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The limits of Big Data - Fortune Features

The limits of Big Data - Fortune Features | FuturICT Books | Scoop.it
Two new books argue that algorithms can’t fully replace human judgment. A review of Samuel Arbesman’s The Half-Life of Facts and Nate Silver’s The Signal and The Noise.
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Quantitative Sociodynamics

Quantitative Sociodynamics | FuturICT Books | Scoop.it
Presents a unified and comprehensive overview of stochastic methods to describe quantitatively behavioral changes based on social interaction processes Includes numerous applications of the theoretical concepts Offers the key source in sociodynamics from one of the founders of this interdisciplinary field crossing boundaries of physics, mathematics and social sciences Contains six new chapters and more than 140 pages of new material

This new edition of Quantitative Sociodynamics presents a general strategy for interdisciplinary model building and its application to a quantitative description of behavioral changes based on social interaction processes. Originally, the crucial methods for the modeling of complex systems (stochastic methods and nonlinear dynamics) were developed in physics and mathematics, but they have very often proven their explanatory power in chemistry, biology, economics and the social sciences as well. Quantitative Sociodynamics provides a unified and comprehensive overview of the different stochastic methods, their interrelations and properties. In addition, it introduces important concepts from nonlinear dynamics (e.g. synergetics, chaos theory). The applicability of these fascinating concepts to social phenomena is carefully discussed. By incorporating decision-theoretical approaches, a fundamental dynamic model is obtained, which opens new perspectives in the social sciences. It includes many established models as special cases, e.g. the logistic equation, the gravity model, some diffusion models, evolutionary game theory and social field theory. Moreover, it implies numerous new results and is relevant for various application areas, such as opinion formation, migration, the self-organization of behavioral conventions, and the behavior of customers and voters. Theoretical results are complemented and illustrated by numerous computer simulations.

Quantitative Sociodynamics is relevant both for social scientists and natural scientists who are interested in the application of stochastic and synergetics concepts to interdisciplinary topics.

Content Level » Research

Keywords » Boltzmann Equation Social Sciences - Book Quantitative Sociodynamics - Evolutionary Game Theory - Fokker Planck Equation - Langevin Equation Social Science - Master Equation Social Sciences - Mathematical Modelling Social Sciences - Modelling Social Structures - Nonlinear Dynamics - Opinion Formation Model - Quantitative Model Social Processes - Self Organization - Social Field - Social Force - Social Sciences - Stochastic Methods - Success Driven Motion - Understanding Collective Behavior

Related subjects » Applications - Complexity - Operations Research & Decision Theory - Probability Theory and Stochastic Processes - Social Sciences

TABLE OF CONTENTS
Introduction and Summary.- Dynamic Decision Behavior.- Part I: Stochastic Methods and Non-Linear Dynamics: Master Equation in State Space.- Boltzmann-Like Equations.- Master Equation in Configuration Space.- The Fokker-Planck Equation.- Langevin Equations and Non-Linear Dynamics.- Part II: Quantitative Models of Social Processes: Overview.- Problems and Terminology.- Decision Theoretical Specification of the Transition Rates.- Opinion Formation Models.- Social Fields and Social Forces.- Evolutionary Game Theory.- Determination of the Model Parameters from Empirical Data.- List of Symbols used in Part I and II.-

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Why Society is a Complex Matter

Why Society is a Complex Matter | FuturICT Books | Scoop.it
Society is complicated. But this book argues that this does not place it beyond the reach of a science that can help to explain and perhaps even to predict social behaviour. As a system made up of many interacting agents – people, ...

 

Explains and exemplifies the application of complex-systems theory to understanding real and pressing societal problems, such as financial crises, pandemics, war and terrorism, human mobility and migration


Written in concise and accessible language by acclaimed science writer Philip Ball
Relevant to a broad readership – from non-specialist scientist and technologists to public administrators and the interested public

 

Society is complicated. But this book argues that this does not place it beyond the reach of a science that can help to explain and perhaps even to predict social behaviour. As a system made up of many interacting agents – people, groups, institutions and governments, as well as physical and technological structures such as roads and computer networks – society can be regarded as a complex system. In recent years, scientists have made great progress in understanding how such complex systems operate, ranging from animal populations to earthquakes and weather. These systems show behaviours that cannot be predicted or intuited by focusing on the individual components, but which emerge spontaneously as a consequence of their interactions: they are said to be ‘self-organized’. Attempts to direct or manage such emergent properties generally reveal that ‘top-down’ approaches, which try to dictate a particular outcome, are ineffectual, and that what is needed instead is a ‘bottom-up’ approach that aims to guide self-organization towards desirable states.

This book shows how some of these ideas from the science of complexity can be applied to the study and management of social phenomena, including traffic flow, economic markets, opinion formation and the growth and structure of cities. Building on these successes, the book argues that the complex-systems view of the social sciences has now matured sufficiently for it to be possible, desirable and perhaps essential to attempt a grander objective: to integrate these efforts into a unified scheme for studying, understanding and ultimately predicting what happens in the world we have made. Such a scheme would require the mobilization and collaboration of many different research communities, and would allow society and its interactions with the physical environment to be explored through realistic models and large-scale data collection and analysis. It should enable us to find new and effective solutions to major global problems such as conflict, disease, financial instability, environmental despoliation and poverty, while avoiding unintended policy consequences. It could give us the foresight to anticipate and ameliorate crises, and to begin tackling some of the most intractable problems of the twenty-first century.

Content Level » Popular/general

Keywords » Complex networks and epidemic spreading - Crisis observatories and decision-support systems - Dynamics of conflict - Economic and financial crisis - FuturICT - Philip Ball - Social simulation and social complexity

Related subjects » Complexity - Economics - Information Systems and Applications - Social Sciences

TABLE OF CONTENTS
Society: a Complex Problem.- On the Road: Predicting traffic.- Every Move You Make: Patterns of crowd movement.- Making Your Mind Up: Norms and decisions.- Broken Windows: The spread and control of crime.- The Social Web: Networks and their failures.- Spreading It Around: Mobility, disease and epidemics.- After the Crash: Economic and financial systems.- Love Thy Neighbour: How to foster cooperation.- Living Cities: Urban development as a complex system.- The Transformation of War: Modelling modern conflict.- Towards a Living Earth Simulator: The FuturICT Project.

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Managing Complexity: Insights, Concepts, Applications

Managing Complexity: Insights, Concepts, Applications | FuturICT Books | Scoop.it
Each chapter in Managing Complexity focuses on analyzing real-world complex systems and transferring knowledge from the complex-systems sciences to applications in business, industry and society. The interdisciplinary contributions range from markets and production through logistics, traffic control, and critical infrastructures, up to network design, information systems, social conflicts and building consensus. They serve to raise readers' awareness concerning the often counter-intuitive behavior of complex systems and to help them integrate insights gained in complexity research into everyday planning, decision making, strategic optimization, and policy.

Intended for a broad readership, the contributions have been kept largely non-technical and address a general, scientifically literate audience involved in corporate, academic, and public institutions.

Content Level » Research

Keywords » complex networks - complexity control - critical infrastructures - risk management

Related subjects » Applications - Business Information Systems - Complexity - Innovation - Technology Management - Operations Research & Decision Theory

TABLE OF CONTENTS
Editorial.- Managing Complexity: An Introduction.- Market Segmentation - The Network Approach.- Managing Autonomy and Control in Economic Systems.- Complexity and the Enterprise: The Illusion of Control.- Benefits and Drawbacks of Simple Models for Complex Production Systems.- Logistics Networks - Coping With Nonlinearity and Complexity.- Repeated Auction Games and Learning Dynamics in Electronic Logistics Marketplaces.- Decentralized Approaches to Adaptive Traffic Control.- Critical Infrastructures Vulnerability: The Highway Networks.- Trade Credit Networks and Systemic Risk.- A Complex System's View of Critical Infrastructures.- Bootstrapping the Long Tail in Peer to Peer Systems.- Coping With Information Overload Through Trust-Based Networks.- Complexity in Human Conflict.- Fostering Consensus in Multidimensional Continuous Opinion Dynamics Under Bounded Confidence.- Multi-Stakeholder Governance- Emergence and Transformational Potential of a New Political Paradigm.- Evolutionary Engineering of Complex Functional Networks.- Path Length Scaling and Discrete Effects in Complex Networks.- Index.

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Social Self-Organization

Social Self-Organization | FuturICT Books | Scoop.it
Comprehensive research overview by the leading scientist
Agent-based modelling for a braod range of applications
From mobility in opinion space to mobility in geographical space
New approaches to manage complexity in socio-economic systems
What are the principles that keep our society together? This question is even more difficult to answer than the long-standing question, what are the forces that keep our world together. However, the social challenges of humanity in the 21st century ranging from the financial crises to the impacts of globalization, require us to make fast progress in our understanding of how society works, and how our future can be managed in a resilient and sustainable way. This book can present only a few very first steps towards this ambitious goal. However, based on simple models of social interactions, one can already gain some surprising insights into the social, ''macro-level'' outcomes and dynamics that is implied by individual, ''micro-level'' interactions. Depending on the nature of these interactions, they may imply the spontaneous formation of social conventions or the birth of social cooperation, but also their sudden breakdown. This can end in deadly crowd disasters or tragedies of the commons (such as financial crises or environmental destruction). Furthermore, we demonstrate that classical modeling approaches (such as representative agent models) do not provide a sufficient understanding of the self-organization in social systems resulting from individual interactions. The consideration of randomness, spatial or network interdependencies, and nonlinear feedback effects turns out to be crucial to get fundamental insights into how social patterns and dynamics emerge. Given the explanation of sometimes counter-intuitive phenomena resulting from these features and their combination, our evolutionary modeling approach appears to be powerful and insightful. The chapters of this book range from a discussion of the modeling strategy for socio-economic systems over experimental issues up the right way of doing agent-based modeling. We furthermore discuss applications ranging from pedestrian and crowd dynamics over opinion formation, coordination, and cooperation up to conflict, and also address the response to information, issues of systemic risks in society and economics, and new approaches to manage complexity in socio-economic systems.
Parts of this book were previously published in peer reviewed journals.

Content Level » Research

Keywords » Agent Based Modelling - Behaviour Social Networks - Computational Social Science - Emergent Social Beahiour - Innovation Spreading Networks - Managing Complexity - Opinion Formation Social System - Risks Society Economics - Self Organization Crowds - Socio-economic Systems

Related subjects » Applications - Complexity - Social Sciences - Social Sciences & Law - Theoretical, Mathematical & Computational Physics

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