CONTENTS -- Preface -- 1. Overview of ARO program on network science for human decision making B.J. West -- 1. Introduction -- 2. Background -- 2.1. What we know about networks -- 2.2. What we do not know about the linking of physical and human networks -- 3. What We Have Been Doing -- 3.1. Complexity theory and modeling without scales -- 3.2. Information propagation in complex adaptive networks -- 4. Preliminary Conclusions -- References -- 2. Viewing the extended mind hypothesis (Clark & Chambers) in terms of complex systems dynamics G. Werner -- 1. Background -- 2. On the Extended Mind Hypothesis -- 3. Brain and World as ONE Complex Dynamical System -- 4. Praxis Ahead of Theory -- 5. Conclusion -- References -- 3. Uncertainty in psychophysics: Deriving a network of psychophysical equations K.H. Norwich -- 1. Introduction -- 2. Philosophical Underpinnings -- 3. Mathematical Representation of the Psychophysical Law (Weber-Fechner and Stevens) -- 4. A Network of Equations Issuing from the Entropic Form of the Psychophysical Law -- 4.1. The differential threshold ( DH from Fechner's conjecture) and Weber's fraction -- 4.2. The hyperbolic law governing the magnitude of n ( DH from Miller's magical number) -- 4.3. Simple reaction time ( DH is the minimum quantity of information needed to react) -- 5. Searching for Support within Thermodynamics and Statistical Physics -- 5.1. Emergence of the Weber-Fechner law from thermodynamics -- 6. Discussion -- 6.1. Review -- 6.2. Quantum Sufficiat -- Acknowledgements -- References -- 4. The collective brain E. Tagliazucchi and D.R. Chialvo -- 1. Introduction -- 2. Emergent Complex Dynamics is always Critical -- 3. The Collective Large-scale Brain Dynamics -- 4. Neuronal Avalanching in Small Scale is Critical -- 5. Psychophysics and Behavior -- 6. An Evolutionary Perspective.

7. Noise or Critical Fluctuations? Equilibrium vs Non-equilibrium -- 8. Outlook -- Acknowledgements -- References -- 5. Acquiring long-range memory through adaptive avalanches S. Boettcher -- 1. Introduction -- 2. Motivation from Self-organized Criticality -- 3. Spin Glass Ground States with Extremal Optimization -- 4. EO Dynamics -- 5. Annealed Optimization Model -- 6. Evolution Equations for Local Search Heuristics -- 6.1. Extremal optimization algorithm -- 6.2. Update probabilities for extremal optimization -- 6.3. Update probabilities for metropolis algorithms -- 6.4. Evolution equations for a simple barrier model -- 6.5. Jamming model for -EO -- References -- 6. Random walk of complex networks: From infinitely slow to instantaneous transition to equilibrium N.W. Hollingshad, P. Grigolini and P. Allegrini -- 1. Introduction -- 2. Preliminary Remarks on the Size of a Complex Network -- 3. On the Master Matrix A -- 4. Transition to Equilibrium in Hierarchical Networks -- 5. Return to the Origin in a Scale-free Network -- 5.1. Ad hoc scale-free network -- 5.2. Hierarchical network -- 6. Conclusions -- Acknowledgements -- References -- 7. Coherence and complexity M. Bologna, E. Geneston, P. Grigolini, M. Turalska and M. Lukovic -- 1. Introduction -- 2. Analytical Approach to Synchronization -- 3. The Case of a Finite Number of Nodes -- 4. Concluding Remarks -- Acknowledgements -- References -- 8. Quakes in complex systems as a signature of cooperation E. Geneston and P. Grigolini -- 1. Introduction -- 2. Mittag-Leffler Function: Detection of the Phase-transition Occurrence -- 3. Description of the Model -- 4. Subordination Theory -- 5. Criticality -- 6. Temporal Complexity at Criticality -- 7. Avalanches -- 8. Complex Network -- 9. Concluding Remarks -- Acknowledgments -- References.

9. Renewal processes in the critical brain P. Allegrini, P. Paradisi, D. Menicucci and A. Gemignani -- 1. Introduction -- 1.1. Complexity and the brain -- 1.2. Brain activity, 1/f noise, and criticality -- 2. Rapid Transitions: A Serial Process Visiting Metastable States -- 2.1. Detection of rapid transition processes -- 2.2. Multichannel RTP detection -- 3. Serial and Critical Means Intermittent -- 3.1. The complexity index (what it is, how to measure it) -- 3.2. Event-driven random walks -- 3.3. Diffusion Entropy and Detrended Fluctuation Analysis -- 4. Superposition of Poisson Noise to Non-Poisson Signals -- 5. Complexity Matching -- References -- 10. The principle of complexity management B.J. West and P. Grigolini -- 1. Introduction -- 2. Wiener's Rule -- 3. From the Cerebral to the Celestial -- 4. Information Exchange between Complex Networks -- 5. Conclusions -- Acknowledgements -- References.