LEADER 04911nam 2200625Ia 450 001 9910813532403321 005 20240416151027.0 010 $a0-674-04406-1 024 7 $a10.4159/9780674044067 035 $a(CKB)1000000000786906 035 $a(StDuBDS)AH23050914 035 $a(SSID)ssj0000111428 035 $a(PQKBManifestationID)11133943 035 $a(PQKBTitleCode)TC0000111428 035 $a(PQKBWorkID)10074801 035 $a(PQKB)10155034 035 $a(Au-PeEL)EBL3300376 035 $a(CaPaEBR)ebr10318366 035 $a(OCoLC)923110955 035 $a(DE-B1597)574586 035 $a(DE-B1597)9780674044067 035 $a(MiAaPQ)EBC3300376 035 $a(OCoLC)1248759270 035 $a(EXLCZ)991000000000786906 100 $a20021107d2003 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBigger than chaos $eunderstanding complexity through probability /$fMichael Strevens 205 $a1st ed. 210 $aCambridge, MA $cHarvard University Press$d2003 215 $a1 online resource (xii, 413 p. ) $cill 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-674-01042-6 311 $a0-674-02259-9 320 $aIncludes bibliographical references (p. 397-401) and index. 327 $aNote to the Reader 1. The Simple Behavior of Complex Systems 1.1 Simplicity in Complex Systems 1.2 Enion Probability Analysis 1.3 Towards an Understanding of Enion Probabilities 2. The Physics of Complex Probability 2.1 Complex Probability Quantified 2.2 Microconstant Probability 2.3 The Interpretation of IC-Variable Distributions 2.4 Probabilistic Networks 2.5 Standard IC-Variables 2.6 Complex Probability and Probabilistic Laws 2.7 Effective and Critical IC-Values 2.A The Method of Arbitrary Functions 2.B More on the Tossed Coin 2.C Proofs 3. The Independence of Complex Probabilities 3.1 Stochastic Independence and Selection Rules 3.2 Probabilities of Composite Events 3.3 Causal Independence 3.4 Microconstancy and Independence 3.5 The Probabilistic Patterns Explained 3.6 Causally Coupled Experiments 3.7 Chains of Linked IC-Values 3.A Conditional Probability 3.B Proofs 4. The Simple Behavior of Complex Systems Explained 4.1 Representing Complex Systems 4.2 Enion Probabilities and Their Experiments 4.3 The Structure of Microdynamics 4.4 Microconstancy and Independence of Enion Probabilities 4.5 Independence of Microdynamic Probabilities 4.6 Aggregation of Enion Probabilities 4.7 Grand Conditions for Simple Macrolevel Behavior 4.8 Statistical Physics 4.9 Population Ecology 5. Implications for the Philosophy of the Higher-Level Sciences 5.1 Reduction 5.2 Higher-Level Laws 5.3 Causal Relevance 5.4 The Social Sciences 5.5 The Mathematics of Complex Systems 5.6 Are There Simple Probabilities? Notes Glossary References Index 330 $aMichael 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. 330 $bMany 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. 606 $aProbabilities 606 $aStatistical physics 615 0$aProbabilities. 615 0$aStatistical physics. 676 $a003 686 $aCC 3700$2rvk 700 $aStrevens$b Michael$01145008 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910813532403321 996 $aBigger than chaos$93970240 997 $aUNINA