Simplicial Complexes In Complex Systems

For the last few decades researchers from different fields gather their findings and knowledge trying to give a shape to the new science of complex systems. To address this problem, new tools and methods have to be established. A new, or more precisely an alternative, framework for the characterization of complex system was proposed. In this book we will introduce the applicability of applicability of simplicial complexes in the science of complex systems. After introducing the main definitions and properties of simplicial complexes necessary for representation and analysis of complex systems, we will illustrate the usefulness and versatility of tools and concepts related to the simplicial complexes.

The book discusses the potential of higher-order interactions to model real-world relational systems. Over the last decade, networks have emerged as the paradigmatic framework to model complex systems. Yet, as simple collections of nodes and links, they are intrinsically limited to pairwise interactions, limiting our ability to describe, understand, and predict complex phenomena which arise from higher-order interactions. Here we introduce the new modeling framework of higher-order systems, where hypergraphs and simplicial complexes are used to describe complex patterns of interactions among any number of agents. This book is intended both as a first introduction and an overview of the state of the art of this rapidly emerging field, serving as a reference for network scientists interested in better modeling the interconnected world we live in.

The modern world is complex beyond human understanding and control. The science of complex systems aims to find new ways of thinking about the many interconnected networks of interaction that defy traditional approaches. Thus far, research into networks has largely been restricted to pairwise relationships represented by links between two nodes. This volume marks a major extension of networks to multidimensional hypernetworks for modeling multi-element relationships, such as companies making up the stock market, the neighborhoods forming a city, people making up committees, divisions making up companies, computers making up the internet, men and machines making up armies, or robots working as teams. This volume makes an important contribution to the science of complex systems by: (i) extending network theory to include dynamic relationships between many elements; (ii) providing a mathematical theory able to integrate multilevel dynamics in a coherent way; (iii) providing a new methodological approach to analyze complex systems; and (iv) illustrating the theory with practical examples in the design, management and control of complex systems taken from many areas of application. Contents:IntroductionSets and RelationsHypergraphs and the Galois LatticeSimplicial Complexes and Q-AnalysisBackcloth and Traffic: Dynamics Constrained by TopologyHypernetworksMultilevel SystemsTime, Events, Prediction and ForecastingHypernetworks and DesignPolicy, Design, Planning and ScienceNotes and Reflections Readership: Academics: researchers, scientists, social scientists, complex systems scientists, economists, biologists; Industry: managers, planners, consultants, knowledge professionals, systems engineers, automation specialists, supply chain managers; Public services: Council Officers, city planners, transport planners, health professionals, welfare professionals. Key Features:A first book of its kind on 'hypernetworks', a term the author has coined to describe the mathematical structures he has worked on over the last 30 yearsIntroduces hypernetworks in the context of examples drawn from a wide range of fields, including design, planning, management, art, neural network, machine vision, robotics, traffic, road accidents, amongst othersThe emphasis of this volume is on useful and added-value applications. It will develop the necessary mathematics and assuming no pre-requisitesContains a myriad of illustrations to further understand the topics discussedReviews: “The mathematics is explicitly explained and no difficult theorems are included in this book in order to enable scientists, designers and policy makers to work together.” Zentralblatt MATH

Computational and mathematical models provide us with the opportunities to investigate the complexities of real world problems. They allow us to apply our best analytical methods to define problems in a clearly mathematical manner and exhaustively test our solutions before committing expensive resources. This is made possible by assuming parameter(s) in a bounded environment, allowing for controllable experimentation, not always possible in live scenarios. For example, simulation of computational models allows the testing of theories in a manner that is both fundamentally deductive and experimental in nature. The main ingredients for such research ideas come from multiple disciplines and the importance of interdisciplinary research is well recognized by the scientific community. This book provides a window to the novel endeavours of the research communities to present their works by highlighting the value of computational modelling as a research tool when investigating complex systems. We hope that the readers will have stimulating experiences to pursue research in these directions.

This book is a presentation of my recent research work into the theory and applications of mathematical relations between finite sets. The consequent results and concepts are expressed in 'conjunction' with the idea of a union of simplicial complexes and the algorithms and overall methodology have become known as Q-Analysis. The mathematical ideas are essentially comhinatorial and are developed in Section-A of the book, whilst Section-R contains an application to a community study (that of the IJniversity of Rssex). All of the work has been published elsewhere, in the usual form of papers, and a comprehensive introduction to the whole approach will be found in my earlier book, Mathematical Structure in Human Affairs (Heinemann, London 1974). Much of the research work referred to in this present hook would not have been possible without the co-operation of my research team, of whom I would particularly like to thank J.II. Johnson, and its puhlication owes a lot to the secretarial help provided by ~Iary Low. R.H. Atkin IJniversity of Rssex U.K.

Higher-order networks describe the many-body interactions of a large variety of complex systems, ranging from the the brain to collaboration networks. Simplicial complexes are generalized network structures which allow us to capture the combinatorial properties, the topology and the geometry of higher-order networks. Having been used extensively in quantum gravity to describe discrete or discretized space-time, simplicial complexes have only recently started becoming the representation of choice for capturing the underlying network topology and geometry of complex systems. This Element provides an in-depth introduction to the very hot topic of network theory, covering a wide range of subjects ranging from emergent hyperbolic geometry and topological data analysis to higher-order dynamics. This Elements aims to demonstrate that simplicial complexes provide a very general mathematical framework to reveal how higher-order dynamics depends on simplicial network topology and geometry.