Showing posts with label complexity. Show all posts
Showing posts with label complexity. Show all posts

A New Kind of Science Review

A New Kind of Science
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This review took almost one year. Unlike many previous referees (rank them by Amazon.com's "most helpful" feature) I read all 1197 pages including notes. Just to make sure I won't miss the odd novel insight hidden among a million trivial platitudes.
On page 27 Wolfram explains "probably the single most surprising discovery I have ever made:" a simple program can produce output that seems irregular and complex.
This has been known for six decades. Every computer science (CS) student knows the dovetailer, a very simple 2 line program that systematically lists and executes all possible programs for a universal computersuch as a Turing machine (TM). It computes all computable patterns, including all those in Wolfram's book, embodies the well-known limits of computability, and is basis of uncountable CS exercises.
Wolfram does know (page 1119) Minsky's very simple universal TMs from the 1960s. Using extensive simulations, he finds a slightly simpler one. New science? Small addition to old science. On page 675 we find a particularly simple cellular automaton (CA) and Matthew Cook's universality proof(?). This might be the most interesting chapter. It reflects that today's PCs are more powerful systematic searchers for simple rules than those of 40 years ago. No new paradigm though.
Was Wolfram at least first to view programs as potential explanations of everything? Nope. That was Zuse. Wolfram mentions him in exactly one line (page 1026): "Konrad Zuse suggested that [the universe] could be a continuous CA." This is totally misleading. Zuse's 1967 paper suggested the universe is DISCRETELY computable, possibly on a DISCRETE CA just like Wolfram's. Wolfram's causal networks (CA's with variable toplogy, chapter 9) will run on any universal CA a la Ulam & von Neumann & Conway & Zuse. Page 715 explains Wolfram's "key unifying idea" of the "principle of computational equivalence:" all processes can be viewed as computations. Well, that's exactly what Zuse wrote 3 decades ago.
Chapter 9 (2nd law of thermodynamics) elaborates (without reference)on Zuse's old insight that entropy cannot really increase in deterministically computed systems, although it often SEEMS to increase. Wolfram extends Zuse's work by a tiny margin, using today's more powerful computers to perform experiments as suggested in Zuse's 1969 book. I find it embarassing how Wolfram tries to suggest it was him who shifted a paradigm, not the legendary Zuse.
Some reviews cite Wolfram's previous reputation as a physicist and software entrepreneur, giving him the benefit of the doubt instead of immediately dismissing him as just another plagiator. Zuse's reputation is in a different league though: He built world's very first general purpose computers (1935-1941), while Wolfram is just one of many creators of useful software (Mathematica). Remarkably, in his history of computing (page 1107) Wolfram appears to try to diminuish Zuse's contributions by only mentioning Aiken's later 1944 machine.
On page 465 ff (and 505 ff on multiway systems) Wolfram asks whether there is a simple program that computes the universe. Here he sounds like Schmidhuber in his 1997 paper "A Computer Scientist's View of Life, the Universe, and Everything." Schmidhuber applied the above-mentioned simple dovetailer to all computable universes. His widely known writings come out on top when you google for "computable universes" etc, so Wolfram must have known them too, for he read an "immense number of articles books and web sites" (page xii) and executed "more than a hundred thousand mouse miles" (page xiv). He endorses Schmidhuber's "no-CA-but-TM approach" (page 486, no reference) but not his suggestion of using Levin's asymptotically optimal program searcher (1973) to find our universe's code.
On page 469 we are told that the simplest program for the data is the most probable one. No mention of the very science based on this ancient principle: Solomonoff's inductive inference theory (1960-1978); recent optimality results by Merhav & Feder & Hutter. Following Schmidhuber's "algorithmic theories of everything" (2000), short world-explaining programs are necessarily more likely, provided the world is sampled from a limit-computable prior distribution. Compare Li & Vitanyi's excellent 1997 textbook on Kolmogorov complexity.
On page 628 ff we find a lot of words on human thinking and short programs. As if this was novel! Wolfram seems totally unaware of Hutter's optimal universal rational agents (2001) based on simple programs a la Solomonoff & Kolmogorov & Levin & Chaitin. Wolfram suggests his simple programs will contribute to fine arts (page 11), neither mentioning existing, widely used, very short, fractal-based programs for computing realistic images of mountains and plants, nor the only existing art form explicitly based on simple programs: Schmidhuber's low-complexity art.
Wolfram talks a lot about reversible CAs but little about Edward Fredkin & Tom Toffoli who pioneered this field. He ignores Wheeler's "it from bit," Tegmark & Greenspan & Petrov & Marchal's papers, Moravec & Kurzweil's somewhat related books, and Greg Egan's fun SF on CA-based universes (Permutation City, 1995).
When the book came out some non-expert journalists hyped it without knowing its contents. Then cognoscenti had a look at it and recognized it as a rehash of old ideas, plus pretty pictures. And the reviews got worse and worse. As far as I can judge, positive reviews were written only by people without basic CS education and little knowledge of CS history. Some biologists and even a few physicists initially were impressed because to them it really seemed new. Maybe Wolfram's switch from physics to CS explains why he believes his thoughts are radical, not just reinventions of the wheel.
But he does know Goedel and Zuse and Turing. He must see that his own work is minor in comparison. Why does he desparately try to convince us otherwise? When I read Wolfram's first praise of the originality of his own ideas I just had to laugh. The tenth time was annoying. The hundredth time was boring. And that was my final feeling when I laid down this extremely repetitive book:exhaustion and boredom. In hindsight I know I could have saved my time. But at least I can warn others.

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Thinking in Complexity: The Computational Dynamics of Matter, Mind, and Mankind Review

Thinking in Complexity: The Computational Dynamics of Matter, Mind, and Mankind
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[A review of the 4th Edition, 2003.]
This book studies complexity and nonlinearity across a diverse range of applications. Much of the book revolves around organic evolution and the evolution of a sentient mind. And how complexity analysis might aid in the understanding of these fields. Not the least in devising deeper forms of artificial intelligence.
So intriguing techniques like cellular automata and neural networks are studied. There is a fair amount of speculation as to how these and other topics might ultimately relate to sentience or consciousness. But the musings are grounded in solid science. Like that of a Hopfield system or a Boltzmann machine. This 4th edition is a good reflection of the boundaries of our knowledge.

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Individual-based Modeling and Ecology (Princeton Series in Theoretical and Computational Biology) Review

Individual-based Modeling and Ecology (Princeton Series in Theoretical and Computational Biology)
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After having read this book from cover to cover - I currently consider it a "Must Have" on the bookshelf of anyone who is serious about ecological modelling or complex systems. The book is comprehensive, clear, honest, deep and enlightening.

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Modeling Complex Systems (Graduate Texts in Contemporary Physics) Review

Modeling Complex Systems (Graduate Texts in Contemporary Physics)
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This is a fine book to learn the state of the art in 2004 in the field of complex systems modeling. It has the right blend of useful illustrations from many types of applications and of clean mathematics, without overdoing it in terms of abstraction. It is expensive but I don't regret my purchase.

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This book explores the process of modeling complex systems in the widest sense of that term, drawing on examples from such diverse fields as ecology, epidemiology, sociology, seismology, as well as economics. It also provides the mathematical tools for studying the dynamics of these systems. Boccara takes a carefully inductive approach in defining what it means for a system to be "complex" (and at the same time addresses the equally elusive concept of emergent properties). This is the first text on the subject to draw comprehensive conclusions from such a wide range of analogous phenomena.

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Computational and Mathematical Modeling in the Social Sciences Review

Computational and Mathematical Modeling in the Social Sciences
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Imagine that you're out for a relaxing dinner at your neighborhood bistro. Your waiter, a lanky young lad named Trey, sidles up to your table and describes the evening's specials beginning with a free range, grilled, Sonoma chicken bathed in a white wine and balsamic reduction and peppered with bits of black truffle. You think that the dish sounds wonderful and accept it for consumption with no revisions.
After reading this book, I can only guess that Scott de Marchi's reaction would be a little different. He'd point out that grilling was just one of many options. Alternatively, the chef could have fried it, baked it, braised it, seared it, roasted it, or even cooked it at low temperature in a Ziplock bag. Why did the chef choose grilling? And, oh by the way, why free range Sonoma chicken? Why not organic chicken - wouldn't its stronger taste hold up better to the wine and balsamic reduction? Heck, why not go all out and get one of those chickens that was hand-fed corn mash by Italian monks who gave it daily massages and hour long walks thru Tuscan valleys? And what about the number of possible spice and sauce combinations? Why black truffles?
The chicken entrée suffers from a curse of dimensionality. By modifying our choices on each dimension, we can create enough chicken variations to awe even the late Carl Sagan. Gourmands benefit from the curse - we can expect an original special every week. Social scientists - historians, theorists and empiricists alike - take the curse on the chin. It obliges a rethinking of how we construct and evaluate a model, or so says Scott de Marchi in this fascinating and challenging new book. To feel the effects of the curse, suppose that you're writing an empirical model of why countries go to war. In choosing variables for your regression, you pick ten from a set of twenty. You then toss in an interaction term, chosen with great care from the forty-five possible pairs. You then choose a model specification - linear, log linear, non parametric, or whatever. When you step back and look at the process of creating your empirical model, you realize that you have as many possible regressions as the bistro's chef has chicken entrees.
Suppose instead that you're writing a game theory model of first strikes. Professor de Marchi has a few questions to ask: Is the game one shot or repeated? Are moves sequential or simultaneous? Is information asymmetric? Are the players risk averse? Are preferences separable?
Given the billions of possible model specifications, the task of finding significant coefficients or proving (wink-wink) a general theorem suddenly doesn't look so impressive. Even combining the two: integrating a theoretical model and empirical analysis (EITM anyone?) looks about as hard as cooking up a little Bonferroni chicken to go with that Oregon Coast Pinot Noir. Once aware of the existence of the curse, we can see no shortage of naked emperors (some of whom de Marchi reveals with some relish). We can also try to get around it, to conjure up a counter hex.
The counter hex proposed by de Marchi consists of three parts. First, he wants us to split our data into training sets and testing sets -- a good idea, but it comes with a cost. A little math shows that dividing the data results in data sets that are, on average, only half as big, so we'll need a lot more wars for IR to have any hope of finding statistical significance.
Second, he wants us to analyze classes of models and not individual models with idiosyncratic (and possibly brittle) assumptions. In demanding that we consider classes of models, de Marchi implicitly charges some mathematical theorists with selling magic beans in the form of theorems that rely on specific functional forms. Results for a single functional form do not a general theorem make. The difference between a three person, three alternative example of a Condercet Cycle and Arrow's Possibility Theorem is the difference between predicting that a falling apple will hit the ground and formulating the theory of gravity. But proving general results is not easy. In fact, few general results exist. So why not be honest about the lack of generality rather than cooking up specific models that give the desired result?
The proposed solution, to create a feature space (dimensions on which we make various assumptions) and explore all of the models within that space, sounds good but it creates a problem unless we can increase the birth rates in Pasadena and Rochester. We still have too many models to explore. To get around the problem of too many models and too little time, de Marchi has a novel solution: use computational methods to explore the space of possible models. If we're using specific functional forms anyway, we might as well simulate them and not bother with formal proofs. Simulation is quicker. By simulating within feature space, we can distinguish robust findings from brittle examples. This approach requires combining art and science. We must constrain the feature space so that we're merely stunned and not cursed by the dimensionality.
Third, de Marchi wants our models to be more realistic. (Who doesn't?) But, how do we achieve realism and yet maintain a limited feature space that we explore in depth? Can we be realistic and remain within or at least comfortably near Chris Achen's three-variable world? de Marchi believes that we can, provided that we start simple and build up toward realism. Thus, we have complicated models as the sum of lots of simple models, all of which we understand fully as a result of exploring their feature spaces. As an example of a realistic model, he goes outside of social science and looks at machine chess programs. These programs don't just apply to chess in some metaphorical sense, as in "the Colonel Blotto game captures the essence of chess." They actually play chess and play better than people do. Having a model that plays chess produces a further advantage: the modeler can use real data from games.
Let's suppose we take a vote between continuing with the status quo and accepting de Marchi's vision of the future. The status quo consists of unrealistic, narrow models that we test using all of our data with substantial freedom over what control variables we include. de Marchi's alternative consists of cumulative realistic models (as well as nearby models to make sure that our theory is robust) that are calibrated on training sets and tested on separate data. The vote would be Roosevelt-Landon 1936 all over again. Apart from some holdouts in Vermont and Maine, de Marchi would win everywhere.However, in this election, we don't just pull levers. We have to vote with our heads, which can be thick and slow to respond.
The path de Marchi would like us to take requires nontrivial changes in how we build models and how we test them. Sure we can learn to split our data sets in two. But will we learn Perl? Will we take the time to construct a feature space? And what if that feature space reveals brittleness? Will we bail out and write a paper with quasi-linear preferences or with a one-dimensional preference space? Not only does he require that we learn new tools, he's asking us to change our standards. Rather than bestow awards on books that consist of (a) a captivating anecdote from history (b) a narrow specific functional form model that provides the key intuition (c) an empirical test with ten control variables and one interactive term that demonstrates validity of key intuition and (d) a rich case study that fills in all the gaps, we might see these books as cursed by problems of dimensionality. With so much history, so many models, and so many variables to choose from, these books should be as easy to make as the Chicken Marbella from The Silver Palate.
This critique of the status quo may get under the skin of some readers. Sure, your average PhD student can choose from among thousands of theoretical models and econometric specifications, but finding two that align -- where the econometrics support what the theory predicts - is not as easy as he makes it sound. If it were, we'd have many more papers that met this standard, and we wouldn't have summer courses sponsored by the NSF teaching students how to integrate these methods taught by (among others) Scott de Marchi. Furthermore, the models in these award-winning books aren't all that brittle. They do meet qualitative robustness criteria. Most theorists and econometricians can sniff out rigged models. We can tell a universal insight from a unicorn. When we see a model with quasi-linear preferences or the monotone likelihood ratio property, we know the rabbit has been placed in the hat, and we take the author to task accordingly.
Given that we're all aware of the curse, and we're qualitatively mindful of it when evaluating research, de Marchi's claims seem less provocative, and at the same time, more reasonable. He's advocating that we supplement our reasoned judgment with a scientific approach based on feature spaces and computational models. Any time we can replace subjective criteria with more objective, scientific criteria, we move science forward, which this book urges us to do.
Some critiques may complain that this book explains how to do it, but it doesn't actually do it. True, the book would be stronger if it took us on a complete tour of the shiny new city on the hill that it constructs. A short chapter on how de Marchi built a model (and critics may say an unrealistic one - ouch!) along with a smidgen of Perl code won't sway the masses. The book would be more convincing if had a six hundred page companion volume that took on a puzzle, defined and explored a feature space, tested the robust conclusions out of...Read more›

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Mathematical models in the social sciences have become increasingly sophisticated and widespread in the last decade.This period has also seen many critiques, most lamenting the sacrifices incurred in pursuit of mathematical perfection. If, as critics argue, our ability to understand the world has not improved during the mathematization of the social sciences, we might want to adopt a different paradigm.This book examines the three main fields of mathematical modeling--game theory, statistics, and computational methods--and proposes a new framework for modeling.

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Cellular Automata Machines: A New Environment for Modeling (Scientific Computation) Review

Cellular Automata Machines: A New Environment for Modeling (Scientific Computation)
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This is a terrific book that takes a step-by-step approach to cellular automata, especially for modelling. Within the first two chapters I had already found several interesting ideas for improving my own general-purpose automata program.
The part of the book that is most dated is the discussion of a specific hardware card and software designed for IBM PCs and ATs, and a specific dialect of Forth that can be used to program automata that will run on this card. Obviously this is no longer the mainstream approach to programming automata - even massively parallel systems programming has moved away from Forth. For me, I think of it as pseudo-code instead of a program example, and the book is still very very useful.
So on the whole, I would say this is a valuable addition to the bookshelf of any automata enthusiast.

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Applied Dimensional Analysis and Modeling, Second Edition Review

Applied Dimensional Analysis and Modeling, Second Edition
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Thomas Szirtes' _Applied Dimensional Analysis and Modeling_ is an encyclopedic beast of a book. There are many, many worked examples, both standards that are found in Bridgeman and others (like the period of a pendulum), and novel ones.It also has all of the tricks that allow for effective use of the techniques of dimensional analysis in more complicated problems like breaking the mass into inertial and gravitational aspects to solve problems (and send your thoughts down the road of bad philosophy). For this reason alone it is a useful book.
It has some problems, however, that make it difficult for me to recommend the book to someone who doesn't have a good grasp of units, dimensions, and the difference between the two, even though Szirtes intends the book for those with only "an inquisitive mind and a knowledge of basic mechanics and electricity" and "elementary matrix arithmetic." These problems are:
1.Szirtes' use of dimensions and units is non-standard, calling meters a dimension rather than a unit, making ideas like coversion more difficult and some of the examples more convoluted than they need to be,
2.Many of the problems and examples have implied units, so that he might write a formula for velocity in terms of time as v = 9.81 t + 3.2 [this is not in the text, I use it because it's simple],
3.The more mathematical sections include sloppy proofs that, in my view, don't yield any additional understanding.
These are all serious problems for a beginner, who could pick up some bad habits from the book. Something that makes the book a little less useful than it could be is a paucity of electricity and magnetism examples, which are mechanics heavy.
I think this book would be a good introduction for someone with a solid background in physics or engineering or someone who has looked at a less challenging or thorough book in dimensional analysis (such as Bridgeman). I also think it is also a good book for instructors, being a treasure trove of examples, even if they should be sanitized before being given to students.


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Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Studies in Complexity) Review

Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Studies in Complexity)
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Josh Epstein's new Opus is a landmark publication in the emerging field of multiagent-based simulation of dynamic social systems. Since Josh is not only one of this still nascent (though burgeoning) field's ablest and most creative practitioners, but also among its most thoughtful critics, the reader of has two treats in store: (1) a generous, and wide-ranging, sampling of case studies (including social networks and evolution, population growth, emergence of economic classes, civil unrest, timing of retirement, the dynamics of adaptive organizations and the spread of infectious disease), and (2) a cogent "meta" discussion of what multiagent models ARE, ARE NOT and how (when their properties and limitations are *not* properly taken account of) they can easily be MISAPPLIED.
Far from suggesting that multiagent-based models are a panacea solution to all (or most) social dynamical systems, Josh's book carefully articulates the conditions for which such an approach IS (and is NOT) appropriate; an approach rarely taken by other, similar, overviews of the field. Indeed, the cogent philosophical discussion in Chapter One - alone! - in which the generativist's position is defined and put into a broader modeling/simulation context, is worth the price of admission; I have not seen a better "manifesto" of multiagent-based modeling elsewhere.
Finally, without taking away any of the inherent "beauty" (in the technical sense) of the often exaggerated concept of "emergence," Josh succeeds admirably in both defining the term, and de-mystifying it, stripping it of some of its unnecessary "quasi-mystical" baggage (at least as it is often portrayed in lay publications).
Anyone who is interested in understanding how agent models may be used to help explore the dynamics of social dynamical systems, should have this book firmly on top of their "must read" list! Josh has generously provided future generations of agent explorers their go-to source of both inspiration and ideas. Well done Josh!

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Agent-based computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation.

This book represents a powerful consolidation of Epstein's interdisciplinary research activities in the decade since the publication of his and Robert Axtell's landmark volume, Growing Artificial Societies. Beautifully illustrated, Generative Social Science includes a CD that contains animated movies of core model runs, and programs allowing users to easily change assumptions and explore models, making it an invaluable text for courses in modeling at all levels.


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