Cellular automata are fully discrete, computational, or dynamical systems, characterised by a local, totally decentralised action. Although extremely simple in structure, they are able to represent arbitrarily complex phenomena. However, due to the very big number of rules in any nontrivial space, finding a local rule that globally unfolds as desired remains a challenging task. In order to help along this direction, here we present the current state of cellular automata templates, a data structure that allows for the representation of sets of cellular automata in a compact manner. The template data structure is defined, along with processes by which interesting templates can be built. In the end, we give an illustrative example showcasing how templates can be used to explore a very large cellular automaton space. Although the idea itself of template has been introduced before, only now its conceptual underpinnings and computational robustness rendered the notion effective for practical use.
A Fully Operational Framework for Handling Cellular Automata Templates
Mauricio Verardo and Pedro P. B. de Oliveira
Volume 2019, Article ID 6573793, 11 pages
Mathematical and computational models are key tools for understanding biological phenomena. In the last decades, scientific and technological advances have facilitated their evergrowing adoption in biologically oriented research. The strongly interdisciplinary character of these areas, in which biologists work along with researchers from physical sciences, engineering, and medicine, fosters the cross-fertilization between scientific fields. However, the large degree of structural and parametric uncertainty typically associated with biological processes makes it nontrivial to analyze them using techniques imported from fields in which these issues are less prevalent. Thus, there is a need for new methodological developments that fill this gap. The present special issue addresses this need by providing an overview of current open problems and presenting recent results regarding mathematical inference and modelling of biological systems.
Computational Methods for Identification and Modelling of Complex Biological Systems
Alejandro F. Villaverde, Carlo Cosentino, Attila Gábor, and Gábor Szederkényi
Volume 2019, Article ID 4951650, 3 pages
Nature’s spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life’s creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the first of two special issues on current research on OEE and on the more general concept of open-endedness. Most of the papers presented in these special issues are elaborations of work presented at the Third Workshop on Open-Ended Evolution, held in Tokyo as part of the 2018 Conference on Artificial Life.
Open-Ended Evolution and Open-Endedness: Editorial Introduction to the Open-Ended Evolution Special Issue
Norman Packard, Mark A. Bedau, Alastair Channon, Takashi Ikegami,
Volume 25 | Issue 1 | Winter 2019
The origins of religion and of complex societies represent evolutionary puzzles. The ‘moralizing gods’ hypothesis offers a solution to both puzzles by proposing that belief in morally concerned supernatural agents culturally evolved to facilitate cooperation among strangers in large-scale societies. Although previous research has suggested an association between the presence of moralizing gods and social complexity, the relationship between the two is disputed, and attempts to establish causality have been hampered by limitations in the availability of detailed global longitudinal data. To overcome these limitations, here we systematically coded records from 414 societies that span the past 10,000 years from 30 regions around the world, using 51 measures of social complexity and 4 measures of supernatural enforcement of morality. Our analyses not only confirm the association between moralizing gods and social complexity, but also reveal that moralizing gods follow—rather than precede—large increases in social complexity. Contrary to previous predictions, powerful moralizing ‘big gods’ and prosocial supernatural punishment tend to appear only after the emergence of ‘megasocieties’ with populations of more than around one million people. Moralizing gods are not a prerequisite for the evolution of social complexity, but they may help to sustain and expand complex multi-ethnic empires after they have become established. By contrast, rituals that facilitate the standardization of religious traditions across large populations25,26 generally precede the appearance of moralizing gods. This suggests that ritual practices were more important than the particular content of religious belief to the initial rise of social complexity.
Complex societies precede moralizing gods throughout world history
Harvey Whitehouse, Pieter François, Patrick E. Savage, Thomas E. Currie, Kevin C. Feeney, Enrico Cioni, Rosalind Purcell, Robert M. Ross, Jennifer Larson, John Baines, Barend ter Haar, Alan Covey & Peter Turchin
Multilayer networks preserve full information about the different interactions among the constituents of a complex system, and have recently proven quite useful in modelling transportation networks, social circles, and the human brain. A fundamental and still open problem is to assess if and when the multilayer representation of a system is a qualitatively better model than the classical single-layer aggreagated network approach. Here we tackle this problem from an algorithmic information theory perspective. We propose an intuitive way to encode a multilayer network into a bit string, and we define the complexity of a multilayer network as the ratio of the Kolmogorov complexity of the bit strings associated to the multilayer and to the corresponding aggregated graph. We find that there exists a maximum amount of additional information that a multilayer model can encode with respect to an equivalent single-layer graph. We show how our measure can be used to obtain low-dimensional representations of multidimensional systems, to cluster multilayer networks into a small set of meaningful super-families, and to detect tipping points in different time-varying multilayer graphs. These results suggest that information-theoretic approaches can be effectively employed in the study of multi-dimensional complex systems, and pave the way to a more systematic analysis of static and time-varying multidimensional complex systems.
Algorithmic complexity of multiplex networks
A. Santoro, V. Nicosia