This book brings together scientists from all over the world who have defined and developed the field of Coordination Dynamics. Grounded in the concepts of self-organization and the tools of nonlinear dynamics, appropriately extended to handle informational aspects of living things, Coordination Dynamics aims to understand the coordinated functioning of a variety of different systems at multiple levels of description. The book addresses the themes of Coordination Dynamics and Dynamic Patterns in the context of the following topics: Coordination of Brain and Behavior, Perception-Action Coupling, Control, Posture, Learning, Intention, Attention, and Cognition.

This volume extends Clancey's successful exploration of Situated Cognition by examining how concepts may be physically represented as coordinated percepts. Cognitive scientists, both theoretical and applied, will find this book a fascinating read.

By offering a critical assessment of the evolution of standard game theory, this book argues for a shift in the ontology and methodology of game theory for appraising games, one based on understanding the players’ strategic reasoning process. Analyzing the history of economic thought, the book highlights the methodological issues faced by standard game theory in its treatment of strategic reasoning and the consequence it has on the status of players’ beliefs. It also highlights how the two original contributions of T. C. Schelling and M. Bacharach can be applied to these issues. Furthermore, the book assesses the intersubjective dimension in games by applying the cognitive sciences and by integrating simulation theory into game theory. Consequently, this book offers an interdisciplinary approach for reassessing the nature of the intersubjectivity involved in strategic reasoning. It shows that the analysis of games should involve the study and identification of the reasoning process that leads the players to a specific outcome, i.e., to a specific solution. A game should not be understood (as is done in standard game theory) as a mathematical representation of an individual choice at equilibrium. This requires investigating the players’ capacity for coordination. Understanding the process of coordination allows us to understand strategic reasoning and ultimately to provide new answers to the indeterminacy problem, one of the central hurdles in game theory, and one that underscores its normative difficulties.

As artificial intelligence (AI) is applied to more complex problems and a wider set of applications, the ability to take advantage of the computational power of distributed and parallel hardware architectures and to match these architec tures with the inherent distributed aspects of applications (spatial, functional, or temporal) has become an important research issue. Out of these research concerns, an AI subdiscipline called distributed problem solving has emerged. Distributed problem-solving systems are broadly defined as loosely-coupled, distributed networks of semi-autonomous problem-solving agents that perform sophisticated problem solving and cooperatively interact to solve problems. N odes operate asynchronously and in parallel with limited internode commu nication. Limited internode communication stems from either inherent band width limitations of the communication medium or from the high computa tional cost of packaging and assimilating information to be sent and received among agents. Structuring network problem solving to deal with consequences oflimited communication-the lack of a global view and the possibility that the individual agents may not have all the information necessary to accurately and completely solve their subproblems-is one of the major focuses of distributed problem-solving research. It is this focus that also is one of the important dis tinguishing characteristics of distributed problem-solving research that sets it apart from previous research in AI.