Asymmetry: The Foundation of Information

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Springer Science & Business Media, May 11, 2007 - Computers - 165 pages
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As individual needs have arisen in the fields of physics, electrical engineering and computational science, each has created its own theories of information to serve as conceptual instruments for advancing developments. This book provides a coherent consolidation of information theories from these different fields. The author gives a survey of current theories and then introduces the underlying notion of symmetry, showing how information is related to the capacity of a system to distinguish itself. A formal methodology using group theory is employed and leads to the application of Burnside's Lemma to count distinguishable states. This provides a versatile tool for quantifying complexity and information capacity in any physical system. Written in an informal style, the book is accessible to all researchers in the fields of physics, chemistry, biology, computational science as well as many others.

 

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Contents

Introduction
1
Information
5
22 A Survey of Information Theories
6
221 Thermodynamic Information Theory
7
222 Information Communication Theory
32
223 Algorithmic Information Theory
34
224 Signpost
54
23 Probability
56
44 Information and Probability
100
45 Information and Statistical Mechanics
112
452 Demonic Information
116
46 Information and Physical Thermodynamics
118
462 Symmetry and the Third Law
120
463 Information and The Gibbs Paradox
122
47 Quantum Information
124
471 Quantum Information and Distinguishability
125

231 Subjective Probability
57
233 Dispositional Probability
63
24 Signpost
65
Information and Distinguishability
67
A Foundational Approach
76
Information and Symmetry
79
42 Symmetry and Group Theory
81
421 Subgroups and Special Groups
87
422 Group Theory and Information
89
43 Symmetry and Information
96
431 Information Generation
97
432 Extrinsic and Intrinsic Information
99
48 Symmetries and Algorithmic Information Theory
132
483 Groups and Algorithmic Information Theory
133
484 Symmetry and Randomness
137
485 A Final Signpost
141
Conclusion
143
Burnsides Lemma
147
Worked Examples
149
B13 Case 3
152
B2 Binary String
153
References
155
Index
161
Copyright

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About the author (2007)

Scott Muller graduated from the University of Queensland in Chemical Engineering specialising in biotechnology. He worked in Australia and Italy in the biotechnology and pharmaceutical industries. In 2004 he received his doctorate from the University of Newcastle (Australia) where he studied the foundations of information and conducted research into the nature of "emergence". Recently he has worked on automated reasoning and expert systems in the telecommunications industry. Scott is currently developing industrial, adaptive decision-making systems using evolutionary programming techniques.