and dynamical systems theory were replaced by chaotic attractor and chaos theory. The new theory swept through the sciences in a wave of renewal. The Chaos Revolution was underway.
Journalists began calling me to ask: "What is chaos theory? Does it have anything to do with chaos in ordinary life? What is the theory good for? Why are scientists so excited about it?"
These questions, which I could not easily answer, drove me deeply into the literature of myths and cultural history. I found that the word Chaos first appeared in a book called Theogony, by Hesiod, one of the early Greek poets. His poem is a creation myth telling stories of the origins of the gods. Here the word chaos does not mean disorder. Instead, it represents an abstract cosmic principle referring to the source of all creation. It also appears in connection with the two other fundamental concepts: Gaia (the created universe) and Eros (the creative impulse).
I was amazed to realize that this same trinity, which preceded the creation of the gods and goddesses of the usual pantheon of early Greek paganism (also called Orphism), is also associated with three revolutionary movements underway in the sciences:
* The Chaos Revolution was named in 1975 for a new branch of mathematics that provides models for many intrinsically irregular natural processes.
* The Gaia Hypothesis, named in 1973, proposes a self-regulation capability of the complex system composed of earth, ocean, atmosphere, and the living ecosystems of our planet. According to Gaia theory; which views Earth as a living system, the biosphere acts to create and maintain favorable conditions for life.
*Erodynamics, named in 1989, applies dynamical systems theory to human social phenomena.
What strange synchronicity, I wondered, led to three different recent innovations in the sciences, in apparently independent developments, sharing a common mathematical basis, bearing names (Chaos, Gaia, Eros) that are associated in Hesiod’s trinity almost three thousand years ago?”
The book races the development of the concept of the orphic trinity – chaos, gaia and eros – through the three parts of the book:
1. Dynamics and the Orphic Trinity in History
2. The Orphic Trinity in Myth
3. The Orphic Trinity in the Sciences
I encourage all readers interested in understanding the bigger “picture” of complexity and the future to study this book.
Chaos*Gaia*Eros
Ralph Abraham, Harper, 1994, 263 p
The characteristic features of this tradition are the trinity:
* Chaos, the creative void, source of all form
* Gaia, the physical existence and living spirit of the created world
* Eros, the spiritual medium connecting Chaos and Gaia; the creative impulse
According to the author: “One of the main goals of this book is to introduce the concept of dynamical historiography, the application of the mathematical theory of dynamical systems, chaos, and bifurcations to the patterns of history. It is hoped that from the future development of this mode of inquiry we may evolve a better understanding of ourselves and our evolutionary challenges.”
Abraham writes in the introduction, “Since 1960, I've been working in the area of dynamical systems theory, a classical branch of mathematics created by Isaac Newton in the seventeenth century. In the midst of the cultural upheavals of the 1960s, great inroads were made in the field of dynamical systems, due in part to the computer revolution. Dynamical systems theory deals with moving systems, such as the solar system, and the patterns they trace in space and time. Newton discovered mathematical laws that such systems obey, and constructed mathematical models that are abstract analogues of their space-time patterns. His discovery has been credited as one of the greatest intellectual contributions ever made by a single person.
About a century ago, dynamical systems theory was revolutionized by Henri Poincare, the great French mathematician, when he discovered models for highly complex motions (which later came to be called strange attractors).l By the late 1960s, numerous examples of strange attractors had been discovered in computer simulations.
In 1972 I traveled to the Institut des Hautes Etudes Scientifiques, in France, to visit with Rene Thorn. In his book on morphogenesis, the study of pattern formation, Thorn introduced a new language for the application of dynamical systems theory, which included the terms attractor; basin of attraction, and catastrophe. I was interested in pursuing these ideas with him, but when I arrived, Thorn was onto something new. He showed me a book of photographs by Hans Jenny, an amateur scientist from Basel. The photographs showed forms created by sound vibrations in sand, powder, and water. They were suggestive of galaxies, plants, brain waves, memories, hallucinations, and abstract works of art. A theory of morphogenesis, in which the mysteries of creation were seemingly revealed, was projected wordlessly by the book. My mind reeled with new possibilities for the application of dynamical systems theory to nature and society.
That summer I went to India on holiday and soon found myself living in a cave in the jungle of the Himalayas, a mile above sea level. The cave had been inhabited for centuries by jungle yogis, and in it I experienced a number of illuminations on the concepts of vibration in Hindu philosophy, and on harmony and resonance concepts in mathematics, music, and mysticism.
When I returned to California in 1974, I began a program of research and teaching on vibrations, chaos, computation, and computer graphics, delving deeply into the histories of these subjects, going ever backward-to the Baroque, to the Renaissance, to ancient Greece, and beyond. Soon after my return, I found other people who shared these interests, including Terence McKenna and the late Erich Jantsch. Erich was a missionary of general evolution theory, a whole systems theory evolving from the work of a number of twentieth century scientists interested in conceptualizing a science of the all-and-everything. The theory offered a strategy by which to understand the structure of history through the kind of mathematical model introduced by Rene Thom. Here, my Himalayan cave illuminations could be abstracted and applied to society, to the history of consciousness (and unconsciousness), and therefore to the future.
I've spent the last twenty years exploring a broad range of applications for these concepts, on which this book is a meditation. It offers a conceptual model for history, constructed from the mathematical tools conceived by Rene Thom and applied in the style of Erich Jantsch. Such a model may be crucial for understanding our history and as an aid in creating our future.
The Chaos Revolution, Gaia Hypothesis, and Erodynamics
During the 1970s paradigm shifts within the sciences began to emerge into public view. Around 1973 new dynamical models were applied to turbulent fluid motions (for example, boiling water, and a dripping faucet), but it was not until 1975 that these models were connected with the word chaos. The terms strange attractor and dynamical systems theory were replaced by chaotic attractor and chaos theory. The new theory swept through the sciences in a wave of renewal. The Chaos Revolution was underway.
Journalists began calling me to ask: "What is chaos theory? Does it have anything to do with chaos in ordinary life? What is the theory good for? Why are scientists so excited about it?"
These questions, which I could not easily answer, drove me deeply into the literature of myths and cultural history. I found that the word Chaos first appeared in a book called Theogony, by Hesiod, one of the early Greek poets. His poem is a creation myth telling stories of the origins of the gods. Here the word chaos does not mean disorder. Instead, it represents an abstract cosmic principle referring to the source of all creation. It also appears in connection with the two other fundamental concepts: Gaia (the created universe) and Eros (the creative impulse).
I was amazed to realize that this same trinity, which preceded the creation of the gods and goddesses of the usual pantheon of early Greek paganism (also called Orphism), is also associated with three revolutionary movements underway in the sciences:
* The Chaos Revolution was named in 1975 for a new branch of mathematics that provides models for many intrinsically irregular natural processes.
* The Gaia Hypothesis, named in 1973, proposes a self-regulation capability of the complex system composed of earth, ocean, atmosphere, and the living ecosystems of our planet. According to Gaia theory; which views Earth as a living system, the biosphere acts to create and maintain favorable conditions for life.
*Erodynamics, named in 1989, applies dynamical systems theory to human social phenomena.
What strange synchronicity, I wondered, led to three different recent innovations in the sciences, in apparently independent developments, sharing a common mathematical basis, bearing names (Chaos, Gaia, Eros) that are associated in Hesiod’s trinity almost three thousand years ago?”
The book races the development of the concept of the orphic trinity – chaos, gaia and eros – through the three parts of the book:
1. Dynamics and the Orphic Trinity in History
2. The Orphic Trinity in Myth
3. The Orphic Trinity in the Sciences
I encourage all readers interested in understanding the bigger “picture” of complexity and the future to study this book.
Chaos*Gaia*Eros
Ralph Abraham, Harper, 1994, 263 p
Saturday, May 2, 2009
Modeling Complexity with Massively Parallel Microworlds
This is an overview of two books taken together – Turtles, Termites, and Traffic Jams and Adventures in Modeling. Both of these books describe the StarLogo system create and programmed by Mitchel Resnick, the author of the first book and a coauthor of the second book.
StarLogo is a programmable modeling environment for exploring the workings of decentralized systems -- systems that are organized without an organizer, coordinated without a coordinator. With StarLogo, you can model (and gain insights into) many real-life phenomena, such as bird flocks, traffic jams, ant colonies, and market economies.
In decentralized systems, orderly patterns can arise without centralized control. Increasingly, researchers are choosing decentralized models for the organizations and technologies that they construct in the world, and for the theories that they construct about the world. But many people continue to resist these ideas, assuming centralized control where none exists -- for example, assuming (incorrectly) that bird flocks have leaders. StarLogo is designed to help students (as well as researchers) develop new ways of thinking about and understanding decentralized systems.
StarLogo is a specialized version of the Logo programming language. With traditional versions of Logo, you can create drawings and animations by giving commands to graphic "turtles" on the computer screen. StarLogo extends this idea by allowing you to control thousands of graphic turtles in parallel. In addition, StarLogo makes the turtles' world computationally active: you can write programs for thousands of "patches" that make up the turtles' environment. Turtles and patches can interact with one another -- for example, you can program the turtles to "sniff" around the world, and change their behaviors based on what they sense in the patches below. StarLogo is particularly well-suited for Artificial Life projects
StarLogo is developed at Media Laboratory and Teacher Education Program, MIT, Cambridge, Massachusetts, with support from the National Science Foundation and the LEGO group. It is available free for download and works on PCs and Macs. The free downloads have numerous preprogrammed examples, demonstrations and a user group.
Turtles, Termites, and Traffic Jams is a book about the development of the program, the reasons for its existence and some applications. Adventures in Modeling is a guide for a teacher to introduce the concepts of complexity and modeling, and to get the students to program their own models. I would recommend reading Turtles, Termites, and Traffic Jams first.
Turtles, Termites, and Traffic Jams begins with an excellent description of the foundations of decentralized complex systems with emergent characteristics.
“A flock of birds sweeps across the sky. Like a well-choreographed dance troupe, the birds veer to the left in unison. Then, suddenly, they all dart to the right and swoop down toward the ground. Each movement seems perfectly coordinated. The flock as a whole is as graceful-maybe more graceful-than any of the birds within it.
How do birds keep their movements so orderly, so synchronized? Most people assume that birds play a game of follow-the-leader: the bird at the front of the flock leads, and the others follow. But that's not so. In fact, most bird flocks don't have leaders at all. There is no special "leader bird." Rather, the flock is an example of what some people call "self-organization." Each bird in the flock follows a set of simple rules, reacting to the movements of the birds nearby it. Orderly flock patterns arise from these simple, local interactions. None of the birds has a sense of the overall flock pattern. The bird in front is not a leader in any meaningful sense - it just happens to end up there. The flock is organized without an organizer, coordinated without a coordinator.
Bird flocks are not the only things that work that way. Ant colonies, highway traffic, market economies, immune systems-in all of these systems, patterns are determined not by some centralized authority but by local interactions among decentralized components. As ants forage for food, their trail patterns are determined not by the dictates of the queen ant but by local interactions among thousands of worker ants. Patterns of traffic arise from local interactions among individual cars. Macroeconomic patterns arise from local interactions among millions of buyers and sellers. In immune systems, armies of antibodies seek out bacteria in a systematic, coordinated attack-without any "generals" organizing the overall battle plan.
In recent years, there has been a growing fascination with these types of systems. Ideas about decentralization and self-organization are spreading through the culture like a virus, infecting almost all domains of life. Increasingly, people are choosing decentralized models for the organizations and technologies that they construct in the world - and for the theories that they construct about the world.”
Yet there is still deep seated resistance to the ideas of decentralization and emergence. We seem to have buried deep within us a centralized mindset that is so strong that we tend to look for leaders or causes behind every result. “The problems is that people have, too often, relied almost entirely on centralized strategies. Decentralized approaches have been ignored, undervalued, and overlooked.” This, in spite of the fact that the evidence supporting a decentralized model is everywhere in our lives.
In the foundations chapter, Resnick explores decentralization in five domains – organizations, technologies, scientific models, theories of self and mind, and theories of knowledge.
He writes, “As we enter the era of Decentralization, there is an important educational challenge: How can we help people become intellectually engaged with the new types of systems and new types of thinking that characterize this new era? To date, schools, and other educational institutions have done little, if anything, to engage students with the ideas of decentralization. Indeed they often perpetuate centralized explanations and approaches.” StarLogo and StarLogo TNG are responses to this impulse.
The models you can create with StarLogo are unlike any models you’ve probable created before. The approach in the past would have been to work out a mathematical model from assumptions about the system you were trying to model. Quite often simplifications would have to be made in order to represent the problem with mathematical equations.
If the system was an oscillator, the math would likely include sine functions. With StarLogo, each agent in the system is given simple rules to follow, the simpler the better, and then turned loose to interact with each other and the environment. The result is the emergence of a sine wave.
In the above case, the green curve represents the amount of grass on a field and the red curve represents the number of rabbits.
Adventures in Modeling is beautifully designed as a teacher’s handbook to teach the concepts and modeling to high school students. However, it could easily be used for adults as well. I particularly like activity – challenge pairing. I’ve not used the activities, but they would seem to be very useful ways to get students to understand the concept before trying to program a model.
The programming of StarLogo is relatively simple reminiscent of older programming languages with simple logical rules and commands. A guide to the language is provided online. Note: The agents are called “turtles” and they operate on “patches”.
The topics in the book are:
Introduction
Chapter 1: Travels with StarLogo
Chapter 2: Models in Science and Education
Chapter 3: Planning Your Adventure
Chapter 4: Frequently Asked Questions
Chapter 5: Adventures in a Secondary School Environment
Chapter 6: StarLogo World
Activities and Challenges
Activity 1: A Round of Applause
Challenge 1: A StarLogo Modeler Is Born
Activity 2: 27 Blind Mice
Challenge 2: Turtles, I Command Thee
Activity 3: Pixelated Paths
Challenge 3: Landmark Decisions
Activity 4: Watch Your Investments Grow
Challenge 4: Turning Turtles Into Termites
Activity 5: Survival of the Fittest Paper Catchers
Challenge 5: State of the Turtles
Activity 6: Sold! to the Highest Bidder
Challenge 6: Teaching Turtles to Talk
Activity 7: Foraging Frenzy
Challenge 7: Quest for Communication
Activity 8: Majority Rules
Challenge 8: The Plot Thickens
Activity 9: Flight of the Humanboids
Challenge 9: How to Track a Turtle
Activity 10: The Gambler's Dilemma
Challenge 10: Breeds-The Final Frontier
Student Handouts
Appendices
Appendix A: Notes for MacStarLogo Users
Appendix B: Collected Hints
Appendix D: Common StarLogo Error Messages
Bibliography
Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds
Mitchell Resnick, MIT Press, 1994, 163 p
Adventures in Modeling: Exploring Complex, Dynamic Systems with StarLogo
Vanessa Stevens Colella, Eric Klopfer and Mitchel Resnick, Teachers College Press, 2001, 188 p
StarLogo is a programmable modeling environment for exploring the workings of decentralized systems -- systems that are organized without an organizer, coordinated without a coordinator. With StarLogo, you can model (and gain insights into) many real-life phenomena, such as bird flocks, traffic jams, ant colonies, and market economies.
In decentralized systems, orderly patterns can arise without centralized control. Increasingly, researchers are choosing decentralized models for the organizations and technologies that they construct in the world, and for the theories that they construct about the world. But many people continue to resist these ideas, assuming centralized control where none exists -- for example, assuming (incorrectly) that bird flocks have leaders. StarLogo is designed to help students (as well as researchers) develop new ways of thinking about and understanding decentralized systems.
StarLogo is a specialized version of the Logo programming language. With traditional versions of Logo, you can create drawings and animations by giving commands to graphic "turtles" on the computer screen. StarLogo extends this idea by allowing you to control thousands of graphic turtles in parallel. In addition, StarLogo makes the turtles' world computationally active: you can write programs for thousands of "patches" that make up the turtles' environment. Turtles and patches can interact with one another -- for example, you can program the turtles to "sniff" around the world, and change their behaviors based on what they sense in the patches below. StarLogo is particularly well-suited for Artificial Life projects
StarLogo is developed at Media Laboratory and Teacher Education Program, MIT, Cambridge, Massachusetts, with support from the National Science Foundation and the LEGO group. It is available free for download and works on PCs and Macs. The free downloads have numerous preprogrammed examples, demonstrations and a user group.
Turtles, Termites, and Traffic Jams is a book about the development of the program, the reasons for its existence and some applications. Adventures in Modeling is a guide for a teacher to introduce the concepts of complexity and modeling, and to get the students to program their own models. I would recommend reading Turtles, Termites, and Traffic Jams first.
Turtles, Termites, and Traffic Jams begins with an excellent description of the foundations of decentralized complex systems with emergent characteristics.
“A flock of birds sweeps across the sky. Like a well-choreographed dance troupe, the birds veer to the left in unison. Then, suddenly, they all dart to the right and swoop down toward the ground. Each movement seems perfectly coordinated. The flock as a whole is as graceful-maybe more graceful-than any of the birds within it.
How do birds keep their movements so orderly, so synchronized? Most people assume that birds play a game of follow-the-leader: the bird at the front of the flock leads, and the others follow. But that's not so. In fact, most bird flocks don't have leaders at all. There is no special "leader bird." Rather, the flock is an example of what some people call "self-organization." Each bird in the flock follows a set of simple rules, reacting to the movements of the birds nearby it. Orderly flock patterns arise from these simple, local interactions. None of the birds has a sense of the overall flock pattern. The bird in front is not a leader in any meaningful sense - it just happens to end up there. The flock is organized without an organizer, coordinated without a coordinator.
Bird flocks are not the only things that work that way. Ant colonies, highway traffic, market economies, immune systems-in all of these systems, patterns are determined not by some centralized authority but by local interactions among decentralized components. As ants forage for food, their trail patterns are determined not by the dictates of the queen ant but by local interactions among thousands of worker ants. Patterns of traffic arise from local interactions among individual cars. Macroeconomic patterns arise from local interactions among millions of buyers and sellers. In immune systems, armies of antibodies seek out bacteria in a systematic, coordinated attack-without any "generals" organizing the overall battle plan.
In recent years, there has been a growing fascination with these types of systems. Ideas about decentralization and self-organization are spreading through the culture like a virus, infecting almost all domains of life. Increasingly, people are choosing decentralized models for the organizations and technologies that they construct in the world - and for the theories that they construct about the world.”
Yet there is still deep seated resistance to the ideas of decentralization and emergence. We seem to have buried deep within us a centralized mindset that is so strong that we tend to look for leaders or causes behind every result. “The problems is that people have, too often, relied almost entirely on centralized strategies. Decentralized approaches have been ignored, undervalued, and overlooked.” This, in spite of the fact that the evidence supporting a decentralized model is everywhere in our lives.
In the foundations chapter, Resnick explores decentralization in five domains – organizations, technologies, scientific models, theories of self and mind, and theories of knowledge.
He writes, “As we enter the era of Decentralization, there is an important educational challenge: How can we help people become intellectually engaged with the new types of systems and new types of thinking that characterize this new era? To date, schools, and other educational institutions have done little, if anything, to engage students with the ideas of decentralization. Indeed they often perpetuate centralized explanations and approaches.” StarLogo and StarLogo TNG are responses to this impulse.
The models you can create with StarLogo are unlike any models you’ve probable created before. The approach in the past would have been to work out a mathematical model from assumptions about the system you were trying to model. Quite often simplifications would have to be made in order to represent the problem with mathematical equations.
If the system was an oscillator, the math would likely include sine functions. With StarLogo, each agent in the system is given simple rules to follow, the simpler the better, and then turned loose to interact with each other and the environment. The result is the emergence of a sine wave.
In the above case, the green curve represents the amount of grass on a field and the red curve represents the number of rabbits.
Adventures in Modeling is beautifully designed as a teacher’s handbook to teach the concepts and modeling to high school students. However, it could easily be used for adults as well. I particularly like activity – challenge pairing. I’ve not used the activities, but they would seem to be very useful ways to get students to understand the concept before trying to program a model.
The programming of StarLogo is relatively simple reminiscent of older programming languages with simple logical rules and commands. A guide to the language is provided online. Note: The agents are called “turtles” and they operate on “patches”.
The topics in the book are:
Introduction
Chapter 1: Travels with StarLogo
Chapter 2: Models in Science and Education
Chapter 3: Planning Your Adventure
Chapter 4: Frequently Asked Questions
Chapter 5: Adventures in a Secondary School Environment
Chapter 6: StarLogo World
Activities and Challenges
Activity 1: A Round of Applause
Challenge 1: A StarLogo Modeler Is Born
Activity 2: 27 Blind Mice
Challenge 2: Turtles, I Command Thee
Activity 3: Pixelated Paths
Challenge 3: Landmark Decisions
Activity 4: Watch Your Investments Grow
Challenge 4: Turning Turtles Into Termites
Activity 5: Survival of the Fittest Paper Catchers
Challenge 5: State of the Turtles
Activity 6: Sold! to the Highest Bidder
Challenge 6: Teaching Turtles to Talk
Activity 7: Foraging Frenzy
Challenge 7: Quest for Communication
Activity 8: Majority Rules
Challenge 8: The Plot Thickens
Activity 9: Flight of the Humanboids
Challenge 9: How to Track a Turtle
Activity 10: The Gambler's Dilemma
Challenge 10: Breeds-The Final Frontier
Student Handouts
Appendices
Appendix A: Notes for MacStarLogo Users
Appendix B: Collected Hints
Appendix D: Common StarLogo Error Messages
Bibliography
Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds
Mitchell Resnick, MIT Press, 1994, 163 p
Adventures in Modeling: Exploring Complex, Dynamic Systems with StarLogo
Vanessa Stevens Colella, Eric Klopfer and Mitchel Resnick, Teachers College Press, 2001, 188 p
Friday, May 1, 2009
All Together Now (or, Can Collective Intelligence Save the Planet?)
MIT Sloan Management Review
An interview with Thomas Malone
April 23, 2009
MIT Sloan School professor Thomas Malone addresses the mental models that impede management progress, the role of collective intelligence in solving climate problems, and his view of how wrong people are about what business is for.
Even before launching the MIT Center for Collective Intelligence, Thomas Malone was trying to imagine how work could one day be done differently. A professor at the MIT Sloan School of Management, he was a founding co-director of the Initiative on Inventing the Organizations of the 21st Century, and in general has continuously explored how “to help society take advantage of the opportunities for organizing itself in new and better ways made possible by technology.”
Some of those ways offer interesting paths to sustainability—but the paths are to sustainability as Malone defines it, which doesn’t mean a world in which everything is built to last. “It’s often the case that good things are sustainable, but sometimes things are sustainable but not good,” he says. “And sometimes things are good but not sustainable.”
In this installment of the MIT Sustainability Interview series, Malone addresses the mental models that impede management progress, the role of collective intelligence in solving climate problems, and his view of how wrong people are about what business is for. He spoke with MIT Sloan Management Review Editor-in-Chief Michael S. Hopkins.
More
An interview with Thomas Malone
April 23, 2009
MIT Sloan School professor Thomas Malone addresses the mental models that impede management progress, the role of collective intelligence in solving climate problems, and his view of how wrong people are about what business is for.
Even before launching the MIT Center for Collective Intelligence, Thomas Malone was trying to imagine how work could one day be done differently. A professor at the MIT Sloan School of Management, he was a founding co-director of the Initiative on Inventing the Organizations of the 21st Century, and in general has continuously explored how “to help society take advantage of the opportunities for organizing itself in new and better ways made possible by technology.”
Some of those ways offer interesting paths to sustainability—but the paths are to sustainability as Malone defines it, which doesn’t mean a world in which everything is built to last. “It’s often the case that good things are sustainable, but sometimes things are sustainable but not good,” he says. “And sometimes things are good but not sustainable.”
In this installment of the MIT Sustainability Interview series, Malone addresses the mental models that impede management progress, the role of collective intelligence in solving climate problems, and his view of how wrong people are about what business is for. He spoke with MIT Sloan Management Review Editor-in-Chief Michael S. Hopkins.
More
Emergence
“Most of all, we need to preserve the absolute unpredictably and total improbability of our connected minds. That way we can keep open all the options, as we have in the past.”
Lewis Thomas, 1973
This book by Steven Johnson is a good book to read if you’re just getting started on the topic of complexity science or interested in how to build emergent systems.
Reading on complexity is difficult. Words like complexity, and especially chaos, take on different meanings when you are talking about the science of complexity. Authors in the field don’t always agree on what the words mean. This book is on emergence that is a property of complex systems, another complication.
The author identifies Warren Weaver as the originator of the science of complexity in 1948. Weaver wrote an article for the American Scientist that identified the three regions of science that we’re still having difficultly naming. He called the three simplicity, disorganized complexity and organized complexity.
“…Weaver divided the last few centuries of scientific inquiry into three broad camps. First, the study of simple systems: two or three variable problems, such as the rotation of planets, or the connection between an electric current and its voltage and resistance. Second, problems of "disorganized complexity": problems characterized by millions or billions of variables that can only be approached by the methods of statistical mechanics and probability theory. These tools helped explain not only the behavior of molecules in a gas, or the patterns of heredity in a gene pool, but also helped life insurance companies turn a profit despite their limited knowledge about any individual human's future health. Thanks to Claude Shannon's work, the statistical approach also helped phone companies deliver more reliable and intelligible long distance service.
But there was a third phase to this progression, and we were only beginning to understand. ‘This statistical method of dealing with disorganized complexity, so powerful an advance over the earlier two-variable methods, leaves a great field untouched,’ Weaver wrote. There was a middle region between two-variable equations and problems that involved billions of variables. Conventionally, this region involved a "moderate" number of variables, but the size of the system was in fact a secondary characteristic:
Much more important than the mere number of variables is the fact that these variables are all interrelated.... These problems, as contrasted with the disorganized situations with which statistics can cope, show the essential feature of organization. We will therefore refer to this group of problems as those of organized complexity.”
The author describes the five fundamental principles of a system that will learn from the ground up, from local knowledge, and have the likely hood of exhibiting emergence:
• More is different - a critical mass must exist and individuals don’t “know” that they are organizing
• Ignorance is useful – simplicity of rules and language
• Random encounters – decentralized systems rely on random encounters
• Pattern detection – individuals have to be able to recognize patterns
• Interaction with neighbors – local information can lead to global wisdom
Emergence is a phenomena that is easily observable from thermal convection in liquids to the movement of slime mold to ant colonies to the growth of cities to even life itself.
One of the most difficult parts of the emergence paradigm to grasp by humans is that structures emerge without a leader, they are all decentralized, no one is in charge. But the real lesson of this book is that not all large systems of entities will exhibit emergent behavior. They must follow these principles.
Emergence: The Connected Lives of Ants, Brains, Cities and Software
Steven Johnson
Scribner, 2001, 288p
Lewis Thomas, 1973
This book by Steven Johnson is a good book to read if you’re just getting started on the topic of complexity science or interested in how to build emergent systems.
Reading on complexity is difficult. Words like complexity, and especially chaos, take on different meanings when you are talking about the science of complexity. Authors in the field don’t always agree on what the words mean. This book is on emergence that is a property of complex systems, another complication.
The author identifies Warren Weaver as the originator of the science of complexity in 1948. Weaver wrote an article for the American Scientist that identified the three regions of science that we’re still having difficultly naming. He called the three simplicity, disorganized complexity and organized complexity.
“…Weaver divided the last few centuries of scientific inquiry into three broad camps. First, the study of simple systems: two or three variable problems, such as the rotation of planets, or the connection between an electric current and its voltage and resistance. Second, problems of "disorganized complexity": problems characterized by millions or billions of variables that can only be approached by the methods of statistical mechanics and probability theory. These tools helped explain not only the behavior of molecules in a gas, or the patterns of heredity in a gene pool, but also helped life insurance companies turn a profit despite their limited knowledge about any individual human's future health. Thanks to Claude Shannon's work, the statistical approach also helped phone companies deliver more reliable and intelligible long distance service.
But there was a third phase to this progression, and we were only beginning to understand. ‘This statistical method of dealing with disorganized complexity, so powerful an advance over the earlier two-variable methods, leaves a great field untouched,’ Weaver wrote. There was a middle region between two-variable equations and problems that involved billions of variables. Conventionally, this region involved a "moderate" number of variables, but the size of the system was in fact a secondary characteristic:
Much more important than the mere number of variables is the fact that these variables are all interrelated.... These problems, as contrasted with the disorganized situations with which statistics can cope, show the essential feature of organization. We will therefore refer to this group of problems as those of organized complexity.”
The author describes the five fundamental principles of a system that will learn from the ground up, from local knowledge, and have the likely hood of exhibiting emergence:
• More is different - a critical mass must exist and individuals don’t “know” that they are organizing
• Ignorance is useful – simplicity of rules and language
• Random encounters – decentralized systems rely on random encounters
• Pattern detection – individuals have to be able to recognize patterns
• Interaction with neighbors – local information can lead to global wisdom
Emergence is a phenomena that is easily observable from thermal convection in liquids to the movement of slime mold to ant colonies to the growth of cities to even life itself.
One of the most difficult parts of the emergence paradigm to grasp by humans is that structures emerge without a leader, they are all decentralized, no one is in charge. But the real lesson of this book is that not all large systems of entities will exhibit emergent behavior. They must follow these principles.
Emergence: The Connected Lives of Ants, Brains, Cities and Software
Steven Johnson
Scribner, 2001, 288p
Complexity
This was an exciting, enlightening and humbling book for me to read. I was educated as a physicist and didn’t go past my masters’ degree partially because I was disenchanted with physics. The edge of physics had been left to the mathematical physicists. And, even though I minored in mathematics, this type of mathematics was beyond my capability to appreciate in physical terms. I could not visualize in real world terms what the equations meant. It seemed to be just a lot of mathematical manipulations that sometimes produced answers that described some aspect of reality.
Had I discovered the beginnings of the science of emergence, I might have stayed in the field. It’s a fresh new look at old phenomena with far ranging implications. I despaired when I learned that the field started in 1948 and was being worked on in the late 1950s when I left academia to work on semiconductors in industry. The Santa Fe Institute, an independent offshoot from Los Alamos Labs, was created in 1984 and work has spread all over the world since then.
From: Complexity, Wikipedia
Complexity science is not a single discipline, but a collaboration of disciplines. Some have called it a trans-disciplinary science. George Cowan, the creator of the Santa Fe Institute called it “the sciences of the twenty-first century.”
This book is a history of the development of the science of complexity and the formation of the Santa Fe Institute.
In addressing a number of questions unanswered by pre-complexity science, the author writes:
“In every case, moreover, the very richness of these interactions allows the system as a whole to undergo spontaneous self-organization. Thus, people trying to satisfy their material needs unconsciously organize themselves into an economy through myriad individual acts of buying and selling; it happens without anyone being in charge or consciously planning it. The genes in a developing embryo organize themselves in one way to make a liver cell and in another way to make a muscle cell. Flying birds adapt to the actions of their neighbors, unconsciously organizing themselves into a flock. Organisms constantly adapt to each other through evolution, thereby organizing themselves into an exquisitely tuned ecosystem. Atoms search for a minimum energy state by forming chemical bonds with each other, thereby organizing themselves into structures known as molecules. In every case, groups of agents seeking mutual accommodation and self-consistency somehow manage to transcend themselves, acquiring collective properties such as life, thought, and purpose that they might never have possessed individually.
Furthermore, these complex, self-organizing systems are adaptive, in that they don't just passively respond to events the way a rock might roll around in an earthquake. They actively try to turn whatever happens to their advantage. Thus, the human brain constantly organizes and reorganizes its billions of neural connections so as to learn from experience (sometimes, anyway). Species evolve for better survival in a changing environment-and so do corporations and industries. And the marketplace responds to changing tastes and lifestyles, immigration, technological developments, shifts in the price of raw materials, and a host of other factors.
Finally, every one of these complex, self-organizing, adaptive systems possesses a kind of dynamism that makes them qualitatively different from static objects such as computer chips or snowflakes, which are merely complicated. Complex systems are more spontaneous, more disorderly, more alive than that. At the same time, however, their peculiar dynamism is also a far cry from the weirdly unpredictable gyrations known as chaos. In the past two decades, chaos theory has shaken science to its foundations with the realization that very simple dynamical rules can give rise to extraordinarily intricate behavior; witness the endlessly detailed beauty of fractals, or the foaming turbulence of a river. And yet chaos by itself doesn't explain the structure, the coherence, the self-organizing cohesiveness of complex systems.
Instead, all these complex systems have somehow acquired the ability to bring order and chaos into a special kind of balance. This balance point-often called the edge of chaos-is were the components of a system never quite lock into place, and yet never quite dissolve into turbulence, either. The edge of chaos is where life has enough stability to sustain itself and enough creativity to deserve the name of life. The edge of chaos is where new ideas and innovative genotypes are forever nibbling away at the edges of the status quo, and where even the most entrenched old guard will eventually be overthrown. The edge of chaos is where centuries of slavery and segregation suddenly give way to the civil rights movement of the 1950s and 1960s; where seventy years of Soviet communism suddenly give way to political turmoil and ferment; where eons of evolutionary stability suddenly give way to wholesale species transformation. The edge of chaos is the constantly shifting battle zone between stagnation and anarchy, the one place where a complex system can be spontaneous, adaptive, and alive.”
One of the big stories featured in the book is the development of a complexity science for economics. Economics had become very mathematical as it responded to criticisms from the sciences that it was too “soft”. The result was very complex mathematical systems that did not represent the reality of economics. These mathematical systems were all based on an equilibrium model, which history has shown many times is not true for economic systems. Unfortunately, even with all the progress made by the Santa Fe Institute to develop a complexity science for economics, I see little impact in the present environment.
What we have seen is that highly structured and controlled economies have failed all around us.
The story is compelling and the writing fluid. It was a joy to read this book.
Complexity: The Emerging Science at the Edge of Order and Chaos
M. Mitchell Waldrop, Touchstone, 1992, 380 p
Had I discovered the beginnings of the science of emergence, I might have stayed in the field. It’s a fresh new look at old phenomena with far ranging implications. I despaired when I learned that the field started in 1948 and was being worked on in the late 1950s when I left academia to work on semiconductors in industry. The Santa Fe Institute, an independent offshoot from Los Alamos Labs, was created in 1984 and work has spread all over the world since then.
From: Complexity, Wikipedia
Complexity science is not a single discipline, but a collaboration of disciplines. Some have called it a trans-disciplinary science. George Cowan, the creator of the Santa Fe Institute called it “the sciences of the twenty-first century.”
This book is a history of the development of the science of complexity and the formation of the Santa Fe Institute.
In addressing a number of questions unanswered by pre-complexity science, the author writes:
“In every case, moreover, the very richness of these interactions allows the system as a whole to undergo spontaneous self-organization. Thus, people trying to satisfy their material needs unconsciously organize themselves into an economy through myriad individual acts of buying and selling; it happens without anyone being in charge or consciously planning it. The genes in a developing embryo organize themselves in one way to make a liver cell and in another way to make a muscle cell. Flying birds adapt to the actions of their neighbors, unconsciously organizing themselves into a flock. Organisms constantly adapt to each other through evolution, thereby organizing themselves into an exquisitely tuned ecosystem. Atoms search for a minimum energy state by forming chemical bonds with each other, thereby organizing themselves into structures known as molecules. In every case, groups of agents seeking mutual accommodation and self-consistency somehow manage to transcend themselves, acquiring collective properties such as life, thought, and purpose that they might never have possessed individually.
Furthermore, these complex, self-organizing systems are adaptive, in that they don't just passively respond to events the way a rock might roll around in an earthquake. They actively try to turn whatever happens to their advantage. Thus, the human brain constantly organizes and reorganizes its billions of neural connections so as to learn from experience (sometimes, anyway). Species evolve for better survival in a changing environment-and so do corporations and industries. And the marketplace responds to changing tastes and lifestyles, immigration, technological developments, shifts in the price of raw materials, and a host of other factors.
Finally, every one of these complex, self-organizing, adaptive systems possesses a kind of dynamism that makes them qualitatively different from static objects such as computer chips or snowflakes, which are merely complicated. Complex systems are more spontaneous, more disorderly, more alive than that. At the same time, however, their peculiar dynamism is also a far cry from the weirdly unpredictable gyrations known as chaos. In the past two decades, chaos theory has shaken science to its foundations with the realization that very simple dynamical rules can give rise to extraordinarily intricate behavior; witness the endlessly detailed beauty of fractals, or the foaming turbulence of a river. And yet chaos by itself doesn't explain the structure, the coherence, the self-organizing cohesiveness of complex systems.
Instead, all these complex systems have somehow acquired the ability to bring order and chaos into a special kind of balance. This balance point-often called the edge of chaos-is were the components of a system never quite lock into place, and yet never quite dissolve into turbulence, either. The edge of chaos is where life has enough stability to sustain itself and enough creativity to deserve the name of life. The edge of chaos is where new ideas and innovative genotypes are forever nibbling away at the edges of the status quo, and where even the most entrenched old guard will eventually be overthrown. The edge of chaos is where centuries of slavery and segregation suddenly give way to the civil rights movement of the 1950s and 1960s; where seventy years of Soviet communism suddenly give way to political turmoil and ferment; where eons of evolutionary stability suddenly give way to wholesale species transformation. The edge of chaos is the constantly shifting battle zone between stagnation and anarchy, the one place where a complex system can be spontaneous, adaptive, and alive.”
One of the big stories featured in the book is the development of a complexity science for economics. Economics had become very mathematical as it responded to criticisms from the sciences that it was too “soft”. The result was very complex mathematical systems that did not represent the reality of economics. These mathematical systems were all based on an equilibrium model, which history has shown many times is not true for economic systems. Unfortunately, even with all the progress made by the Santa Fe Institute to develop a complexity science for economics, I see little impact in the present environment.
What we have seen is that highly structured and controlled economies have failed all around us.
The story is compelling and the writing fluid. It was a joy to read this book.
Complexity: The Emerging Science at the Edge of Order and Chaos
M. Mitchell Waldrop, Touchstone, 1992, 380 p
Exploring Complexity
This book is an introduction to the science of complexity. Although written at a fairly elementary level, the chemistry, physics and mathematics are detailed. Therefore it is probably not a good book for some who is not interested in learning the basic mathematics.
Never the less, for those wishing to do the work, this is a very insightful book. It outlines and describes observations and understanding that are very revolutionary. The concepts of complexity science are no less fundamental than other well know advances in science and will have profound impact on our society.
As an example, th
e authors describe a simple experiment observed in 1900 whose results were not understood. Imagine two parallel plates with the space between them being filled with a liquid. If the temperature of the two plates and the liquid are all at the same temperature, the system is in equilibrium and nothing is moving or changing (at least at the macro level). However, if the temperature of one plate is raised, convection patterns begin to form patterns as shown in the drawing below. This was discovered by Benard in 1900. The flow patterns alternate – one clockwise and the next one counter clockwise.
These patterns emerge with no intelligence in fluid or a design. And, they are repeatable. If the temperature difference is increased, at some pint the patterns break down and chaos, or turbulence, is created that has a high degree of randomness.
I’m sure that you will note that these patterns look similar to what we observe in weather system. High pressure systems and low pressure system alternate directions of rotation.
Imagine yourself an observer in the liquid between the two plates. When the system is in equilibrium and nothing is changing, you would have no perception of time or space as everything would look the same. However, when the patterns emerge, time and space now have meaning. And, those meanings change again when it becomes chaotic.
For a summary of the book, I have chosen the authors’ words in the Preface:
“Our physical world is no longer symbolized by the stable and periodic planetary motions that are at the heart of classical mechanics. It is a world of instabilities and fluctuations, which are ultimately responsible for the amazing variety and richness of the forms and structures we see in nature around us. New concepts and new tools are clearly necessary to describe nature, in which evolution and pluralism become the key words. This book provides a short introduction to the methods devised over recent decades to explore complexity, be it at the level of molecules, of biological systems, or even of social systems.
We stress the role of two disciplines that have dramatically modified our outlook on complexity. The first is non-equilibrium physics. In this discipline the most unexpected outcome is the discovery of fundamental new properties of matter in far-from-equilibrium conditions. The second discipline is the modern theory of dynamical systems. Here, the central discovery is the prevalence of instability. Briefly, this means that small changes in initial conditions may lead to large amplifications of the effects of the changes.
The new methods developed in this context lead to a better understanding of the environment in which we live. In this environment we find both unexpected regularities as well as equally unexpected large-scale fluctuations. As evidence of regularity: matter is associated with an overwhelming dominance of particles over antiparticles, and life with a dominance of chiral (Note: Chirality, or "handedness", (Greek, χειρ, kheir: "hand") is a property of asymmetry important in several branches of science., asymmetric biomolecules over their symmetrical opposites. What could have been the selection mechanism giving rise to such large-scale regularities? Conversely, we could have expected uniformity and stability of our climatic conditions. However, contrary to such expectations, climate has fluctuated violently over periods quite short as compared to the characteristic time of the evolution of the sun. How is this possible? We now begin to have methods to address these questions.
The first chapter of this book presents selected examples of complex phenomena arising in the framework of physico-chemical and biological systems, as well environment at large. This description brings out a number of concepts that deal with mechanisms that are encountered repeatedly throughout the different phenomena; they are nonequilibrium, stability, bifurcation and symmetry breaking, and long-range order. In Chapter 2 these concepts are taken up and analyzed in more detail. They become the basic elements of what we believe to be a new scientific vocabulary, the vocabulary of complexity.
Following these two purely descriptive chapters, Chapter 3 addresses the problem of complexity from the standpoint of the modern theory of dynamical systems. We discuss some mechanisms by which nonlinear systems driven away from equilibrium can generate instabilities that lead to bifurcations and symmetry breaking. Special emphasis is placed in our analysis on the emergence of chaotic dynamics, the natural tendency of large classes of systems to evolve to states displaying both deterministic behavior and unpredictability.
Chapter 4 attempts a more detailed description of complex phenomena, going beyond the purely phenomenological level of the preceding chapters. We present the basic elements of probabilistic analysis of nonlinear nonequilibrium systems and construct a microscopic model of bifurcation and evolution. We also discuss some ways by which the concept of information can be integrated in the description of dynamical systems.
In the classical view, there was a sharp distinction between chance and necessity, between stochastic and deterministic behavior. The analysis in Chapters 3 and 4 shows that the situation is much more subtle. There are various forms of randomness, some of which are associated with the chaotic behavior of the solutions of simple deterministic equations. Chapter 5 addresses the question of the origin of randomness and irreversibility. We also discuss the closely related problem of understanding entropy and, in fact, the very concept of time. We believe that we begin to decipher the message of the celebrated second law of thermodynamics. We are living in a world of unstable processes, and this allows us to define an entropy function. Moreover, we live in a world in which the symmetry between past and future is broken; a world in which irreversible processes lead to equilibrium in our future. This universal prevalence of the breaking of time symmetry is at the heart of the second law.
We have expressed our conviction that science is bound to play an increasingly important role in our effort to understand our global environment. The ability to break the disciplinary barriers and to try new ways of looking at sometimes long standing problems is therefore one of the essential goals of the methods of analysis of complex phenomena set forth in this book. Chapter 6 demonstrates how this transfer of knowledge from one field to another can be envisaged. We devote much of this final chapter to questions that are beyond the realm of traditional concern in the physical sciences, such as the dynamics of climatic change, and the behavior of social insects and human populations. Obviously, each of these problems has its own specificities, and the possibility of a broad generalization should in no way be anticipated. Still, the role of nonlinearities and of fluctuations appears very clearly. It strongly suggests that the modeling of such systems should benefit from the new perspectives that the study of complex phenomena in nonlinear dynamical systems has provided to science.”
Exploring Complexity: An Introduction
Gregoire Nicolis and Ilya Prigogine
Freeman, 1989, 313 p
Never the less, for those wishing to do the work, this is a very insightful book. It outlines and describes observations and understanding that are very revolutionary. The concepts of complexity science are no less fundamental than other well know advances in science and will have profound impact on our society.
As an example, th
e authors describe a simple experiment observed in 1900 whose results were not understood. Imagine two parallel plates with the space between them being filled with a liquid. If the temperature of the two plates and the liquid are all at the same temperature, the system is in equilibrium and nothing is moving or changing (at least at the macro level). However, if the temperature of one plate is raised, convection patterns begin to form patterns as shown in the drawing below. This was discovered by Benard in 1900. The flow patterns alternate – one clockwise and the next one counter clockwise.These patterns emerge with no intelligence in fluid or a design. And, they are repeatable. If the temperature difference is increased, at some pint the patterns break down and chaos, or turbulence, is created that has a high degree of randomness.
I’m sure that you will note that these patterns look similar to what we observe in weather system. High pressure systems and low pressure system alternate directions of rotation.
Imagine yourself an observer in the liquid between the two plates. When the system is in equilibrium and nothing is changing, you would have no perception of time or space as everything would look the same. However, when the patterns emerge, time and space now have meaning. And, those meanings change again when it becomes chaotic.
For a summary of the book, I have chosen the authors’ words in the Preface:
“Our physical world is no longer symbolized by the stable and periodic planetary motions that are at the heart of classical mechanics. It is a world of instabilities and fluctuations, which are ultimately responsible for the amazing variety and richness of the forms and structures we see in nature around us. New concepts and new tools are clearly necessary to describe nature, in which evolution and pluralism become the key words. This book provides a short introduction to the methods devised over recent decades to explore complexity, be it at the level of molecules, of biological systems, or even of social systems.
We stress the role of two disciplines that have dramatically modified our outlook on complexity. The first is non-equilibrium physics. In this discipline the most unexpected outcome is the discovery of fundamental new properties of matter in far-from-equilibrium conditions. The second discipline is the modern theory of dynamical systems. Here, the central discovery is the prevalence of instability. Briefly, this means that small changes in initial conditions may lead to large amplifications of the effects of the changes.
The new methods developed in this context lead to a better understanding of the environment in which we live. In this environment we find both unexpected regularities as well as equally unexpected large-scale fluctuations. As evidence of regularity: matter is associated with an overwhelming dominance of particles over antiparticles, and life with a dominance of chiral (Note: Chirality, or "handedness", (Greek, χειρ, kheir: "hand") is a property of asymmetry important in several branches of science., asymmetric biomolecules over their symmetrical opposites. What could have been the selection mechanism giving rise to such large-scale regularities? Conversely, we could have expected uniformity and stability of our climatic conditions. However, contrary to such expectations, climate has fluctuated violently over periods quite short as compared to the characteristic time of the evolution of the sun. How is this possible? We now begin to have methods to address these questions.
The first chapter of this book presents selected examples of complex phenomena arising in the framework of physico-chemical and biological systems, as well environment at large. This description brings out a number of concepts that deal with mechanisms that are encountered repeatedly throughout the different phenomena; they are nonequilibrium, stability, bifurcation and symmetry breaking, and long-range order. In Chapter 2 these concepts are taken up and analyzed in more detail. They become the basic elements of what we believe to be a new scientific vocabulary, the vocabulary of complexity.
Following these two purely descriptive chapters, Chapter 3 addresses the problem of complexity from the standpoint of the modern theory of dynamical systems. We discuss some mechanisms by which nonlinear systems driven away from equilibrium can generate instabilities that lead to bifurcations and symmetry breaking. Special emphasis is placed in our analysis on the emergence of chaotic dynamics, the natural tendency of large classes of systems to evolve to states displaying both deterministic behavior and unpredictability.
Chapter 4 attempts a more detailed description of complex phenomena, going beyond the purely phenomenological level of the preceding chapters. We present the basic elements of probabilistic analysis of nonlinear nonequilibrium systems and construct a microscopic model of bifurcation and evolution. We also discuss some ways by which the concept of information can be integrated in the description of dynamical systems.
In the classical view, there was a sharp distinction between chance and necessity, between stochastic and deterministic behavior. The analysis in Chapters 3 and 4 shows that the situation is much more subtle. There are various forms of randomness, some of which are associated with the chaotic behavior of the solutions of simple deterministic equations. Chapter 5 addresses the question of the origin of randomness and irreversibility. We also discuss the closely related problem of understanding entropy and, in fact, the very concept of time. We believe that we begin to decipher the message of the celebrated second law of thermodynamics. We are living in a world of unstable processes, and this allows us to define an entropy function. Moreover, we live in a world in which the symmetry between past and future is broken; a world in which irreversible processes lead to equilibrium in our future. This universal prevalence of the breaking of time symmetry is at the heart of the second law.
We have expressed our conviction that science is bound to play an increasingly important role in our effort to understand our global environment. The ability to break the disciplinary barriers and to try new ways of looking at sometimes long standing problems is therefore one of the essential goals of the methods of analysis of complex phenomena set forth in this book. Chapter 6 demonstrates how this transfer of knowledge from one field to another can be envisaged. We devote much of this final chapter to questions that are beyond the realm of traditional concern in the physical sciences, such as the dynamics of climatic change, and the behavior of social insects and human populations. Obviously, each of these problems has its own specificities, and the possibility of a broad generalization should in no way be anticipated. Still, the role of nonlinearities and of fluctuations appears very clearly. It strongly suggests that the modeling of such systems should benefit from the new perspectives that the study of complex phenomena in nonlinear dynamical systems has provided to science.”
Exploring Complexity: An Introduction
Gregoire Nicolis and Ilya Prigogine
Freeman, 1989, 313 p
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