While the significance of networks in various human behavior and activities has a history as long as human's existence, network awareness is a recent scientific phenomenon. The neologism network science is just one or two decades old. Nevertheless, with this limited time, network thinking has substantially reshaped the recent development in economics, and almost all solutions to real-world problems involve the network element. This book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The authors begin with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling's segregation model and Axelrod's spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The text also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. It reviews a number of pioneering and representative models in this family. Upon the given foundation, the second part reviews three primary forms of network dynamics, such as diffusions, cascades, and influences. These primary dynamics are further extended and enriched by practical networks in goods-and-service markets, labor markets, and international trade. At the end, the book considers two challenging issues using agent-based models of networks: network risks and economic growth.

Agent-based modelling enables the explicit representation of entities and their interaction with each other and the environment, and so it became an important method to study complex systems. Social networks form an important part of agent-based social simulation, as they define the topology of agent interaction. This dissertation initially identifies important properties of social networks and their dynamics and reviews their representation in agent-based models of relevant domains. A classification of levels of detail for the network modelling components initialisation, dynamics of networks, and dynamics on networks is proposed and guides the identification of deficits. A formal, iterative evaluation framework is developed to quantitatively assess network modelling approaches under a set of weighted criteria (representativity, adjustability, validity, and efficiency). The framework is applied to an abstract model of opinion dynamics and to an empirically grounded model of social influence. A lifestyle-specific network survey is designed, conducted, and analysed and helps to ground the evaluation of the network modelling’s representativity on empirical data. The study finds significant differences of degree and distance distributions as well as in the composition of ego networks between lifestyles. New network modelling approaches are developed to account for requirements in agent-based models such as agent-type specific link preferences, degree and distance distributions, community structures, and interaction dynamics. The comparison of simple to elaborated network modelling for the application models shows a significant impact on simulation results, highlighting the need for informed decisions about suitable approaches.

Analytical sociology is a strategy for understanding the social world. It is concerned with explaining important social facts such as network structures, patterns of residential segregation, typical beliefs, cultural tastes, and common ways of acting. It explains such facts by detailing in clear and precise ways the mechanisms through which the social facts were brought about. Making sense of the relationship between micro and macro thus is one of the central concerns of analytical sociology. The approach is a contemporary incarnation of Robert K. Merton's notion of middle-range theory and presents a vision of sociological theory as a tool-box of semi-general theories each of which is adequate for explaining certain types of phenomena. The Handbook brings together some of the most prominent sociologists in the world. Some of the chapters focus on action and interaction as the cogs and wheels of social processes, while others consider the dynamic social processes that these actions and interactions bring about.

Decision makers in large scale interconnected network systems require simulation models for decision support. The behaviour of these systems is determined by many actors, situated in a dynamic, multi-actor, multi-objective and multi-level environment. How can such systems be modelled and how can the socio-technical complexity be captured? Agent-based modelling is a proven approach to handle this challenge. This book provides a practical introduction to agent-based modelling of socio-technical systems, based on a methodology that has been developed at TU Delft and which has been deployed in a large number of case studies. The book consists of two parts: the first presents the background, theory and methodology as well as practical guidelines and procedures for building models. In the second part this theory is applied to a number of case studies, where for each model the development steps are presented extensively, preparing the reader for creating own models.

To fully understand not only the past, but also the trajectories, of human societies, we need a more dynamic view of human social systems. Agent-based modeling (ABM), which can create fine-scale models of behavior over time and space, may reveal important, general patterns of human activity. Agent-Based Modeling for Archaeology is the first ABM textbook designed for researchers studying the human past. Appropriate for scholars from archaeology, the digital humanities, and other social sciences, this book offers novices and more experienced ABM researchers a modular approach to learning ABM and using it effectively. Readers will find the necessary background, discussion of modeling techniques and traps, references, and algorithms to use ABM in their own work. They will also find engaging examples of how other scholars have applied ABM, ranging from the study of the intercontinental migration pathways of early hominins, to the weather–crop–population cycles of the American Southwest, to the trade networks of Ancient Rome. This textbook provides the foundations needed to simulate the complexity of past human societies, offering researchers a richer understanding of the past—and likely future—of our species.

The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The authors begin with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling's segregation model and Axelrod's spatial game. The text shows that the modern network science mainly driven by game-theorists andsociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks.

This unique book brings together a comprehensive set of papers on the background, theory, technical issues and applications of agent-based modelling (ABM) within geographical systems. This collection of papers is an invaluable reference point for the experienced agent-based modeller as well those new to the area. Specific geographical issues such as handling scale and space are dealt with as well as practical advice from leading experts about designing and creating ABMs, handling complexity, visualising and validating model outputs. With contributions from many of the world’s leading research institutions, the latest applied research (micro and macro applications) from around the globe exemplify what can be achieved in geographical context. This book is relevant to researchers, postgraduate and advanced undergraduate students, and professionals in the areas of quantitative geography, spatial analysis, spatial modelling, social simulation modelling and geographical information sciences.