Frank Schweitzer – författare

This bookconstitutes the refereed proceedings of the 12th International Conference andSchool of Network Science, NetSci-X 2016, held in Wroclaw, Poland, in January2016.The 12 fulland 6 short papers were carefully reviewed and selected from 59 submissions.The papers deal with the study of network models in domains ranging frombiology and physics to computer science, from financial markets to culturalintegration, and from social media to infectious diseases.

Die Erforschung komplexer Strukturen ist gegenwartig eines der interessantesten wissenschaftlichen Themen. Dieses Buch behandelt Moglichkeiten der Beschreibung und quantitativen Charakterisierung komplexer Strukturen mit Hilfe verschiedener Entropie- und Informationsmasse. Nach einer allgemein verstandlichen Einfuhrung der Grundbegriffe werden die fur eine quantitative Analyse erforderlichen Konzepte ausfuhrlich behandelt und an zahlreichen Beispielen, wie Zeitreihen, Biosequenzen, literarischen Texten und Musikstucken, veranschaulicht.

When we contemplate phenomena as diverse as electrochemical deposition or the spatial patterns of urban development, it is natural to assume that they havenothing incommon. Afterall,therearemanylevelsinthehierarchythat builds up from atoms to human society, and the rules that govern atoms are quite di?erent from those that govern the geographical emergence of a city. The common view among many, if not most, biologists and social scientists is that the devil is entirely in the details. This school of thought asserts that social science and biology have little or nothing in common, and indeed many biologists claim that even di?erent ?elds of biology have little in common. If they are right, then science can only proceed by recording vast lists of details that no common principles will ever link together. Physics, in contrast, has achieved a parsimonious description for a broad range of phenomena based on only a few general principles. The phenomena that physics addresses are unquestionably much simpler than those of biology or social science, and on the surface appear entirely dissimilar. A cell is far more complicated than a pendulum or an atom, and human society, being builtoutofagreatmanycells,isfarmorecomplicatedstill. Cellsandsocieties have many layers of hierarchical organization, with complex functional and computational properties; they have identities, idiosyncracies stemming from an accumulation of historical contingency that makes them impossible to characterize in simple mathematical terms. Their complexity is far beyond that of the simple systems usually studied in physics.

When we contemplate phenomena as diverse as electrochemical deposition or the spatial patterns of urban development, it is natural to assume that they havenothing incommon. Afterall,therearemanylevelsinthehierarchythat builds up from atoms to human society, and the rules that govern atoms are quite di?erent from those that govern the geographical emergence of a city. The common view among many, if not most, biologists and social scientists is that the devil is entirely in the details. This school of thought asserts that social science and biology have little or nothing in common, and indeed many biologists claim that even di?erent ?elds of biology have little in common. If they are right, then science can only proceed by recording vast lists of details that no common principles will ever link together. Physics, in contrast, has achieved a parsimonious description for a broad range of phenomena based on only a few general principles. The phenomena that physics addresses are unquestionably much simpler than those of biology or social science, and on the surface appear entirely dissimilar. A cell is far more complicated than a pendulum or an atom, and human society, being builtoutofagreatmanycells,isfarmorecomplicatedstill. Cellsandsocieties have many layers of hierarchical organization, with complex functional and computational properties; they have identities, idiosyncracies stemming from an accumulation of historical contingency that makes them impossible to characterize in simple mathematical terms. Their complexity is far beyond that of the simple systems usually studied in physics.

Scientific progress during the last three decades has greatly profited from our advances in understanding complex systems. Fundamental modeling approaches were considerably improved, particularly agent-based modeling, network science, nonlinear dynamics, and system science. At the same time, these approaches have been applied to and adopted by various scientific disciplines, ranging from physics and chemistry to engineering, molecular biology, economics, and the social sciences.This book reflects the success of complexity science both from a historical and a modeling perspective. It uses 25 articles from different disciplines, published over 25 years, to demonstrate the power and problems of modeling complex systems.The book's four parts, Agent-based Models, Network Models, Models of System Dynamics, and Models of Evolution, each provide an informative synopsis of the respective modeling approach. An introductory overview summarizes each approach's essential concepts, addresses the main research directions, and reviews applications in various disciplines. The selection of reprinted publications is motivated by their scientific relevance and methodological contributions to understanding complex phenomena. A chronological list of publications details the development of each modeling approach over the past 25 years.

Economics and the social sciences are, in fact, the “hard” sciences, as Herbert Simon argued, because the complexity of the problems dealt with cannot simply be reduced to analytically solvable models or decomposed into separate subprocesses. Nevertheless, in recent years, the emerging interdisciplinary “sciences of complexity” have provided new methods and tools for tackling these problems, ranging from complex data analysis to sophisticated computer simulations. In particular, advanced methods developed in the natural sciences have recently also been applied to social and economic problems.The twenty-one chapters of this book reflect this modern development from various modeling perspectives (such as agent-based models, evolutionary game theory, reinforcement learning and neural network techniques, time series analysis, non-equilibrium macroscopic dynamics) and for a broad range of socio-economic applications (market dynamics, technological evolution, spatial dynamics and economic growth, decision processes, and agent societies). They jointly demonstrate a shift of perspective in economics and the social sciences that is allowing a new outlook in this field to emerge.