Artificial Societies

The Computer Simulation Of Social Life

Nigel Gilbert, Rosaria Conte, Nigel Gilbert, Rosaria Conte

• Publisher: Routledge
• Year: 2006
• Print ISBN: 9781138963924
• eBook ISBN: 9781135367305
• Edition: 1
• Topic: Social Sciences
• Subtopic: Sociology
• Index: Social Sciences

An exploration of the implications of developments in artificial intelligence for social scientific research, which builds on the theoretical and methodological insights provided by "Simulating societies".; This book is intended for worldwide library market for social science subjects such as sociology, political science, geography, archaeology/anthropology, and significant appeal within computer science, particularly artificial intelligence. Also personal reference for researchers.

Chapter 1
Introduction
Computer simulation for social theory

Rosaria Conte and Nigel Gilbert

In the Introduction to this volume’s predecessor, Simulating societies: the computer simulation of social phenomena (Gilbert & Doran 1994), the editors, Nigel Gilbert and Jim Doran, described the state of the art in research on social simulation and defined social simulation as a particular approach to the study of societies that involved “the use of precisely specified simulation models, often formulated and run on computers”. This approach, said the editors, has been “fruitful in the past and…appears likely to be even more so in the future” (p. 1). Their forecast has proved to be correct even within the short time since it was made. Three different indicators suggest that the use of computer simulations for studying essential aspects of societies has gained importance:

• The number of studies relying on this methodology has increased.
• The number of disciplines involved has also grown. Once, studies in social simulation were drawn from the central core of the social sciences and from computer science.Now other disciplines, such as cognitive science, biology, neuroscience, and some artificial intelligence (AI) subfields, such as distributed artificial intelligence and research on multi-agent systems, have been showing a growing interest in the computer-based study of societies. The motivations that are leading people with a nonsocial scientific background to this common area of investigation are obviously heterogeneous. However, there are some broad questions of cross-disciplinary interest that seem to demand the use of computer simulation. These include evolutionary and dynamic models, the problem of complexity and the paradigm of emergence. Later in this chapter we shall return to these questions and to the necessity of using computer simulation to deal with them successfully.
• The number of theoretical perspectives involved has increased. Even radically different approaches, such as conventional AI rule-based systems and research based on neural nets and genetic algorithms, share the use of computer simulation as their methodological grounding. The more differentiated the conceptual and theoretical framework of social simulation becomes, the more influential and significant will be its scientific role.

Doran and Gilbert (1994) argue that computer simulation is an appropriate methodology whenever a social phenomenon is not directly accessible, either because it no longer exists (as in archaeological studies) or because its structure or the effects of its structure, i.e. its behaviour, are so complex that the observer cannot directly attain a clear picture of what is going on (as in some studies of world politics). The simulation is based on a model constructed by the researcher that is more observable than the target phenomenon itself. This raises issues immediately about which aspects of the target ought to be modelled, how the model might be validated and so on. However, these issues are not so much of an epistemological stumbling block as they might appear. Once the process of modelling has been accomplished, the model achieves a substantial degree of autonomy. It is an entity in the world and, as much as any other entity, it is worthy of investigation. Models are not only necessary instruments for research, they are themselves also legitimate objects of enquiry. Such “artificial societies” and their value in theorizing will be the concern of the first part of this chapter.

In the second part, we will consider an idea that, although central to social simulation, remains controversial: the idea of “emergence”. Emergence is also a major concern for many fields of investigation adjacent to and intertwined with social simulation; for example, the study of artificial life, biology, and solid state physics. It recurs in many chapters in this book and was addressed specifically during the second Symposium on Simulating Societies, held in Siena, Italy, in July 1993, the meeting at which initial versions of most of the chapters in this book were first presented. We shall argue that social simulation may help to reformulate the question of emergence in more specific and empirically relevant terms.

The study of “possible” societies

The study of natural societies is the original objective of social simulation. The behaviour of a model of a society can be observed “in vitro” and the underlying theory tested. For example, this use of computer simulation lies at the heart of the studies of Mayan “collapse” carried out in the 1960s and 1970s (for a review, see Doran & Gilbert 1994), and Palaeolithic social organization (see Doran et al. 1994; Doran & Palmer in this volume).

During the 1980s, a different use of computer simulation developed within the social sciences. For example, within game theory, computer simulation has been applied to study the differential dynamics of processes and to assess the mechanisms and reasons for social learning; more specifically, the spreading of different social “strategies”, or behavioural patterns. As well as testing theories of society, experimental simulations are run to observe which interactional strategy among several existing in a given population is most likely to survive and spread over time, and why.

Such studies of the dynamics of social processes demonstrate another role for computer simulation. The question here is not “what has happened?”, as in the case of the computer study of the Mayan collapse or even “what might have happened?”, but rather “what are the sufficient conditions for a given result to be obtained?”. While the first two questions are exclusively descriptive, the latter may have prescriptive consequences. It may provide hints about how to enhance or reinforce some social strategies by telling us, for example, under what conditions these strategies become stabilized. Promising studies are being carried out within the social sciences, some AI subfields, operational research, organization theory, management science and so on, with the normative aim of reinforcing socially desirable outcomes or optimizing co-operation and co-ordination.That this also gives an indirect account of how things are, in fact, or have been, is only a secondary aspect of the work.

This alternative role for social simulation deserves attention. We will not be directly concerned with purely prescriptive studies, although for a variety of reasons the goal of optimization has had a strong impact on the computer-based study of social processes. Another, at least equally important, objective in this work is to realize, observe and experiment with “artificial societies” in order to improve our knowledge and understanding, but through exploration, rather than just through description. In this mode of research, the target is no longer a natural society, but an artificial one, existing only in the mind of the researcher. A new target (the artificial system) is created with its own structure (the architecture of the system) and behaviour (the simulation). When a simulation is run, the system operates in a certain way and displays certain behaviour. The simulation may either provide a test of the model and its underlying theory, if any, or may simply allow the experimenter to observe and record the behaviour of the target system. As the emphasis shifts from describing the behaviour of a target system in order to understand natural social systems the better to exploit the behaviour of a target for its own sake, so the objective of the research changes to the observation of and experimentation with possible social worlds. With the possibility of constructing artificial systems, a new methodology of scientific inquiry becomes possible.

The value of building artificial societies is not to create new entities for their own sake. Such an approach, although common to much research and debate about artificial life, has only moderate scientific interest. It is exemplified by some philosophical disputes about whether artificial beings may be considered as living (see Emmeche 1994, Harnad 1994, Langton 1994). These disputes are of little relevance because their resolution depends entirely on how the notion of “life” is defined.

Our stress, instead, is on a new experimental methodology consisting of observing theoretical models performing on some testbed. Such a new methodology could be defined as “exploratory simulation”. The exploratory aim synthesizes both the prescriptive and descriptive objectives: on the one hand, as with the testing of existing theories, the aim is to increase our knowledge; but on the other, as happens with studies orientated to the optimization of real life processes, the aim is not to reproduce the social world, but to create new, although not necessarily “better”, or more desirable, systems. Here lies the difference from optimization research.

Exploratory research based on social simulation can contribute typically in any of the following ways:

• implicit but unknown effects can be identified. Computer simulations allow effects analytically derivable from the model but as yet unforeseen to be detected;
• possible alternatives to a performance observed in nature can be found; (c) the functions of given social phenomena can be carefully observed (we will
• the functions of given social phenomena can be carefully observed (we will return to this issue later); and
• “sociality”, that is, agenthood orientated to other agents, can be modelled explicitly. In the next section, we shall show why simulation is of particular value for the development of social theory about sociality.

Artificial societies and artificial sociality

Notwithstanding the importance of its theoretical contributions, the study of society has always been plagued with some crucial methodological and empirical difficulties. For example, social theorists often fail to provide sufficiently specific models, categories of analysis and conceptual instruments for the exploration of social reality. As a result, a wide gap continues to exist between empirical research and theorizing.

In the past, sociologists used to embrace distinct approaches and combine suggestions and intuitions from other disciplines in highly comprehensive systems of thought (consider, for example, the role of biological and evolutionary models in structural-functionalism in general, and the impact of the psychological and psychoanalytic literature on Parsons’ theory of action in particular). Nowadays, sociologists have all but abandoned the creation of large, comprehensive theories. However, many also seem to have relinquished ambitions to engage in any form of modelling of social reality. Dissatisfied with the poor predictive or explanatory power of theories of action, sociologists have taken refuge in claiming that social reality cannot be described scientifically because it is construed by and only accessible through the minds of heterogeneous agents. Consequently, social thinkers are said to be entitled, at most, to gather and interpret lay people’s self-reports about their actions. And often the warrant for these interpretations is far from clear.

An exception to this general trend is represented by those approaches to the study of sociality that are grounded in mathematical terms, such as game theory Within this area, there is active theorizing and empirical work. Not surprisingly, however, these approaches share a strong methodological individualistic assumption; that is, the idea that there is nothing about societies that cannot be said about the microinteractions among its members (see Chapter 8). Consequently, the specific aim of sociological inquiry, the study of societies, is essentially nullified.

Social simulation studies provide an opportunity to fill the gap between empirical research and theoretical work, while avoiding the individualist tendency of most mathematically-based approaches. In particular, social simulation provides not only a methodology for testing hypotheses, but also an observatory of social processes. It can therefore offer the basis for new efforts to devise categories of description and new analyses of social reality. In other words, social simulation can provide instruments for modelling sociality.

A couple of examples will help to clarify what is meant by the modelling of sociality as opposed to developing large comprehensive social theories, on the one hand, and atheoretical, empiricist research on the other.

Between co-operation and conflict

Usually, social action is considered to be a choice between co-operation and conflict (think of the structure of the “Prisoner’s Dilemma”, where each player is faced with the choice of whether to renounce part of the immediate reward to let the opponent have a share of the cake, or else get as much as possible and forget about the other players’ outcomes). However, such a simple dichotomy is fallacious, for several reasons:

• there are many forms of co-operation and conflict, as well as many forms of altruistic and selfish, desirable and undesirable social action;
• the alternatives are not clear-cut: there are forms of apparently cooperative action that may be regarded as being conflictual. Social exchange, for example, although notionally a form of co-operation, may give rise to manipulation and produce inequality;
• many concrete examples of sociality are neither advantageous nor harmful to cooperation. For example, conversation is itself neither intrinsically conflictual nor cooperative. Furthermore, many abstract categories of social action are neither pro- nor anti-social. Consider, for example, communication, which may be either co-operative or aggressive; and influence and persuasion, which may be either selfish or altruistic;and
• a social action may be governed by a complex hierarchy of goals so that a given action’s lower-level goals may differ from the higher-level ones. A pro-social action may be included in an anti-social plan and vice versa. Deception may be used for helping, and co-operation for cheating.

Natural interaction is undoubtedly much richer than is allowed by the basic categories of co-operation and conflict. To understand and model social action and account for the complex and varied forms of interaction means working out subtler categories of analysis than are usually employed by social thinkers. The fine-grained modelling of sociality allowed by the study of artificial society could greatly enhance our capacity to account for the complexity of interaction.

Self-sufficient agents and social interference

Often, social agents are thought of as self-sufficient beings acting in a common world. The term applied by some social scientists to describe structural social relations is “interference”, essentially referring to agents accidentally hindering or facilitating one another. This view is limiting, since it does not account for the role of the structures that pre-exist interaction (Conte & Sichman, in press). It does not show that certain forms of social action are present in nuce in certain types of structural relations and emerge from them. A detailed description of different types of social structures would allow the relationships between social structures and social action to be modelled in more revealing ways.

Groups and coalition formation

The classical view of groups and coalition formation is based on two ideas: first, coordination to overcome interference and, secondly, collaboration involving social intentions and beliefs.

Co-ordination

If agents are conceived to be self-sufficient beings, once they are in a social context they will find constraints to their self-fulfilment. The common social environment and the other agents limit their autonomy and achievements. This may be ameliorated through co-ordination of the agents’ actions.

Collaboration

Co-ordination alone is insufficient for any sort of coalition or agreement to take place among rational agents. Agents also need to have some degree of awareness of others’ existence, wants and habits. In other words, agents need to have some mental representation of other agents’ minds. These representations can be divided into two main categories:

• Strategic beliefs (in the sense of strategic rationality), which consist of individuals’mental states produced by, and taking into account, the mental states of other, possibly interfering, agents.
• Group beliefs and intentions (Gilbert 1987, 1989; Tuomela 1991, 1992), which consist of the sharing of similar beliefs and intentions by an aggregate of agents.Social thinkers have been debating what exactly should be meant by “we-ness” and “we-intentions”, and where precisely the core of groups and teams resides, without reaching any satisfactory agreement.

However, these two ingredients are not in themselves sufficient to account for coalition formation. They do not allow the gap between the individual level of agency and the group or collective level to be filled. Or, when some attempt in this direction is made, as happened with the notion of strategic knowledge provided by game theorists, only a simplistic, reductionist view of social mental states could be obtained. What is lacking is a fine-grained modelling of sociality that allows for the variety of social beliefs and goals to be taken into account. Agency should not be se...

Table of contents

• Cover Page
• Title Page
• Copyright Page
• Preface
• Notes on contributors
• Chapter 1: Introduction Computer simulation for social theory
• Part I: The simulation of social theories
• Part II: The evolution and emergence of societies
• Part III: The foundations of social simulation
• Bibliography