The End of Chaoplexity
Locked
 
|
Chaoplexologists,
And I used to like Scientific American! This guy is practically a Creationist. From: John Horgan, senior writer for Scientific American "The End of Science: Facing the Limits of Knowledge in the Twilight of the= =20 Scientific Age" -------------------------------------- Quotes from: "The End of Chaoplexity" http://freeinfo.org/tch/fall99/articles/horgan.html "So far, chaoplexologists have created some potent metaphors: the butterfly effect, fractals, artificial life, the edge of chaos, self-organized criticality. But they have not told us anything about the world that is both concrete and truly surprising. . . . " "I think the important thing for us is to grow, not to remain in our own present stupid state. "(Marvin Minsky) "We don't need something else in order to get something else." (Murray=20 Gell-Mann) -------------------------------------- Quotes from: The Electrochemical Society Interface Winter 1998 =93It won't be heaven or hell, post-science. But remember we=92ll still have sex and beer.=94 "Horgan stated that math was an invention with cognitive limits." -------------------------------------- Quotes from: "Why I Think Science Is Ending" A Talk With John Horgan http://www.edge.org/3rd_culture/horgan/horgan_p1.html "6. The Chaoplexity Gambit Many modern scientists hope that advances in computers and mathematics will enable them to transcend their current knowledge and create a powerful new science. This is the faith that sustains the trendy fields of chaos and complexity. In my book I lump chaos and complexity together under a single term, chaoplexity, because after reading dozens of books about chaos and complexity and talking to scores of people in both fields, I realized that there is no significant difference between them. Chaoplexologists have argued that with more powerful computers and mathematics they can answer age-old questions about the inevitability, or lack thereof, of life, or even of the entire universe. They can find new laws of nature analogous to gravity or the second law of thermodynamics. They can make economics and other social sciences as rigorous as physics. They can find a cure for AIDS. These are all claims that have been made by researchers at the Santa Fe Institute. These claims stem from an overly optimistic interpretation of certain developments in computer science. Over the past few decades, researchers have found that various simple rules, when followed by a computer, can generate patterns that appear to vary randomly as a function of time or scale. Let's call this illusory randomness "pseudo-noise." A paradigmatic example of a pseudo-noisy system is the mother of all fractals, the Mandelbrot set, which is an icon of the chaoplexity movement. The fields of both chaos and complexity have held out the hope that much of the noise that seems to pervade nature is actually pseudo-noise, the result of some underlying, deterministic algorithm. But the noise that makes it so difficult to predict earthquakes, the stock market, the weather and other phenomena is not apparent but very real. This kind of noisiness will never be reduced to any simple set of rules, in my view. Of course, faster computers and advanced mathematical techniques will improve our ability to predict certain complicated phenomena. Popular impressions notwithstanding, weather forecasting has become more accurate over the last few decades, in part because of improvements in computer modeling. But an even more important factor is improvements in data-gathering=97notably satellite imaging. Meteorologists have a larger, more accurate database upon which to build their models and against which to test them. Forecasts improve through this dialectic between simulation and data-gathering. At some point, we are drifting over the line from science per se toward engineering. The model either works or doesn't work according to some standard of effectiveness; "truth" is irrelevant. Moreover, chaos theory tells us that there is a fundamental limit to forecasting related to the butterfly effect. One has to know the initial conditions of a system with infinite precision to be able to predict its course. This is something that has always puzzled me about chaoplexologists: according to one of their fundamental tenets, the butterfly effect, many of their goals may be impossible to achieve. " -------------------------------------- Chaoplexity is dead, long live "pseudo-noise"-ology! Regards, Lanny |
|
|
"This kind of noisiness will never
be reduced to any simple set of rules, in my view." Oh, now *there's* a cogent argument. Carl -----Original Message----- From: [hidden email] [mailto:[hidden email]]On Behalf Of Lanny H. Bear Sent: Saturday, February 22, 2003 12:05 PM To: [hidden email] Subject: [FRIAM] The End of Chaoplexity Chaoplexologists, And I used to like Scientific American! This guy is practically a Creationist. From: John Horgan, senior writer for Scientific American "The End of Science: Facing the Limits of Knowledge in the Twilight of the Scientific Age" -------------------------------------- Quotes from: "The End of Chaoplexity" http://freeinfo.org/tch/fall99/articles/horgan.html "So far, chaoplexologists have created some potent metaphors: the butterfly effect, fractals, artificial life, the edge of chaos, self-organized criticality. But they have not told us anything about the world that is both concrete and truly surprising. . . . " "I think the important thing for us is to grow, not to remain in our own present stupid state. "(Marvin Minsky) "We don't need something else in order to get something else." (Murray Gell-Mann) -------------------------------------- Quotes from: The Electrochemical Society Interface Winter 1998 It won't be heaven or hell, post-science. But remember well still have sex and beer. "Horgan stated that math was an invention with cognitive limits." -------------------------------------- Quotes from: "Why I Think Science Is Ending" A Talk With John Horgan http://www.edge.org/3rd_culture/horgan/horgan_p1.html "6. The Chaoplexity Gambit Many modern scientists hope that advances in computers and mathematics will enable them to transcend their current knowledge and create a powerful new science. This is the faith that sustains the trendy fields of chaos and complexity. In my book I lump chaos and complexity together under a single term, chaoplexity, because after reading dozens of books about chaos and complexity and talking to scores of people in both fields, I realized that there is no significant difference between them. Chaoplexologists have argued that with more powerful computers and mathematics they can answer age-old questions about the inevitability, or lack thereof, of life, or even of the entire universe. They can find new laws of nature analogous to gravity or the second law of thermodynamics. They can make economics and other social sciences as rigorous as physics. They can find a cure for AIDS. These are all claims that have been made by researchers at the Santa Fe Institute. These claims stem from an overly optimistic interpretation of certain developments in computer science. Over the past few decades, researchers have found that various simple rules, when followed by a computer, can generate patterns that appear to vary randomly as a function of time or scale. Let's call this illusory randomness "pseudo-noise." A paradigmatic example of a pseudo-noisy system is the mother of all fractals, the Mandelbrot set, which is an icon of the chaoplexity movement. The fields of both chaos and complexity have held out the hope that much of the noise that seems to pervade nature is actually pseudo-noise, the result of some underlying, deterministic algorithm. But the noise that makes it so difficult to predict earthquakes, the stock market, the weather and other phenomena is not apparent but very real. This kind of noisiness will never be reduced to any simple set of rules, in my view. Of course, faster computers and advanced mathematical techniques will improve our ability to predict certain complicated phenomena. Popular impressions notwithstanding, weather forecasting has become more accurate over the last few decades, in part because of improvements in computer modeling. But an even more important factor is improvements in data-gatheringnotably satellite imaging. Meteorologists have a larger, more accurate database upon which to build their models and against which to test them. Forecasts improve through this dialectic between simulation and data-gathering. At some point, we are drifting over the line from science per se toward engineering. The model either works or doesn't work according to some standard of effectiveness; "truth" is irrelevant. Moreover, chaos theory tells us that there is a fundamental limit to forecasting related to the butterfly effect. One has to know the initial conditions of a system with infinite precision to be able to predict its course. This is something that has always puzzled me about chaoplexologists: according to one of their fundamental tenets, the butterfly effect, many of their goals may be impossible to achieve. " -------------------------------------- Chaoplexity is dead, long live "pseudo-noise"-ology! Regards, Lanny =================== FRIAM Complexity Coffee listserv Meets Fridays 9AM @ Museum Hill Cafe Archives, unsubscribe, etc.: http://www.redfish.com/mailman/listinfo/friam_redfish.com |
|
|
In reply to this post by Friam mailing list
On Saturday, February 22, 2003, at 12:05 PM, Lanny H. Bear wrote:
> From: > John Horgan, senior writer for Scientific American > "The End of Science: Facing the Limits of Knowledge in the Twilight of > the Scientific Age" First, I think that a recognition of limitations is often a sign of a mature and solid science; not a sign of its decay. What about Goedel's proof, the discovery of an infinite number of uncomputable problems (for example the halting problem), the impossibility of reverse time travel? None of these are signs that mathematics, computer science, and physics are dead. Of course this is science, and under a different system of rules these limitations may disappear, but the definition of boundaries on a given discipline is not the "End of Science". > In my book I lump chaos and complexity > together under a single term, chaoplexity, > because after reading dozens of books > about chaos and complexity and talking > to scores of people in both fields, I > realized that there is no significant > difference between them. Yeah? Well good for you Mr. Horgan. I believe (and I think most other researchers agree) that there is a significant and important distinction between chaos and complexity. Chaos can be defined simply and easily as an extreme sensitivity to initial conditions, leading to large scale and unpredictable differences from predicted results after only a short period of system evolution. Complexity, on the other hand, is much more... well... complex, and difficult to define. It relates to the absence of a simple, short description of a system. It relates to the presence of a large number of interconnected or interdependent components. It related to self-organization and adaptation. > Over the past few decades, researchers have > found that various simple rules, when > followed by a computer, can generate > patterns that appear to vary randomly as a > function of time or scale. Let's call this > illusory randomness "pseudo-noise." A > paradigmatic example of a pseudo-noisy > system is the mother of all fractals, the > Mandelbrot set, which is an icon of the > chaoplexity movement. > > The fields of both chaos and complexity > have held out the hope that much of the > noise that seems to pervade nature is > actually pseudo-noise, the result of some > underlying, deterministic algorithm. But > the noise that makes it so difficult to > predict earthquakes, the stock market, the > weather and other phenomena is not > apparent but very real. This kind of > noisiness will never be reduced to any > simple set of rules, in my view. Once again, the "psuedo-noise" created by chaos is very different from the apparent randomness that can be observed in other systems. "Randomness" in a system can can be from three sources: 1) An outside force that continually introduces perturbations. 2) An extreme sensitivity to initial conditions (chaos). 3) A continual, internal generation of "randomness" by the system itself (see "A New Kind of Science", by Stephen Wolfram, p. 299). This internal generation of apparent randomness is also related to complexity. -dan |
«
Return to Friam
|
1 view|%1 views
| Free forum by Nabble | Edit this page |