Springer Creativity and Innovation: Cognitive, Social, and Computational Approaches (Understanding Complex Systems)

This book focuses on the emergence of creative ideas from cognitive and social dynamics. In particular, it presents data, models, and analytical methods grounded in a network dynamics approach. It has long been hypothesized that innovation arises from a recombination of older ideas and concepts, but this has been studied primarily at an abstract level. In this book, we consider the networks underlying innovation – from the brain networks supporting semantic cognition to human networks such as brainstorming groups or individuals interacting through social networks – and relate the emergence of ideas to the structure and dynamics of these networks. Methods described include experimental studies with human participants, mathematical evaluation of novelty from group brainstorming experiments, neurodynamical modeling of conceptual combination, and multi-agent modeling of collective creativity. The main distinctive features of this book are the breadth of perspectives considered, the integration of experiments with theory, and a focus on the combinatorial emergence of ideas. From the Back Cover This book focuses on the emergence of creative ideas from cognitive and social dynamics. In particular, it presents data, models, and analytical methods grounded in a network dynamics approach. It has long been hypothesized that innovation arises from a recombination of older ideas and concepts, but this has been studied primarily at an abstract level. In this book, we consider the networks underlying innovation – from the brain networks supporting semantic cognition to human networks such as brainstorming groups or individuals interacting through social networks – and relate the emergence of ideas to the structure and dynamics of these networks. Methods described include experimental studies with human participants, mathematical evaluation of novelty from group brainstorming experiments, neurodynamical modeling of conceptual combination, and multi-agent modeling of collective creativity. The main distinctive features of this book are the breadth of perspectives considered, the integration of experiments with theory, and a focus on the combinatorial emergence of ideas. About the Author Simona Doboli is Professor of Computer Science at Hofstra University. She received her PhD from the University of Cincinnati in 2001. Her research interests are in artificial intelligence, neural cognitive modeling, creativity, and computational neuroscience. Her recent work includes developing algorithms for automatic assessment of novelty in short texts, studying interactions between group and individual level creativity, and developing neural cognitive models for idea generation processes. She has 19 journal articles and over 40 conference publications. She has served as an associate editor for the journals Cognitive Systems Research and IEEE Transactions on Neural Networks. She was the Tutorials Chair for the 2019 International Joint Conference on Neural Networks (IJCNN 2019), Special Sessions Chair for IJCNN 2011, and Workshops Chair for IJCNN 2013. Her research has been funded by the U.S. National Science Foundation and the U.S. Army Research Office.  Jared B. Kenworthy is an Associate Professor of Psychology at the University of Texas at Arlington. He earned his Ph.D. in social psychology from the University of Southern California in 2002, then completed a post-doctoral fellowship at the University of Oxford (UK) from 2002 until 2005. His research interests include both intergroup relations and group processes. Within intergroup relations, his interests include understanding the predictors of religious, ethnic, and national prejudice as well as prejudice reduction. Within group processes, his interests include the predictors and consequences of ingroup identification, and group coordination, and collective creativity and innovation. His work has been funded by the U.S. National Science Foundation an