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An approach to compatibility analysis of systems of discrete relations is proposed. Unlike the Groebner basis technique, the proposed scheme is not based on the polynomial ring structure. It uses more primitive set-theoretic and topological concepts and constructions. We illustrate the approach by application to some two-state cellular automata. In the two-state case the Groebner basis method is also applicable, and we compare both approaches.
The cellular automata with local permutation invariance are considered. We show that in the two-state case the set of such automata coincides with the generalized Game of Life family. We count the number of equivalence classes of the rules under consideration with respect to permutations of states. This reduced number of rules can be efficiently generated in many practical cases by our C program. Since a cellular automaton is a combination of a local rule and a lattice, we consider also maximally symmetric two-dimensional lattices. In addition, we present the results of compatibility analysis of several rules from the Life family.
The discrete Schrödinger equation with potential belonging to $\F_{2}$ is solved explicitly. On this base the associated (1+1)-dimensional cellular automaton is examined and corresponding set of integrals of motions is constructed.
A cellular automata model that describes as limit cases of his parameters the spread of contagious diseases modeled by systems of ordinary or partial differential equations is developed. Periodic features of the behavior of human settlement are considered. The model is built taking into account the range of motion of the elements of population. For small (large) values of this range, the behaviors described by partial (ordinary) differential equation models are reproduced. Emphasis is done in the study of those scenarios in which the above mentioned equations fail to describe. Some interesting results in these cases are reported.
We study the emergence of the collective spatio-temporal macroscopic properties of the immune system, by representing individually the elementary interactions between its microscopic components (antibodies, antigens, cytokines). The results of this detailed explicit analysis are compared with the traditional procedure of averaging over individuals and representing the collective dynamics in terms of densities that obey partial differential equations (PDE). The simulations show even for very simple elementary reactions the spontaneous emergence of localized complex structures. In turn the effective dynamics of these structures affects the average behavior of the system in a very decisive way: systems which would according to the differential equations approximation die, display in reality a very lively behavior. Our conclusions are supported both by explicit microscopic simulations and by analytic calculations. As the optimal method we propose a mixture of microscopic simulation (MS) systems describing each reaction separately, and average methods describing the average behavior of the agents.
We compare several definitions for number-conserving cellular automata that we prove to be equivalent. A necessary and sufficient condition for \cas to be number-conserving is proved. Using this condition, we give a linear-time algorithm to decide number-conservation. The dynamical behavior of number-conserving \cas is studied and a classification that focuses on chaoticity is given.
We study the transitions to spatio-temporal intermittency in networks of randomly coupled Chate-Manneville maps. The relevant paprameters are the network connectivity, coupling strength, and the local parameter of the map. We show that the spatio-temporal intermittency occurs for some intervals or windows of the values of these parameters. Within the intermittency windows, the system exhibits periodic and other nontrivial collective behaviors. The detailed behavior depends crucially upon the topology of the random graph spanning the network. We present a detailed analysis of the results based on the thermodynamic formalism and random graph theory.
The connection between the evolution of an arbitrary configuration and the evolution of its parts in the first generation is established. The equivalence of Conway's evolution rules to the elementary configurations' (containing one, two, three, and four pieces) evolution laws in the first generation has been proved.
We analyze the steady-state flow as a function of the initial density for a class of deterministic cellular automata rules (``traffic rules'') with periodic boundary conditions [H. Fuks and N. Boccara, Int. J. Mod. Phys. C 9, 1 (1998)]. We are able to predict from simple considerations the observed, unexpected cutoff of the average flow at unity. We also present an efficient algorithm for determining the exact final flow from a given finite initial state. We analyze the behavior of this algorithm in the infinite limit to obtain for R_m,k an exact polynomial equation maximally of 2(m+k)th degree in the flow and density.
In recent years, political economists, financial economists, and other social scientists have introduced spin and lattice models into their theoretical tool kit. To this end, the modeling skills of hard scientists may be of assistance. This lecture introduces examples of how these models are used. A simple dynamical model of how legal rules evolve and propagate in the courts is described.
We introduce a simplified technique for incorporating diffusive phenomena into lattice-gas molecular dynamics models. In this method, spatial interactions take place one dimension at a time, with a separate fractional timestep devoted to each dimension, and with all dimensions treated identically. We show that the model resulting from this technique is equivalent to the macroscopic diffusion equation in the appropriate limit. This technique saves computational resources and reduces the complexity of model design, programming, debugging, simulation and analysis. For example, a reaction-diffusion simulation can be designed and tested as a one-dimensional system, and then directly extended to two or more dimensions. We illustrate the use of this approach in constructing a microscopically reversible model of diffusion limited aggregation as well as in a model of growth of biological films.
We study forecasting capabilities of the methods of Singular Spectrum Analysis (SSA) and Local Approximation (LA). A practical implementation of these methods to several time series is described. Details of the algorithms of these methods are discussed. Advantages and disadvantages of SSA and LA are given. On the basis of our results we generalize LA and SSA and propose a new way for time series forecasting including strongly noisy signals. This allowed us to extend the range of application of LA in comparison with the known standard scheme. For the problem of forecasting, the accuracy enhancement of the numerical computations is discussed.
Traditional approaches to combination tones based on Helmholtz theory encounter essential interpreting difficulties, which the most known example is the anomalous behaviour of the combination tone 2f1-f2. Without doubt the phenomenon of combination tones is connected with nonlinear phenomena in the human ear. Therefore, it is generally assumed that the explanation of this phenomenon is possible on the level of the ear. This paper proposes different approach, which assumes the existence of an additional mechanism beyond the ear. The aim of this mechanism is to diminish sensitivity of the auditory process to nonlinear distortions by appropriate decoding of sound information. The general rule of construction and the mathematical model of such a mechanism is proposed, which allows for the explanation of many problems associated with combination tones. As a consequence an unexpected result is obtained with regard to a combination tone 2f1-f2 that it can originate already from the nonlinear distortion of the second order. This mathematical model is formulated in terms of finite groups.
The work relates to a new way for analysis of one-dimensional stochastic systems, based on consideration of its higher order difference structure. From this point of view, the deterministic and random processes are analyzed. A new numerical characteristic for one-dimensional stochastic systems is introduced. The applications to single neuron models and neural networks are given.
In addition to the $λ$ parameter, we have found another parameter which characterize the class III, class II and class IV patterns more quantitatively. It explains why the different classes of patterns coexist at the same $λ$. With this parameter, the phase diagram for an one dimensional cellular automata is obtained. Our result explains why the edge of chaos(class IV) is scattered rather wide range in $λ$ around 0.5, and presents an effective way to control the pattern classes. \noindent PACS: 89.75.-k Complex Systems
Highly nonlinear behavior of a system of discrete sites on a lattice is observed when a specific feedback loop is introduced into models employing coupled map lattices, quantum cellular automata, or the real-valued analogues of the latter. It is shown that the combination of two operations, i.e. i) enhancement of a site's value when fulfilling a feedback condition and ii) normalization of the system after each time step, produces relatively short-lived spatio-temporal patterns whose mean lifetime can be considered as emergent order parameter of the system. This mean lifetime obeys a scaling law involving a control parameter which tunes the "fault tolerance" of the feedback condition. Thus, within appropriate ranges of the systems variables, the dynamical properties can be characterized by a "fractal evolution dimension" (as opposed to a "fractal dimension").
This paper reviews a class of generic dissipative dynamical systems called N-K models. In these models, the dynamics of N elements, defined as Boolean variables, develop step by step, clocked by a discrete time variable. Each of the N Boolean elements at a given time is given a value which depends upon K elements in the previous time step. We review the work of many authors on the behavior of the models, looking particularly at the structure and lengths of their cycles, the sizes of their basins of attraction, and the flow of information through the systems. In the limit of infinite N, there is a phase transition between a chaotic and an ordered phase, with a critical phase in between. We argue that the behavior of this system depends significantly on the topology of the network connections. If the elements are placed upon a lattice with dimension d, the system shows correlations related to the standard percolation or directed percolation phase transition on such a lattice. On the other hand, a very different behavior is seen in the Kauffman net in which all spins are equally likely to be coupled to a given spin. In this situation, coupling loops are mostly suppressed, and the behavior of the system is much more like that of a mean field theory. We also describe possible applications of the models to, for example, genetic networks, cell differentiation, evolution, democracy in social systems and neural networks.
Book Review for: "A New Kind of Science", by Stephen Wolfram (Wolfram Media, Inc. Champaign IL 2002).