Social Self-Organization Agent-Based Simulations and Experiments to Study Emergent Social Behavior /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Helbing, Dirk (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2012.
• Σειρά: Understanding Complex Systems,
• Θέματα: Physics.; Game theory.; Statistical physics.; Dynamical systems.; Statistics.; Sociology.; Statistical Physics, Dynamical Systems and Complexity.; Numerical and Computational Physics.; Sociology, general.; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.; Game Theory, Economics, Social and Behav. Sciences.

 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. 

 Modeling of Socio-Economic Systems -- Agent-based modeling -- Self-Organization in Pedestrian Crowds -- Opinion Formation -- Spatial Self-Organization through Success-Driven Mobility -- Cooperation in Social Dilemmas -- Co-Evolution of Social Behavior and Spatial Organization -- Evolution of Moral Behavior -- Coordination and Competitive Innovation Spreading in Social Networks -- Heterogeneous Populations: Coexistence, Integration, or Conflict -- Social Experiments and Computers -- Learning of Coordinated Behavior -- Response to Information -- Systemic Risks in Society and Economics -- Managing Complexity -- Challenges in Economics.