Adaptation Noise And Self Organizing Systems

We present a general conceptual framework for self-organized criticality (SOC), based on the recognition that it is nothing but the expression, ''unfolded'' in a suitable parameter space, of an underlying {\em unstable} dynamical critical point. More precisely, SOC is shown to result from the tuning of the {\em order parameter} to a vanishingly small, but {\em positive} value, thus ensuring that the corresponding control parameter lies exactly at its critical value for the underlying transition. This clarifies the role and nature of the {\em very slow driving rate} common to all systems exhibiting SOC. This mechanism is shown to apply to models of sandpiles, earthquakes, depinning, fractal growth and forest-fires, which have been proposed as examples of SOC.

Markets have internal dynamics leading to excess volatility and other phenomena that are difficult to explain using rational expectations models. This paper studies these using a nonequilibrium price formation rule, developed in the context of trading with market orders. Because this is so much simpler than a standard inter-temporal equilibrium model, it is possible to study multi-period markets analytically. There price dynamics have second order oscillatory terms. Value investing does not necessarily cause prices to track values. Trend following causes short term trends in prices, but also causes longer-term oscillations. When value investing and trend following are combined, even though there is little linear structure, there can be boom-bust cycles,excess and temporally correlated volatility, and fat tails in price fluctuations. The long term evolution of markets can be studied in terms of flows of money. Profits can be decomposed in terms of aggregate pairwise correlations. Under reinvestment of profits this leads to a capital allocation model that is equivalent to a standard model in population biology. An investigation of market efficiency shows that patterns created by trend followers are more resistant to efficiency than those created by value investors, and that profit maximizing behavior slows the progression to efficiency. Order of magnitude estimates suggest that the timescale for efficiency is years to decades.

Introducing the effect of extinction into the so-called replicator equations in mathematical biology, we construct a general model of ecosystems. The present model shows mass extinction by its own extinction dynamics when the system initially has a large number of species ( diversity). The extinction dynamics shows several significant features such as a power law in basin size distribution, induction time, etc. The present theory can be a mathematical foundation of the species-area effect in the paleontologic theory for mass extinction.

We present a new model for extinction in which species evolve in bursts or `avalanches', during which they become on average more susceptible to environmental stresses such as harsh climates and so are more easily rendered extinct. Results of simulations and analytic calculations using our model show a power-law distribution of extinction sizes which is in reasonable agreement with fossil data. e also see a number of features qualitatively similar to those seen in the fossil record. For example, we see frequent smaller extinctions in the wake of a large mass extinction, which arise because there is reduced competition for resources in the aftermath of a large extinction event, so that species which would not normally be able to compete can get a foothold, but only until the next cold winter or bad attack of the flu comes along to wipe them out.

In the present monograph we formulate a simple model for heat transfer in living tissue with self - regulation. The initial point of the model is the governing equations describing heat transfer in living tissue at the mesoscopic level, i.e. considering different vessels individually. Then, basing on the well known equivalence of the diffusion type process and random walks, we develop a certain regular procedure that enables us to average these mesoscopic equations practically over all scales of the hierarchical vascular network. The microscopic governing equations obtained in this way describe living tissue in terms of an active medium with continuously distributed self - regulation. One of the interesting results obtained in the present monograph is that there can be the phenomena of ideal self - regulation in large active hierarchical systems. Large hierarchical systems are characterized by such a great information flow that none of its elements can possess whole information required of governing the system behavior. Nevertheless, there exists a cooperative mechanism of regulation which involves individual response of each element to the corresponding hierarchical piece of information and leads to ideal system response due to self - processing of information. The particular results are obtained for bioheat transfer. However, self - regulation in other natural hierarchical systems seems to be organized in a similar way. The characteristics of large hierarchical systems occurring in nature are discussed from the stand point of regulation problems. By way of example, some ecological and economic systems are considered. An cooperative mechanism of self-regulation which enables the system to function ideally is proposed.

In the present monograph we formulate a simple model for heat transfer in living tissue with self - regulation. The initial point of the model is the governing equations describing heat transfer in living tissue at the mesoscopic level, i.e. considering different vessels individually. Then, basing on the well known equivalence of the diffusion type process and random walks, we develop a certain regular procedure that enables us to average these mesoscopic equations practically over all scales of the hierarchical vascular network. The microscopic governing equations obtained in this way describe living tissue in terms of an active medium with continuously distributed self - regulation. One of the interesting results obtained in the present monograph is that there can be the phenomena of ideal self - regulation in large active hierarchical systems. Large hierarchical systems are characterized by such a great information flow that none of its elements can possess whole information required of governing the system behavior. Nevertheless, there exists a cooperative mechanism of regulation which involves individual response of each element to the corresponding hierarchical piece of information and leads to ideal system response due to self - processing of information. The particular results are obtained for bioheat transfer. However, self - regulation in other natural hierarchical systems seems to be organized in a similar way. The characteristics of large hierarchical systems occurring in nature are discussed from the stand point of regulation problems. By way of example, some ecological and economic systems are considered. An cooperative mechanism of self-regulation which enables the system to function ideally is proposed.

The nonlinear response of noisy bistable systems driven by strong amplitude periodical force is investigated by physical experiment. The new phenomenon of locking of the mean switching frequency between states of bistable system is found. It is shown that there is an interval of noise intensities in which the mean switching frequency remains constant and coincides with the frequency of external periodic force. The region on the parameter plane ``noise intensity - amplitude of periodic excitation'' which corresponds to this phenomenon is similar to the synchronization (phase-locking) region (Arnold's tongue) in classical oscillatory systems.

During the last few years an area of active research in the field of complex systems is that of their information storing and processing abilities. Common opinion has it that the most interesting beaviour of these systems is found ``at the edge of chaos'', which would seem to suggest that complex systems may have inherently non-trivial information proccesing abilities in the vicinity of sharp phase transitions. A comprenhensive, quantitative understanding of why this is the case is however still lacking. Indeed, even ``experimental'' (i.e., often numerical) evidence that this is so has been questioned for a number of systems. In this paper we will investigate, both numerically and analitically, the behavior of Random Boolean Networks (RBN's) as they undergo their order-disorder phase transition. We will use a simple mean field approximation to treat the problem, and without lack of generality we will concentrate on a particular value for the connectivity of the system. In spite of the simplicity of our arguments, we will be able to reproduce analitically the amount of mutual information contained in the system as measured from numerical simulations.

We propose a model for a market which structure is of the tree form. Each branch of the tree is composed by identical firms, its root (the branch of the first level) is formed by the firms producing raw material, and the branches of the last level are the retail outlets. The branching points (tree nodes) are micromarkets for firms forming branches connected with a given node. The prices and the production rate are controlled by the ballance in supply and demand, the competition is assumed to be perfect. We show that such a market functions perfectly: the prices are specified by the production expense only, whereas demand determines the production rate. We construct an efficiency functional which extremal gives the governing equations for the market. It turns out that this ideal market is degenerated with respect to its structure. It is shown, that such market functions ideally: the prices are determined by the costs on production of the goods, and the level of production of the goods of any kind defined only by demand for the goods of this sort.

It is shown that due to memory effects the complex behaviour of components in a stochastic system can be transmitted to macroscopic evolution of the system as a whole. Within the Markov approximation widely using in ordinary statistical mechanics, memory effects are neglected. As a result, a time-scale separation between the macroscopic and the microscopic level of description exists, the macroscopic differential picture is no a consequence of microscopic non-differentiable dynamics. On the other hand, the presence of complete memory in a system means that all its components have the same behaviour. If the memory function has no characteristic time scales, the correct description of the macroscopic evolution of such systems have to be in terms of the fractional calculus.