TY  - BOOK
AU  - Strevens,Michael
TI  - Bigger than chaos: understanding complexity through probability 
SN  - 9780674044067
AV  - QC174.85.P76 S77 2003eb
U1  - 003 22
PY  - 2003///]
CY  - Cambridge, Massachusetts
PB  - Harvard University Press
KW  - Probabilities
KW  - SCIENCE
KW  - System Theory
KW  - bisacsh
KW  - TECHNOLOGY & ENGINEERING
KW  - Operations Research
KW  - Chaotic Behavior in Systems
KW  - fast
KW  - Complexe structuren
KW  - gtt
KW  - Waarschijnlijkheid (statistiek)
KW  - Electronic books
N1  - Includes bibliographical references (pages 397-401) and index
N2  - Michael Strevens shows how simplicity can co-exist with the tangled interconnections within complex systems. By looking at the foundations of statistical reasoning about complex systems (gases, ecosystems and even social systems) he provides an understanding of how simplicity emerges from complexity; Many complex systems--from immensely complicated ecosystems to minute assemblages of molecules--surprise us with their simple behavior. Consider, for instance, the snowflake, in which a great number of water molecules arrange themselves in patterns with six-way symmetry. How is it that molecules moving seemingly at random become organized according to the simple, six-fold rule? How do the comings, goings, meetings, and eatings of individual animals add up to the simple dynamics of ecosystem populations? More generally, how does complex and seemingly capricious microbehavior generate stable, predictable macrobehavior? In this book, Michael Strevens aims to explain how simplicity can coexist with, indeed be caused by, the tangled interconnections between a complex system's many parts. At the center of Strevens's explanation is the notion of probability and, more particularly, probabilistic independence. By examining the foundations of statistical reasoning about complex systems such as gases, ecosystems, and certain social systems, Strevens provides an understanding of how simplicity emerges from complexity. Along the way, he draws lessons concerning the low-level explanation of high-level phenomena and the basis for introducing probabilistic concepts into physical theory
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ER  -