This book provides a state-of-the-art overview on the dynamics and coevolution in multi-level strategic interaction games. As such it summarizes the results of the European CONGAS project, which developed new mathematical models and tools for the analysis, prediction and control of dynamical processes in systems possessing a rich multi-level structure and a web of interwoven interactions among elements with autonomous decision-making capabilities. The framework is built around game theoretical concepts, in particular evolutionary and multi-resolution games, and includes also techniques drawn from graph theory, statistical mechanics, control and optimization theory. Specific attention is devoted to systems that are prone to intermittency and catastrophic events due to the effect of collective dynamics.

This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the IX International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2020). The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, network dynamics; diffusion, epidemics and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks and technological networks.

Although the concept of policy networks is now well-established in the field, most research has to content itself with description and analysis of their contribution to policy failure. This book goes further. It accepts policy networks as a fundamental characteristic of modern societies and presents an overview of the strategies for the management of these networks, as well as illustrating the various strategies for intervention.

The primary function of the intelligence analyst is to make sense of information about the world, but the way analysts do that work will look profoundly different a decade from now. Technological changes will bring both new advances in conducting analysis and new risks related to technologically based activities and communications around the world. Because these changes are virtually inevitable, the Intelligence Community will need to make sustained collaboration with researchers in the social and behavioral sciences (SBS) a key priority if it is to adapt to these changes in the most productive ways. A Decadal Survey Of The Social and Behavioral Sciences provides guidance for a 10-year research agenda. This report identifies key opportunities in SBS research for strengthening intelligence analysis and offers ideas for integrating the knowledge and perspectives of researchers from these fields into the planning and design of efforts to support intelligence analysis.

In Collective Action and Exchange: A Game-Theoretic Approach to Contemporary Political Economy, William D. Ferguson presents a comprehensive political economy text aimed at advanced undergraduates in economics and graduate students in the social sciences. The text utilizes collective action as a unifying concept, arguing that collective-action problems lie at the foundation of market success, market failure, economic development, and the motivations for policy. Ferguson draws on information economics, social preference theory, cognition theory, institutional economics, as well as political and policy theory to develop this approach. The text uses classical, evolutionary, and epistemic game theory, along with basic social network analysis, as modeling frameworks. These models effectively bind the ideas presented, generating a coherent theoretic approach to political economy that stresses sometimes overlooked implications.

Models of Science Dynamics aims to capture the structure and evolution of science, the emerging arena in which scholars, science and the communication of science become themselves the basic objects of research. In order to capture the essence of phenomena as diverse as the structure of co-authorship networks or the evolution of citation diffusion patterns, such models can be represented by conceptual models based on historical and ethnographic observations, mathematical descriptions of measurable phenomena, or computational algorithms. Despite its evident importance, the mathematical modeling of science still lacks a unifying framework and a comprehensive study of the topic. This volume fills this gap, reviewing and describing major threads in the mathematical modeling of science dynamics for a wider academic and professional audience. The model classes presented cover stochastic and statistical models, system-dynamics approaches, agent-based simulations, population-dynamics models, and complex-network models. The book comprises an introduction and a foundational chapter that defines and operationalizes terminology used in the study of science, as well as a review chapter that discusses the history of mathematical approaches to modeling science from an algorithmic-historiography perspective. It concludes with a survey of remaining challenges for future science models and their relevance for science and science policy.