Intro -- 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.

Key Features:Due to the heterogeneous nature of the audience the lectures avoid being overly specialized but maintain scientific integrity while reaching across disciplines thereby making them accessible to students at all levelsExplicit contact is made between the qualitative concepts of classical psychology such as the mind and the quantitative methods of psychophysics and neurophysiology by some of today's outstanding scientistsNo other treatment of decision making takes advantage of the technical developments that have been made in the analyses of complex networks over the past decade.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.