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One important issue that modern communication theory does not deal with is the physical substratum of information. We advance here the thesis that information is generated by the ever increasing complexity of a “self”‐organizing hierarchical system—which evolves via cascading bifurcations giving rise to broken symmetry. We call “Language” the process which reveals that information, namely the cognitive gadget which “compresses” the complexity generated by broken symmetry‐thereby providing “minimal length” algorithms for triggering an “internal representation” or the replication of the physical system involved. This compressibility has an obvious “survival value” since it allows the possessor of language to reduce and predict a rapidly changing environment. In characterizing language, like any open system far from equilibrium, the usual concept of free energy mediating a conflict between internal energy and entropy—is not only irrelevant but also wrong. The concepts of “complexity” and organization seem here more pertinent.

Brain‐like structures have evolved by performing signal processing initially by minimizing “tracking errors” on a competitive basis. Such systems are highly complex and at the same time notoriously “disordered”. The functional trace of the cerebral cortex of the (human) brain is a good example. The Electroencephalogram (E.E.G) appears particularly fragmented during the execution of mental tasks, as well as during the recurrent episodes of R.E.M. sleep. A stochastically regular or a highly synchronized E.E.G on the other hand, characterises a drowsy (relaxing) or epileptic subject respectively and indicates—in both cases—a very incompetent information processor. We suggest that such behavioral changeovers are produced via bifurcations which trigger the thalamocortical non‐linear pacemaking oscillator to switch from an unstable limit cycle to a strange attractor regime (i.e. to chaos), or vice versa. Our analysis aims to show that the E.E.G's characteristics are not accidental but inevitable and even necessary and, therefore, functionally significant.

The human mind possesses the unique capability of “mapping” the external (as well as part of the organism's internal) world i.e. it “compresses” long and complex strings of impinging environmental stimuli (“observations”) and then uses these “minimal length algorithms” in order to simulate physical phenomena‐thereby revealing the “laws of nature”. In this paper we theorize that this process of “Self”‐organization and category formation is implimented via a set of coexisting (strange) attractors in the cognizant apparatus each one of which attracts (and therefore compresses) whole subsets of “initial conditions” the sum‐total of which constitute the set of external stimuli. This set of the initial conditions forms the “Basin” of the attractors and the processes of partition and category formation in the mind involves the topology of the separatrixes amongst the individual subsets of the Basin. We examine in particular how the information processing is mediated by the thalamocortical pacemaker of the brain and, therefore, what might be the role of E.E.G (which is measurable on a routine basis) in Cognition.

The evolution of the conflict of “blackmail” between two individuals is dealt with—both for symmetric and asymmetric contests. State—space diagrams are presented illustrating the dynamical coevolution of the cooperathe propensities of the partners when the games are played inductively—and learning takes place via storing the result of the previous outcome. By changing the three parameters of the game α, č, k (the probability of yield— “chicken”—the tempting factor and the coefficient of mutual loss, respectively) we can modify drastically the probability of “locking‐in” at the cooperative state as well as the dynamical repertoire for each contestant (i.e. the number of states between which his strategy undergoes transitions as well as the probabilities of these transitions). Finally, we study the result of additive white noise on the trajectories of the cooperative propensities, both in the symmetric and the asymmetric case.

In this work we perform a detailed entropy analysis of some substitutive sequences using the technique of lumping. The basic novelty of the entropy analysis by lumping is that, unlike the Fourier transform or the conventional entropy analysis by gliding, it gives results that can be related to algorithmic aspects of the sequences and in particular with the important property of automaticity. All computations in this paper have been performed with TOOLS FOR SYMBOLIC DYNAMICS a Maple package developed by the authors.

Given a time‐series, what is the best partitioning of the state space in order to obtain reasonable values for the block entropies? The purpose of this paper is to provide a simple answer (an algorithm), although approximative, in connection with symbolic dynamics and statistical properties of 1‐d maps on the interval.

The logistic map is examined as an archetype of a Complex System with different behaviors, namely: periodicity, order‐to‐chaos period‐doubling transition, weak chaos, parametric intermittent chaos, developed chaos and fully developed chaos. For the logistic map the generating partition is known, and allows comparison with other prescriptions in the literature. The partitioning of the phase space with the easy generated bipartition induced by the mean value of a curve in the plane, gives results in good agreement (roughly up to a 20 per cent difference) with the results of the generating partition, if the trajectory of the system is in parametric intermittent chaos and in developed chaos (DC). In the case of fully developed chaos (FDC), the agreement is perfect.

The authors confirm that a statistical partitioning is almost equivalent with the exact partitioning for the logistic map.

The paper updates previous results and proposes a better understanding on the partitioning for symbolic dynamics.

Political economies evolve institutionally and technologically over time. This means that to understand evolutionary political economy one must understand the nature of the evolutionary process in its full complexity. From the time of Darwin and Spencer natural selection has been seen as the foundation of evolution. This view has remained even as views of how evolution operates more broadly have changed. An issue that some have viewed as an aspect of evolution that natural selection may not fully explain is that of emergence of higher order structures, with this aspect having been associated with the idea of emergence. In recent decades it has been argued that self-organization dynamics may explain such emergence, with this being argued to be constrained, if not overshadowed, by natural selection. Just as the balance between these aspects is debated within organic evolutionary theory, it also arises in the evolution of political economy, as between such examples of self-organizing emergence as the Mengerian analysis of the appearance of commodity money in primitive societies and the natural selection that operates in the competition between firms in markets.

Surveys some of the important contributions of information theory (IT) to the understanding of systems science and cybernetics. Presents a short background on the main definitions of IT, and examines in which way IT could be thought of as a unified approach to general systems. Analyses the topics: syntax and semantics in information, information and self‐organization, entropy of forms (entropy of non‐random functions), and information in dynamical systems. Enumerates some suggestions for further research and takes this opportunity to describe new points of view, mainly by using entropy of non‐random functions.

The purpose of this paper is to discuss the recognizability of Cantorian stochastic automata by generalized entropy‐like qualities.

The paper gives a necessary entropy condition, valid for all sequences on the alphabet {0, 1} read by lumping and generated by a Cantorian stochastic automaton.

The paper finds that, on this basis, once can determine that a given sequence is not generated by Cantorian stochastic automata and reconstruct the automaton when the sequence is generated by a Cantorian stochastic automaton.

This paper derives a new diagnostic for Cantorian stochastic automata, which could find a direct application to biology, where there is a recent claim that the coding regions of chromosomes form Cantor sets.