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.

Traditional network optimization focuses on a single control objective in a network populated by obedient users and limited dispersion of information. However, most of today's networks are large-scale with lack of access to centralized information, consist of users with diverse requirements, and are subject to dynamic changes. These factors naturally motivate a new distributed control paradigm, where the network infrastructure is kept simple and the network control functions are delegated to individual agents which make their decisions independently ("selfishly"). The interaction of multiple independent decision-makers necessitates the use of game theory, including economic notions related to markets and incentives. This monograph studies game theoretic models of resource allocation among selfish agents in networks. The first part of the monograph introduces fundamental game theoretic topics. Emphasis is given to the analysis of dynamics in game theoretic situations, which is crucial for design and control of networked systems. The second part of the monograph applies the game theoretic tools for the analysis of resource allocation in communication networks. We set up a general model of routing in wireline networks, emphasizing the congestion problems caused by delay and packet loss. In particular, we develop a systematic approach to characterizing the inefficiencies of network equilibria, and highlight the effect of autonomous service providers on network performance. We then turn to examining distributed power control in wireless networks. We show that the resulting Nash equilibria can be efficient if the degree of freedom given to end-users is properly designed. Table of Contents: Static Games and Solution Concepts / Game Theory Dynamics / Wireline Network Games / Wireless Network Games / Future Perspectives

As our understanding of complex social, economic, and technological systems improves, it is increasingly apparent that a full account of a system's macroscopic level properties requires us to carefully explore the structure of local, pairwise interactions that take place at the microscopic level. Over the past two decades, networks have emerged as the de facto representation of such systems, leading to the genesis of the interdisciplinary field of network science. During this same period, we have witnessed an explosion of participation and consumption of social media, advertising, and e-commerce on the internet; an ecosystem that is the embodiment of and whose success is fundamentally coupled to the use and exploitation of complex networks. What are the processes and mechanisms responsible for shaping these networks? Do these processes posses any inherent fairness? How can these structures be exploited for the benefit of strategic actors? In this dissertation, I explore these questions and present analytical results couched in a theory of strategic decision making -- algorithmic game theory. First, research is presented on the pairwise inequality that is inherent to strategic models of network formation. The network formation games that we consider model the creation of communication links between rational, self-interested individuals. We use the inequality ratio, defined as the ratio between the highest and lowest costs incurred by individual players in a given outcome, to quantify pairwise inequality. Tight upper bounds for the inequality ratio are derived for pure Nash equilibrium outcomes of the network formation games, and the relationship between inequality and efficiency (outcomes that are globally optimal) is characterized. Next, a multi-level network formation game modeled on the sociological principle of networked social capital is introduced and analyzed. Motivated by the observation that networks found in nature are rarely isolated from one another, this research examines a scenario in which individuals form intra-group links to maximize group cohesion and inter-group links to maximize their group's standing relative to other groups. Our model formulation draws heavily from the empirical research of sociologist Ronald S. Burt and his theory of structural holes, and we derive constructive proofs of the existence of equilibrium outcomes. Departing from the topic of network formation games, I next present research on the strategic seeding of opinions in social networks. This work, which is best introduced in terms of a political election, involves candidates (players) each choosing subsets of voters (nodes in a social network) to seed opinions that are subsequently spread through the voters' social connections by a dynamic process. After the opinions have been allowed to diffuse for a predetermined amount of time, the voters' opinions are aggregated in an election. Voters cast their votes probabilistically, where the probability that they choose a particular candidate is proportional to that voter's opinion toward the candidate relative to their opinion toward the other candidates. Players -- each representing a single candidate -- strategically choose a set of seed nodes that will maximize the probability that their candidate will win the election. We establish the guaranteed existence of pure Nash equilibrium outcomes in the special case where the opinion dynamics are allowed sufficient time to converge to a consensus opinion. We prove that this guarantee does not carry over to cases where the dynamics are not given sufficient time to reach a consensus. We also derive bounds on the budget multiplier, which characterizes the extent that any initial inequality (in terms of players' respective seed budgets) is amplified by the opinion dynamic over the social network into greater inequality in players' utilities. Finally, we analyze the computational problem of finding a player's best response (pure) strategy, proving that it is NP-complete but approximable to within a (1 - 1/e) factor of optimal by a simple greedy algorithm. The concluding chapter of this dissertation presents my work on algorithm instance games, which are a class of games that I have identified as being characterized by having outcomes that are derived from strategy profiles algorithmically. Primarily a conceptual contribution, this research identifies a class of games that includes algorithmic mechanism design as a special case. Two variants of a simple game from this class that is based on the Set Cover optimization problem are presented and analyzed to demonstrate how algorithmic design decisions can influence strategic behavior.

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, together with a wealth of applications. It presents the peer-reviewed proceedings of the VII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2018), which was held in Cambridge on December 11–13, 2018. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure and network dynamics; diffusion, epidemics and spreading processes; and 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.