Behavioral Network Science: Language, Mind, and Society

Behavioral Network Science explains how and why structure matters in the behavioral sciences. Exploring open questions in language evolution, child language learning, memory search, age-related cognitive decline, creativity, group problem solving, opinion dynamics, conspiracies, and conflict, readers will learn essential behavioral science theory alongside novel network science applications. This book also contains an introductory guide to network science, demonstrating how to turn data into networks, quantify network structure across scales, and hone one's intuition for how structure arises and evolves. Online R code allows readers to explore the data and reproduce all the visualizations and simulations for themselves, empowering them to make contributions of their own. For data scientists interested in gaining a professional understanding of how the behavioral sciences inform network science, or behavioral scientists interested in learning how to apply network science from the ground up, this book is an essential guide.

Table of Contents:
Part I. A Brief Guide to Network Science: 1. Making and recognizing networks; 2. Network metrics; 3. Generative network models and network evolution; Part II. Language and Learning: 4. Zipf's law of meaning: the degree distribution of the mind; 5. Network learning: growing a lexicon by degrees; 6. What is distinctive: exploring edge types in multi-layer networks; 7. The small-world spectrum: using small worlds to compare networks; 8. The birthplace of new words: identifying node origins; 9. Agent-Based models of language emergence: structure favors the orangutan; Part III. Mental Processes: 10. False memories: spreading activation in memory networks; 11. Cognitive foraging: exploration versus exploitation in memory search; 12. Age-related Cognitive Decline: a network enrichment account; 13. Creativity: how noisy processes create novel structure; Part IV. Social Dynamics: 14. Network illusions: how structure misleads us; 15. Group problem solving: harnessing the wisdom of the crowds; 16. The Segregation of belief: how structure facilitates false consensus; 17. The conspiracy frame: coherence through self-supporting beliefs; 18. The Kennedy paradox: games of conflict and escalation; 19. Fund people not projects: a universal basic income for research; References.

About the Author :
Dr Thomas Hills is a Professor of Psychology at the University of Warwick. He directs the Behavioral and Data Science MSc at the University of Warwick, concentrating on how humans represent and navigate information in the mind and society. He has previously held fellowships with the Alan Turing Institute and the Royal Society.

Review :
'In the network of books Behavioral Network Science is the 'positive deviant' that acts as a much-needed broker between the behavioral sciences and network science. Written in a lively and engaging style, it is a must-read for anyone who wishes to acquire a powerful set of tools to measure structure, and ultimately, study the relation between structure and behavior, in language, the human mind, and societies.' Cynthia Siew, National University of Singapore 'Thomas Hills' book is a unique and necessary resource for behavioral scientists wishing to use networks to understand human cognition and sociality, as well as for modelers from other sciences aiming to capture network processes underlying human behavior.' Mirta Galesic, Santa Fe Institute 'What a treat and treasure trove of knowledge and ideas this book is! It tackles fundamental questions about behavior through network science. It provides insights into scientific puzzles from language to social dynamics. It is a must-read for anyone eager to understand the underlying structures that shape our world and behaviors.' Ralph Hertwig, Max Planck Institute for Human Development, Germany 'Behavioral network science connects networks with a remarkably wide spectrum of cognitive processes, from language to memory. Readers will gain fascinating insights into how phenomena that plague our society, like gerrymandering, polarization, echo chambers, and conspiracy theories, can be studied and understood through network models of social interactions.' Filippo Menczer, Indiana University 'Network science distills situations to their essence - objects and relations between those objects. This beautifully written and witty book shows how insightful this perspective is for unifying phenomena as diverse as terrorist cells, language learning, conspiratorial beliefs, grant funding reform, artistic creativity, and dolphin whistles. Readers will be empowered to analyze and understand the networks that they contain and that contain them.' Robert Goldstone, Indiana University 'Hills makes the computational tools of network science accessible to the behavioral scientist. The example code gives those who are new to this perspective the power to make discoveries at the micro-, meso-, and macro-levels of whatever phenomenon they study. This interdisciplinary approach is the future of the social sciences.' Michael Vitevitch, The University of Kansas 'The book is the most comprehensive resource bridging network science and behavioral sciences. It is a must read for anyone interested in this rapidly developing field and a must teach in any networks class for students in psychology and adjacent fields.' Yoed Kenett, Technion University, Israel 'Behavioral Network Science is an engaging guide for behavioral scientists interested in exploring complex networks. This book is well-structured, clear, and concise, yet it also provides rich information for conducting both foundational and cutting-edge methodological research with psychological, social, and political data.' Massimo Stella, Universita di Trento, Italy 'Behavioral Network Science takes the reader from the basics of graph theory to advanced topics, all the while linking to studies of behavior. It's network science for cognitive science. Thomas Hills brings the material to life, providing the reader with a valuable perspective on the structure of behavior.' Brad Love, University College London