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SYMMETRY and the END OF PROBABILITY
Foreword
Summary
Excerpts
THE MIDDLE-WAY APPROACH TO SCIENCE
1. Logic for the End of Probability
2. The Space-Time Foundation of Quantum Physics
3. The Resolution Limits of Space and Time
4. Heisenberg's Uncertainty Principle
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Book Summary
SYMMETRY and the END OF PROBABILITY
by DangSon
January, 2003
All rights reserved
CHAPTER 1: (15 pages)
A CRITICAL REVIEW OF THE PROBABILITY THEORY
Probability as a fact of modern life - The failure of classical physics in coin tossing processes Probability conventions and Kolmogorovs axioms The limited value of subjective probability The indifference principle and classical probability Classical probability and the equal opportunity principle The long run paradox of classical probability The incompleteness of frequency probability The incompleteness of the propensity interpretation of probability Strengths and weaknesses of major probability interpretations - Special note on subjective probability and the long run paradox - The confusing state of the probability theory today.
CHAPTER 2: (29 pages)
THE PARADOXES OF PROBABILITY
Distributive and deterministic processes Distributive entity Binomial distribution Normal distribution Normal approximation of binomial distribution - The ignorance argument against probability On the validity of the concept of probability Apparent empirical support for probability The long run paradox and the failure of frequency probability The long run paradox and the inconsistency of classical probability The infinite randomness assumption and its failure in man made computing systems The physically impossible rationale and the reason why it contradicts the probability theory The law of large number and the reason why it is in conflict with the probability theory The Gibbs paradox as another failure of probability.
CHAPTER 3: (21 pages)
SYMMETRY AND THE END OF PROBABILITY
The replacement of probability by propensity Sample size and trial size Normal approximation of binomial distribution (review) - The mystery of the average criterion Why the infinite randomness assumption must be wrong Distributive symmetry and distributive conservation Symmetry vs. probability The symmetry justification for the law of large number The equal opportunity principle as the compromise between symmetry and randomness Maximum symmetry and correction to the law of large number Distributive symmetry and system memory Symmetry solution to the long run paradox Symmetry solution to the Gibbs paradox The end of probability.
CHAPTER 4: (14 pages)
SYMMETRY AND THE LAW OF LARGE NUMBER
The symmetry approach to distributive processes Binomial average Symmetry logic for the law of average Symmetry vs. probability in long term distributions The law of average as the new law of large number Symmetry and the physically impossible rationale The pseudo science status of probability The replacement of probability theory by distribution theory.
CHAPTER 5: (23 pages)
THE FOUNDATION OF DISTRIBUTION THEORY I
Randomness level and interdependency of events
The reality of finite randomness New meaning of randomness and the measure of randomness level The quasi continuous nature of distributive processes The randomness level k The constant k factor in ideal processes The k factor in non ideal processes Experimental k factor results in support of the distribution theory Process resolution, trial size, and the convergence axiom of frequency probability Another symmetry argument against classical probability The interdependency of individual events in distributive processes Interdependency and the meaning of time in distributive processes The symmetry logic of prohibition threshold Prohibited propensities and system capacity More experimental verification of prohibited propensities Extreme k factor and determinism Convergence limit and convergence theorem Convergence failure and convergence size.
CHAPTER 6: (22 pages)
THE FOUNDATION OF DISTRIBUTION THEORY II
The binomial nature of the Central Limit Theorem
The meaning of variance and sigma in arbitrary distributions The central limit theorem The binomial nature of many samples (new proof of the central limit theorem) - The central limit theorem as the convergence theorem for distributive processes Sample size and trial size requirements for the central limit theorem The generalized binomial theorem and the central limit theorem Successive CLT processes Binomial sigma for small sample size The probability mistake of expected deviation.
CHAPTER 7: (20 pages)
THE FOUNDATION OF DISTRIBUTION THEORY III
Distributive period and the Time-Indifference principle
The k factor and the end of probability The return of probability in the last science Effects of k factor and sigma on system performance - The addition of the k factor as a system parameter Distributive period The necessary existence of distributive periodicity The meaning of distributive period Resolution period for the CLT averaging process Unit distribution and mixing length The role of time in distributive processes The near perfect convergence of distributions Simultaneous vs. sequential events Distributive determinism The time indifference principle Limits of the time indifference principle The return of determinism.
Additional Reading for Chapter 7 (12 pages)
Symmetry, Synchronicity, and the meaning of space-time
Inapplicability of the space time model in distributive processes The success of symmetry and the end of probability Symmetry, propensity, and synchronicity - The event dimension in distributive processes Distributive process as the space time realization of propensity Distribution theory: The successful synthesis of determinism and randomness The amazing time indifference principle The time indifference principle as another evidence against probability A final note on space and time.
CHAPTER 8: (23 pages)
THE CENTRAL LIMIT THEOREM AND THE FUTURE OF SCIENCE
The partial inequality of individual events Systems and the central limit theorem System determinism and individual freedom The need for a new approach to science Random and deterministic scales Differentiating chaotic and quantum processes Discontinuity as criterion for quantum processes The role of resolution limit in quantum processes The power of the central limit theorem The central limit theorem as reason for normal distributions The meaning of individual events and individual decisions The role of the central limit theorem in quantum physics The law of average and the future of science.
SPECIAL SECTION (25 pages)
GAMBLER'S WISDOM BEYOND PROBABILITY
To live is to gamble - Why and how the probability theory is wrong - The modified view of probability The basics of event probability Why the so-called gamblers fallacy is not a fallacy and why it still may not increase your chance of winning The logical necessity of luck Luck as a balance between randomness and symmetry The reason why casinos dont need luck but gamblers do The logic of winning and losing in the games of chance The law of average and why beginners luck could be a very dangerous thing - The law of decreasing value and why gamblers tend to increase their bets with time The law of vanishing value and why gamblers tend to overstretch their luck The law of symmetry and why most winning gamblers will eventually lose The law of increasing randomness and why almost everyone will lose to the system Answering the question Am I fit to gamble? Deprogramming the 5 losing laws of gambling Choosing and learning a system that fits your personality Combining short sessions with fixed winning and losing limits - Knowing a little more about Lady Luck.
Thinh Tran's Home
SYMMETRY and THE END OF PROBABILITY
—Foreword
—Summary
—Excerpts
THE MIDDLE-WAY APPROACH TO SCIENCE
—Logic for the End of Probability
—The Space-Time Foundation of Quantum Physics
—The Resolution Limits of Space and Time
—Resolution Limit Interpretation of the Heisenberg's Uncertainty Principle
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