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This chapter presents the history of agent-based modeling and simulation (ABMS) including John Conway's “Game of Life”, Thomas Schelling's housing segregation model, and John Holland's seven features of complex adaptive systems. It also discusses how ABMS is related to important neighboring fields of knowledge and technology such as multi-agent systems, management science, operations research, and network science.

This introductory chapter begins with a brief description of the use of the term ‘network’ in this volume, followed by discussions of non-network and network approaches. An overview of the subsequent chapters is also presented. All the contributions in this volume make explicit use of network models, and the majority from social network analysis (SNA). Many deal with themes that are not a particular strength of SNA, such as dynamic time evolution, geography, and material culture. There are ways of making more use of the powerful techniques of network science, at the risk of moving away from the social questions that ought to be primary; and at the other end of the spectrum, there are those more firmly embedded in the humanities making use of network ideas more figuratively.

This book is about the integration of agent-based modeling and network science. The leading chapter gives a brief historical review as a background and motivation of the book. The historical review begins with the network origin of agent-based models, the rising of autonomous agents and network sciences, the game-theoretic formulation of networks, and the agent-based formulation of networks, and ends with networks as an alternative manifestation of markets. Along this series of development, we see how network work becomes an indispensable element of agent-based models, and how agents-based models can be a powerful tool for modeling the dynamics on and of networks. With this large picture, the book is divided into three parts: foundations, primary dynamics on and of networks, and applications.

Complexity has achieved this paradigm status in both physical and social science disciplines, and forms a theoretical underpinning in fields including communications and linguistics. Complexity has not yet reached a similar status in social work. The study of complexity arose because a group of scientists believed that complex systems across many natural, societal, and technological domains shared similarities. These similarities include being adaptive, self-correcting, or emergent. This introduction traces the beginning of this emerging field of inquiry and introduces related concepts often found in complex systems that are relevant to social work research and evaluation.

Chapter 1 constitutes Part I of the book: ‘Single and Multilayer Networks’. This chapter introduces multilayer networks as an important new development of Network Science that allows a more comprehensive understanding of Complex Systems. It identifies the main motivations driving the research activity in this field of multilayer networks and emphasizes the benefits of taking a multilayer network perpective to characterize network data. The main advantages of a multilayer network approach with respect to the more traditional single layer characterization of complex networks are broadly discussed, focusing on the information gain resulting from the analysis of multilayer networks, the non-reducibility of a multilayer network to a large single network and the rich interplay between structure and function in multilayer networks.

This chapter attempts to answer the following five questions: (1) What is network science and social network analysis? (2) Why are networks important for studying, understanding, and influencing population health? (3) What do we know about how social networks influence population health, especially infectious and chronic diseases and their associated risk factors? (4) What are the primary methods used by network scientists when studying health? (5) What are some important challenges and opportunities for social network analysis and population health? In so doing this chapter sets the stage for the reader to become familiar with social network analysis as a tool for population health.

In distinction to a view of networks as the universal, originary, or necessary form that promises to explain everything from neural structures to collective animal behaviors to online traffic, this coda emphasizes the contingency of the network imaginary. If so many things and relationships are figured as networks, the Coda asks, what is not a network? Or, if so much can be treated as interconnected, does anything escape connectivity? If a network points toward particular logics and qualities of relation in our historical present, what others might we envision in the future? In many ways, these questions are unanswerable from the position of the present. Instead of adopting novel avant-garde aesthetics (to move beyond networks) or opting out of networks (in some cases, to recover elements of pre-networked existence), these final pages propose a third orientation: one of ambivalence that operates as a mode of extreme presence.

This chapter studies the scientific and technological paradigms behind digital networks, and how they have been applied in the assemblage of digital social networks. The technological part of digital networks is made up of computer code or algorithms. Human-computer interaction (HCI) enables easier computer interaction by moving away from designing procedures and toward analyzing interactions, the fluid interplay between machines and humans. The chapter then considers how computer science and network science give shape to a network that structures sociality for its users. These sciences help transform social signs and meanings into technological templates that organize reality.

Several mathematical and statistical concepts that support investigating complex phenomena within social work need reinforcing. This chapter reviews several of these concepts so that readers may apply them when investigating complexity in social work. Reviewing and building upon this general knowledge provides a sufficient basis for understanding complex networks and interconnected environments in which social workers conduct their practices and research. After completing this chapter, readers will gain a preliminary understanding of the mathematical concepts behind complexity theory, understand the role of game theory in the development of complexity, and possess the ability to link these mathematical concepts to social service applications.

This chapter is concerned with the characterization of innovation using the random-copying (neutral) model. It explores databases to test differences between genres over the short, modern, and long prehistoric timescales and within several different niches of knowledge production. This chapter shows that social science examples displayed more indications of drift, consistent with the neutral model. It suggests that it is difficult to distinguish purposeful selection from random drift. It highlights an alternative, objective approach which is the revitalized efforts at social modeling within the physical sciences, particularly in network science.

Roman tableware distribution is traditionally explored through establishing presences in specific places, often visualized as dots on a map. As such these attestations seem to represent distinct entities that do not relate, other than in their relative proximity. This chapter challenges an exclusively geographical perspective of tableware distribution by proposing a networks approach for exploring ceramic distributions. It states that it is equally informing to explore the dynamics between physical and relational space. This chapter aims at addressing the following issues: To what extent can the relationships between attestations of tableware sherds inform us on processes that led to the distribution of the original vessels as we know it, and how can topological and geographical networks complement each other in understanding such processes?