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Preface; Acknowledgments; Contents; 1 Complexity and Swarming Systems; 2 A Biologically Inspired Approach to Collective Behaviors; 2.1 Collective Animal Behaviors; 2.2 Ethology; 2.3 Why Biological Inspiration?; 2.4 What Nature Teaches Us About Swarming; 2.4.1 Self-Organization and the Importance of Order in Life; 2.4.2 Positive Feedback and the Emergence of Order; 2.4.3 Collective Behavior Without Large-Scale Order; 2.4.4 Information Processing and Swarm Intelligence; References; 3 A Physical Approach to Swarming; 3.1 Self-Organization in Physicochemical Systems
3.1.1 Elementary Cellular Automata3.1.2 Collective Phenomena in Physical Systems; 3.1.3 Collective Motion; 3.2 The Self-Propelled Particles (SPP) Model; 3.2.1 Dynamical Foundations; 3.2.2 Neighborhood of Interactions; 3.2.3 Dynamic Update Rule; 3.3 What Statistical Physics Teaches Us; 3.3.1 Phase Transitions; 3.3.2 Scaling and Universality; 3.3.3 Fluctuations, Correlations, Susceptibility, and Nonapparent Collective Behavior; 3.3.4 Nonequilibrium Systems and Self-Organized Criticality; 3.4 What the Theory of Dynamical Systems Teaches Us
3.4.1 Bifurcation, Catastrophe, Collapse, and Tipping Point3.4.2 At the Edge of Chaos; 3.5 Inspiration and Swarm Design; References; 4 A Network-Theoretic Approach to Collective Dynamics; 4.1 A Science of Networks; 4.2 Swarm Signaling Networks; 4.3 Network Properties and Swarm Dynamics; 4.3.1 Assembling the Swarm Signaling Network; 4.3.2 Connectedness of the Signaling Network; 4.3.3 Shortest Connecting Path; 4.3.4 Clustering Coefficient; 4.3.5 Degree Distribution; 4.3.6 Resilience of Swarming; 4.3.7 Controllability of Swarming; 4.3.8 Swarm Network Dynamics
4.4 Design of Signaling Network for Artificial Swarming4.4.1 Models of Signaling Networks; 4.4.2 Enhanced Swarming Behaviors; 4.4.3 Some Words of Caution; References; 5 An Information-Theoretic Approach to Collective Behaviors; 5.1 Social Information Transmission; 5.2 Role of Information in Collective Behaviors; 5.3 Information Flow in Swarms; 5.3.1 Quantifying Information; 5.3.2 Dynamics of Information Transfer; 5.3.3 Transmission Channels; 5.3.4 Capacity of the Transmission Channel; 5.3.5 Informational Bottlenecks in Collective Behaviors
5.3.6 Conditions for the Emergence of Collective Behavior Under Data Rate Limitations5.3.7 Swarming Collapse Under Data Rate Limitations; 5.4 Information and Swarm Design; 5.4.1 Acquisition of Stimuli Information by the Swarm; 5.4.2 Dynamic Balancing of Positive and Negative Feedback Loops; 5.4.3 Leveraging Technological Advances for Novel Swarm Designs; 5.4.4 Coupling Between Information Flow and Agent's Movement; References; 6 A Computational Approach to Collective Behaviors; 6.1 From Collective Behavior to Computation and Information Processing
3.1.1 Elementary Cellular Automata3.1.2 Collective Phenomena in Physical Systems; 3.1.3 Collective Motion; 3.2 The Self-Propelled Particles (SPP) Model; 3.2.1 Dynamical Foundations; 3.2.2 Neighborhood of Interactions; 3.2.3 Dynamic Update Rule; 3.3 What Statistical Physics Teaches Us; 3.3.1 Phase Transitions; 3.3.2 Scaling and Universality; 3.3.3 Fluctuations, Correlations, Susceptibility, and Nonapparent Collective Behavior; 3.3.4 Nonequilibrium Systems and Self-Organized Criticality; 3.4 What the Theory of Dynamical Systems Teaches Us
3.4.1 Bifurcation, Catastrophe, Collapse, and Tipping Point3.4.2 At the Edge of Chaos; 3.5 Inspiration and Swarm Design; References; 4 A Network-Theoretic Approach to Collective Dynamics; 4.1 A Science of Networks; 4.2 Swarm Signaling Networks; 4.3 Network Properties and Swarm Dynamics; 4.3.1 Assembling the Swarm Signaling Network; 4.3.2 Connectedness of the Signaling Network; 4.3.3 Shortest Connecting Path; 4.3.4 Clustering Coefficient; 4.3.5 Degree Distribution; 4.3.6 Resilience of Swarming; 4.3.7 Controllability of Swarming; 4.3.8 Swarm Network Dynamics
4.4 Design of Signaling Network for Artificial Swarming4.4.1 Models of Signaling Networks; 4.4.2 Enhanced Swarming Behaviors; 4.4.3 Some Words of Caution; References; 5 An Information-Theoretic Approach to Collective Behaviors; 5.1 Social Information Transmission; 5.2 Role of Information in Collective Behaviors; 5.3 Information Flow in Swarms; 5.3.1 Quantifying Information; 5.3.2 Dynamics of Information Transfer; 5.3.3 Transmission Channels; 5.3.4 Capacity of the Transmission Channel; 5.3.5 Informational Bottlenecks in Collective Behaviors
5.3.6 Conditions for the Emergence of Collective Behavior Under Data Rate Limitations5.3.7 Swarming Collapse Under Data Rate Limitations; 5.4 Information and Swarm Design; 5.4.1 Acquisition of Stimuli Information by the Swarm; 5.4.2 Dynamic Balancing of Positive and Negative Feedback Loops; 5.4.3 Leveraging Technological Advances for Novel Swarm Designs; 5.4.4 Coupling Between Information Flow and Agent's Movement; References; 6 A Computational Approach to Collective Behaviors; 6.1 From Collective Behavior to Computation and Information Processing