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This chapter outlines the contents of the book, with detailed descriptions of each of the chapters to follow. I discuss the overall motivations of the book, in particular the need for a clear understanding of the relationship between methodologies such as social simulation and the social sciences as a whole.

This chapter examines the philosophical underpinnings of simulation science, with particular emphasis on the new challenges created by the increased use of computational modelling throughout many disciplines of science. In order to illuminate these philosophical discussions, the discussion here focuses on simulation in the context of Artificial Life – a field which seeks the ‘simulation and synthesis of living systems’.

In this chapter, we investigate the use of simulation in Artificial Life as a means for ‘making the artificial real’ – and in doing so develop a framework for artificiality in computational models of living systems. I first describe the differences in the Strong and Weak Artificial Life perspectives, and how each of these attempts to justify itself in the broader context of the field and its efforts to investigate biological life in the digital realm.

This chapter takes our previous discussion of simulation in Artificial Life and places it into the broader context of population biology, which can be viewed as an earlier progenitor of Alife. Population biologists frequently use mathematical models to investigate the behaviour of animal populations, drawing from a similar methodological toolbox as that used by demographers, and in doing so have grappled with the difficulties inherent in modelling complex creatures and behaviours with only systems of equations. Scientists in this discipline have developed some intriguing theoretical frameworks for the use of such models and for describing their limitations, and here we see how these ideas can inform our own quest for a useful framework for computational models.

In Part I, our analysis of modelling focused primarily on the field of artificial life and population biology. In Part II, we will begin to investigate the application of agent-based modelling to the social sciences. This chapter will describe the current status of agent-based social science research, outlining some of the most influential work in this developing field. We will then investigate the methodological difficulties faced by modellers in this area, as well as the philosophical implications of an agent-based approach to the study of human social systems.

Having discussed the current state-of-the-art in modelling for the social sciences, we will begin to delve more deeply into the modelling frameworks discussed thus far. Both Alife and social science simulation will be studied using these modelling frameworks, which will allow us to examine the limitations of these approaches to modelling. We will then attempt to develop a framework for social science modelling, taking into account the needs of the discipline and the strengths and weaknesses of agent-based modelling in this context.

In this chapter we will examine one influential modelling project in detail: the residential segregation model by Schelling. This model has been a seminal example of abstract social modelling since its inception; our analysis will discuss the development of the model itself as well as its subsequent effect on the field and on modelling more generally. We will then examine Schelling’s simple yet powerful model in the light of the modelling frameworks we have developed to this point, and use the insights gained to further enhance these ideas.

In this conclusion to Part II, we bring together our methodological analysis of Alife, population biology and the social sciences and develop a comprehensive summary of the issues facing simulation modellers and how they may be managed. These ideas will be put into context using our bird migration example and incorporate ideas drawn from our analysis of Schelling’s residential segregation model. We will then discuss the further considerations needed to put these ideas into practice, and in the process lay the groundwork for Part III.

This chapter describes the history of demography, setting up the context for our discussions around the introduction of agent-based models into this area of social science. We will discuss the empirical strengths of demography, it’s theoretical shortcomings, and prospects for the future. Subsequent chapters will demonstrate the state-of-the-art in integrating agent-based models with the statistical frameworks more commonly used in demography.

Modern demography, while growing increasingly interdisciplinary over years, has largely remained a data-focused discipline. Demographic research often proceeds under the premises of logical empiricism, in which data is the primary focus on theoretical development or innovation receive far less emphasis. In this chapter, we will investigate some demographic models which have used agent-based approaches successfully to produce useful insight. We will discuss their methods and results in detail, and outline how these models have used simulation to push us closer to a model-based demography.