![]() ![]() Optimization and learning techniques, like the genetic algorithm and adaptive stochastic cellular automata are applied to find cellular automaton rules that model such physical phenomena as crystal growth or perform such adaptive-learning tasks as balancing an inverted pole. Studies of the inverse problem take up the organization and structure of the set of automata, in particular the parameterization of the space of cellular automata. A long-term goal is to develop a set of techniques that can find a rule or set of rules that can reproduce quantitative observations of a physical system. The inverse problem, an area of study gaining prominence particularly in the natural sciences, involves designing rules that possess specified properties or perform specified task. ![]() The role of cellular automata in computation theory is seen as a particularly exciting venue for exploring parallel computers as theoretical and practical tools in mathematical physics. Properties considered include reversibility, invariants, criticality, fractal dimension, and computational power. The forward problem concerns the description of properties of given cellular automata. Current work on cellular automata may be viewed as revolving around two central and closely related problems: the forward problem and the inverse problem. Chapters cover mathematical analysis, the structure of the space of cellular automata, learning rules with specified properties: cellular automata in biology, physics, chemistry, and computation theory and generalizations of cellular automata in neural nets, Boolean nets, and coupled map lattices. The thirty four contributions in this book cover many aspects of contemporary studies on cellular automata and include reviews, research reports, and guides to recent literature and available software. According to the cellular automata simulation for wealth distribution, we will have a deep insight of financial policy of the government.Ĭellular automata, dynamic systems in which space and time are discrete, are yielding interesting applications in both the physical and natural sciences. The effects of social welfare, tax, economical investment and inheritance are considered and simulated. In this study, a more complicate cellular automata model for wealth distribution model is proposed. There are still other variables should be considered while an artificial society is established. The model considers the income, age, working opportunity and salary as control variables. A model, which is based on the Epstein & Axtell's "Sugars cape" model, is presented in Netlogo. Wealth distribution of a country is a complicate system. Cellular Automata Simulation for Wealth Distribution
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