Catálogo de publicaciones - libros

This two-paper series deals with the problem of understanding the relations between viability and sustainability in the context of social metasystem transitions occurring in a global environment. PART I of the paper argues that metasystem transitions in human organizations can be better understood by resorting to concepts from Synergetics, such as those of slaving principle, order parameter and control within order parameter equations. The subject of Organizational Synergetics is thus introduced. After recognizing the centrality of observation processes, an integration to Stafford Beer’s Viable System Model is proposed, which includes a dedicated observing subsystem for the detection of both internal and environmental order parameters. The signal flow graph of the subsystem is constructed and a scheme of its overall transfer function is also derived.

This two-paper series deals with the problem of understanding the relations between viability and sustainability in the context of social metasystem transitions occurring in a global environment. In PART 1 of the series, the subject of Organizational Synergetics has been introduced, and an integration to Stafford Beer’s Viable System Model has been proposed. PART 2 argues that the identification of emergents and their relationships is not only necessary to improve organization’s viability but also to assess organization’s sustainability. The distinction is made, then, between local sustainability and global sustainability. Whereas the former is amenable to a standard computational treatment, the latter, it is argued, is an undecidable property of the global system comprising both the system in focus and its global environment. In force of its undecidable and holonic character, global sustainability can only be attained by resorting to suitable heuristics designed to guide global evolution. Finally, a strategy and a general heuristics, which is based on the concept of a Viable Holonic Network, are proposed.

Classical representation forms are not suited to represent knowledge as human mind does. In tasks as discourse comprehension knowledge stuctures have to adapt themselves on the basis of the objectives, the past experiences and the particular context. So we have developed a modular knowledge acquisition system based on cognitive criteria, that dynamically updates a representation by the use of a scale free graph model.

Random boolean networks (RBN) are well known dynamical systems, whose properties have been extensively studied in the case where each node has the same number of incoming connections, coming from other nodes chosen at random with uniform probability, and the updating is synchronous. In the past, the comparison with experimental results has been limited to some well-known tests; we review here some recent results that demonstrate that the availability of gene expression data now allows further testing of these models. Moreover, in this paper we summarize some recent results and present some novel data concerning the dynamics of these networks in the case where either the network has a scale-free topology or the updating takes place asynchronously.

The aim of this work is to study a metrics that represents the perceptive space of the colors. Besides we want to furnish innovative methods and tools for annotate and seek images. The experimental results have shown that in tasks of evaluation of the similarity, the subjects don’t refer to the most general category of “color”, but they create subordinate categories in base to some particular color. Those categories contain all the variations of this color and also they form intersections between categories in which any variations are shared. The perception of the variations is not isometric; on the contrary that perception is weighed in different manner if the variations belong to a particular color. So the variations that belong to the intersection area will have different values of similarity in relation to the own category. We developed a system of color-oriented content-based image retrieval using this metrics. This system analyzes the image through features of color correspondents to the own perception of the human being. Beyond to guarantee a good degree of satisfaction for the user, this approach furnishes a novelty in the development of the CBIR systems. In fact there is the introduction of a criterion to index the figures; it is very synthetic and fast.

In this paper we consider the correspondence between the centrality of the role of the observer for the concepts of probability and emergence. We base our considerations on the fundamental insight of the Italian mathematician Bruno de Finetti who introduced the concept of probability of an event as the observer’s degree of belief. This correspondence is very important for dealing with modern problems of uncertainty related to chaos and complexity and to the modelling emergence.

General Systems Theory produced many cultural and scientific results and approaches based on some fundamental aspects like the interaction between components, distinguished from relation. Inter-disciplinarity is introduced as the disciplinary study of systemic properties. Trans-disciplinarity is introduced as the study of systemic properties in general and of relationships among them. Finally Systemics is introduced as cultural generalization of the principles contained in the General Systems Theory. In reference to many new scientific disciplinary results we introduce the need to update the concepts and models of Systemics. We introduce a short review of those results, like Collective Phenomena; Phase Transitions in physics; Dynamical Usage of Models (DYSAM); Multiple systems, emerging from the same elements, but having simultaneous different interactions among them; Uncertainty Principles; Laws of scaling; Modelling emergence; Systemic meaning of new theorizations like Quantum Field Theories (QFT) in physics with related applications in biology, in studying the brain, consciousness, and in dealing with long-range correlations. The study of emergence undertaken in many disciplinary fields, like Physics, Biology, Artificial Life, Information Technology and Economics, has been realized focusing on the web of fundamental problems of General Systems Theory like the transition between non-systemic and systemic phases. The problem of modelling emergence relates to modelling processes of interaction between components and the observer. Dealing with those new problems and results calls for new theoretical approaches for Systemics. The change is expected to be so innovative to name this process with particular reference to emergence: Systemics of emergence or Second Systemics. We stress the need that the systems community, honouring its tradition and mission, be active part and leads this process, while at the present the most important research activities on Systemics seem to take place disciplinarily, out from the system community.

I consider whether or not a discrete dynamical system has two isomorphic representations, one recursive and the other non-recursive; if it does not, the system can be said to be an intrinsic computational system. I prove that intrinsic computational systems exist, as well as non-intrinsic ones, and I finally argue that some representation of a non-intrinsic computational system is not effective with respect to the state-space structure of the system.

The paper gives the reasons for analogies in physics showing that they arise from the natural link between global physical variables and the space elements, i.e. points, lines, surfaces and volumes.

This paper introduces a new neural network model of players’ behavior in iterated Prisoner Dilemma Game. Differently from other models of this kind, but in accordance with theoretical framework of evolutionary game theory, it takes into account players’ expectancies in computation of individual moves at every game step. Such a circumstance, however, led to an increase of the number of model free parameters. It was therefore necessary, to search for optimal parameter values granting for a satisfactory fitting of data obtained in an experiment performed on human subjects, to resort to a genetic algorithm.