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A New Kind of Science ハードカバー – イラスト付き, 2002/5/14
英語版
Stephen Wolfram
(著)
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購入オプションとあわせ買い
Challenging the traditional mathematical model of scientific description, a scientist proposes a new dynamic computational approach that utilizes simple codes to generate patterns of ultimate complexity.
- 本の長さ1197ページ
- 言語英語
- 出版社Wolfram Media Inc
- 発売日2002/5/14
- 寸法20.96 x 6.35 x 24.77 cm
- ISBN-101579550088
- ISBN-13978-1579550080
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商品の説明
商品説明
世界で最も尊敬されている科学者のひとりによるこの待望の著作には、これまで公表されることのなかった感動の発見の数々が紹介されている。単純なコンピュータ試験の話から始まって(本の中の図版は、目を見張るようなコンピュータ・グラフィックス)、ウルフラムは、これらの発見がもたらした予期せぬ結果によって、宇宙の活動に対する、完全に新しい考え方が生まれたことを明らかにする。
ウルフラムは、科学における一連の基本問題── 熱力学の第二法則、生物の複雑な発達、数学の計算上の限界、物理学の基本的な理論の可能性、自由意志論と決定論の相互作用など── をひとつひとつ取り上げるというアプローチをとっている。
すばらしく明晰な語り口。1000枚を超えるオリジナルの図版。この独創的な本は、科学者も一般の読者も一様に、将来の重要な知的革命に参加することを可能にしてくれる。(Book Description)
著者について
Stephen Wolfram was born in London and educated at Eton, Oxford and Caltech. He received his PhD in theoretical physics in 1979 at the age of 20, and in the early 1980s made a series of discoveries which launched the field of complex systems research. Starting in 1986 he created Mathematica, the primary software system now used for technical computing worldwide, and the tool which made A New Kind of Science possible. Wolfram is the founder and CEO of Wolfram Research, Inc.---the world's leading technical software company.
登録情報
- 出版社 : Wolfram Media Inc; Illustrated版 (2002/5/14)
- 発売日 : 2002/5/14
- 言語 : 英語
- ハードカバー : 1197ページ
- ISBN-10 : 1579550088
- ISBN-13 : 978-1579550080
- 寸法 : 20.96 x 6.35 x 24.77 cm
- Amazon 売れ筋ランキング: - 86,070位洋書 (洋書の売れ筋ランキングを見る)
- カスタマーレビュー:
著者について
著者をフォローして、新作のアップデートや改善されたおすすめを入手してください。
著者の本をもっと発見したり、よく似た著者を見つけたり、著者のブログを読んだりしましょう
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トップレビュー
上位レビュー、対象国: 日本
レビューのフィルタリング中に問題が発生しました。後でもう一度試してください。
2012年12月5日に日本でレビュー済み
Amazonで購入
Wolfram氏の、セルオートマトンに関する研究結果が詰め込まれた一冊。ページ数はとても多く重量級な本に仕上がっている。ゆえに未だ読了に至らず。しかし、これを読めばセルオートマトンがどのようなものなのか、今までにどのような発展を遂げて来たのか等々を学ぶことが出来ると思われる。セルオートマトンの概要だけをもっと手軽に知りたいと思う人は、Joel L. Schiff著の持ち運びやすく日本語訳された本が売られているのでそちらを勧める。
2002年5月16日に日本でレビュー済み
Amazonで購入
Wolframが10年以上の年月をかけた本がとうとう出た。私は何年も前に本書のドラフトの一部を見たことがあるが、それ以降、なかなか完成しなかったのを知っているので、刊行が待ち遠しかった。
Wolframは、今ではMathematicaの開発者として知られているが、高校生の時から素粒子論の論文を書き、セル・オートマトンの分類を成し遂げたりなど、早くから天才として著名である。彼は、セル・オートマトンが単純な規則から複雑な構造を作り出して行く(進化に興味のある人はDawkinsのBlind Wathcmakerを思い出すだろう)の惹かれて、宇宙を作っている原理は規則と情報であるという独特の世界観にたどり着いたようだ。もちろん本書のタイトルがNew Kind...であることから分かるように正統的な考え方からははずれている。おそらくこれから、いろいろな分野の人たちによって議論されるだろう。
Wolframは、今ではMathematicaの開発者として知られているが、高校生の時から素粒子論の論文を書き、セル・オートマトンの分類を成し遂げたりなど、早くから天才として著名である。彼は、セル・オートマトンが単純な規則から複雑な構造を作り出して行く(進化に興味のある人はDawkinsのBlind Wathcmakerを思い出すだろう)の惹かれて、宇宙を作っている原理は規則と情報であるという独特の世界観にたどり着いたようだ。もちろん本書のタイトルがNew Kind...であることから分かるように正統的な考え方からははずれている。おそらくこれから、いろいろな分野の人たちによって議論されるだろう。
2003年3月3日に日本でレビュー済み
Amazonで購入
あらゆる現象をデジタル計算で解明すると言ふ構想が、多數の興味深い圖と共に、説明してあり、面白い。ただ、インド數學のゼロ(zero)とプールナ(purna)の概念が、全く無いといふ點は、傳統の西洋數學の範圍から、一歩たりとも出てゐないことの證明である。禪宗の「無とは何か」といふ問題を持つ日本の文明は、重要な使命があると思ふ。この本を讀むことで、インド文明と日本文明の存在の意味が、再確認される。是非、多くの日本人に讀んで欲しい。
他の国からのトップレビュー
José Huerta Ibarra
5つ星のうち5.0
Ampliación del conocimiento científico
2024年3月17日にメキシコでレビュー済みAmazonで購入
Wolfram es uno de los genios modernos que amplía el conocimiento de manera extraordinaria. En este libro abre el mundo matemático con la metamatemática que actualmente se encuentra en desarrollo pues dota a los científicos de las estructuras y el lenguaje necesario para entender la organización formal del conocimiento. Libro que inicia el desarrollo moderno del lenguaje de cómputo y la visión integral del aspecto formal de cualquier conocimiento. Indispensable para cualquier científico que desee investigar a fondo los misterios del conocimiento de su disciplina.
Amazon Customer
5つ星のうち5.0
Excellent book
2024年3月26日にインドでレビュー済みAmazonで購入
Simplicity,Beyond a level, can generate amazing complexity. Must read one
Randolph Eck
5つ星のうち5.0
Very interesting, if you can get through it all
2020年12月6日にアメリカ合衆国でレビュー済みAmazonで購入
There are a vast range of physical, biological, and other systems that we are confronted with that exhibit an immense complexity. Throughout history, it has been seen as perhaps the work of a supernatural being. Also, often when we encounter something that seems complex, we take it for granted that the phenomenon must be the result of an underlying mechanism that is itself complex. The author shows us how simple programs can, in fact, produce great complexity. In regards to physics, the author states, “that thinking in terms of simple programs will make it possible to construct a single truly fundamental theory of physics, from which space, time, quantum mechanics and all the other known feature of our universe will emerge.” We learn in this book, through virtually hundreds of pages of diagrams, how simple initial conditions can produce complex behavior. Wolfram argues that this phenomenon is responsible from most of the complexity we see in nature.
There is extensive discussion of cellular automata, where we have simple rules that result in complex behavior- so much in fact that I skipped through some of the material. It is believed that these behaviors are common in nature. An interesting finding is that “adding more complexity to the underlying rules does not yield behavior that it ultimately any more complex.” By chapter four, we are introduced to systems based on numbers. How does their behavior compare with other systems discussed? Even here it is possible to get behavior of considerable complexity by applying a series of operations based on simple arithmetic. Chapter 5 expands things to two dimension and beyond. The next chapter considers completely random initial conditions.
Finally, we get to the application of what was covered in the previous chapters – the study of actual phenomena in nature. There seems to be a similarity between the basic mechanisms in nature and those responsible for the phenomena seen in simple programs. It is important to note that even though the models are based on simple programs that may successfully reproduce the behavior of some system even though that system does not actually consist of discrete cells, it is just a model. Models provide “an abstract representation of effects that are important in determining the behavior of a system.” The author provides examples of snowflake formation and fluidic motion. Regarding evolution, it seems that natural selection may not be as important as once thought saying that features in complexity of biological organisms arise in a sense not because of natural selection but despite it. Also, it was noted that the high degree of complexity seen in nature “can arise in a sense quite effortlessly just as a consequence of following certain simple rules of growth.”
In the chapter on physics, this idea of simple initial conditions producing complex and seemingly random behavior is applied to the Second Law of Thermodynamics. The author discusses models for continuum fluid mechanics and even the universe. In fact, he believes that most physical laws discovered so far “are not truly fundamental but are emergent features of the large-scale behavior of some ultimate underlying rules.” The discussion veers into topics like gravity, spacetime, and quantum phenomena. It is the author’s “strong belief that in the end it will turn out that every detail of our universe does indeed follow rules that can be represented by a very simple program – and that everything we see will ultimately emerge just from running this program.” Wow, the thought went through my head: could we be living in a simulation? Who knows?
The next chapter deals with process of perception and analysis. I skimmed through parts of this section – a bit too much detail for me. But the question becomes could simple mechanisms underlie human thinking? The next chapter covers universality and how “in the end cellular automata can actually be made to emulate almost every single type of system” that has been discussed so far. In fact, a wide range of computations that can be performed by computers can be done by cellular automata.
In the final chapter, the author discusses something called the Principle of Computational Equivalence, where one can view all processes whether produced by humans or nature as computations. Another concept discussed is computational irreducibility, which implies that even if one has all the information to work out how some system will behave, it will still take an irreducible amount of computational work to do this. The concepts developed here apply not only to phenomena in nature but can provide new insights on fundamental issues in mathematics as well. Note that in discussing his work, the author supplies literally hundreds of charts, diagrams, and tables to back up his claims. The author concludes the main purpose of the book has been to build a new kind of basic science, but the most important consequence will have been to introduce a vast new range of systems helpful to technology.
There is extensive discussion of cellular automata, where we have simple rules that result in complex behavior- so much in fact that I skipped through some of the material. It is believed that these behaviors are common in nature. An interesting finding is that “adding more complexity to the underlying rules does not yield behavior that it ultimately any more complex.” By chapter four, we are introduced to systems based on numbers. How does their behavior compare with other systems discussed? Even here it is possible to get behavior of considerable complexity by applying a series of operations based on simple arithmetic. Chapter 5 expands things to two dimension and beyond. The next chapter considers completely random initial conditions.
Finally, we get to the application of what was covered in the previous chapters – the study of actual phenomena in nature. There seems to be a similarity between the basic mechanisms in nature and those responsible for the phenomena seen in simple programs. It is important to note that even though the models are based on simple programs that may successfully reproduce the behavior of some system even though that system does not actually consist of discrete cells, it is just a model. Models provide “an abstract representation of effects that are important in determining the behavior of a system.” The author provides examples of snowflake formation and fluidic motion. Regarding evolution, it seems that natural selection may not be as important as once thought saying that features in complexity of biological organisms arise in a sense not because of natural selection but despite it. Also, it was noted that the high degree of complexity seen in nature “can arise in a sense quite effortlessly just as a consequence of following certain simple rules of growth.”
In the chapter on physics, this idea of simple initial conditions producing complex and seemingly random behavior is applied to the Second Law of Thermodynamics. The author discusses models for continuum fluid mechanics and even the universe. In fact, he believes that most physical laws discovered so far “are not truly fundamental but are emergent features of the large-scale behavior of some ultimate underlying rules.” The discussion veers into topics like gravity, spacetime, and quantum phenomena. It is the author’s “strong belief that in the end it will turn out that every detail of our universe does indeed follow rules that can be represented by a very simple program – and that everything we see will ultimately emerge just from running this program.” Wow, the thought went through my head: could we be living in a simulation? Who knows?
The next chapter deals with process of perception and analysis. I skimmed through parts of this section – a bit too much detail for me. But the question becomes could simple mechanisms underlie human thinking? The next chapter covers universality and how “in the end cellular automata can actually be made to emulate almost every single type of system” that has been discussed so far. In fact, a wide range of computations that can be performed by computers can be done by cellular automata.
In the final chapter, the author discusses something called the Principle of Computational Equivalence, where one can view all processes whether produced by humans or nature as computations. Another concept discussed is computational irreducibility, which implies that even if one has all the information to work out how some system will behave, it will still take an irreducible amount of computational work to do this. The concepts developed here apply not only to phenomena in nature but can provide new insights on fundamental issues in mathematics as well. Note that in discussing his work, the author supplies literally hundreds of charts, diagrams, and tables to back up his claims. The author concludes the main purpose of the book has been to build a new kind of basic science, but the most important consequence will have been to introduce a vast new range of systems helpful to technology.
un autre fred
5つ星のうち5.0
Hyper confortable
2021年7月13日にフランスでレビュー済みAmazonで購入
Très bonne facture. Un peu surpris au début par la couleur - je l’ai prise en gris - dont les variations vont du gris très clair 20% au gris 60% mais finalement séduit car hyper confortable, stable et résistant bien à la chaleur directe.
Rudolf P.
5つ星のうち5.0
interessant, aber wenig bescheidener Autor
2020年7月16日にドイツでレビュー済みAmazonで購入
Thematisch ist das Buch durchaus interessant.
Die Ich-Bezogenheit des Autors und des Schreibstils muss man dabei halt ein wenig ausblenden.
Die Ich-Bezogenheit des Autors und des Schreibstils muss man dabei halt ein wenig ausblenden.