Jim Doran

Simulating Societies: the computer simulation of social phenomena

Simulating societies: an introduction, Jim Doran and Nigel Gilbert simulating of complex organizational processes - a review of methods and their epistemological foundations, Ann C. Seror the evolution of technologies, Klaus G. Troitzsch simulating the emergence of social order from individual behaviour, Andrzej Nowak and Bibb Latane the architecture of society - stochastic simulation of urban movement, Alan Penn and Nick Dalton multi-agent simulation as a tool for studying emergent processes in societies, Alexis Drogoul and Jacques Ferber simulating fishermen society, F. Bousquet, C. Cambier, C. Mullon, P. Morand, J. Quensiere Simulating prehistoric hunter-gatherer societies, Steven Mithen the EOS project - modelling Upper Palaeolithic social change, Jim Doran, Mike Palmer, Nigel Gilbert, Paul Mellars learning to co-operate using cultural algorithms, Robert G. Reynolds the simulation of trade in oligopolistic markets Jose Castro Caldas and Helder Coelho mind is not enough - the precognitive bases of social interaction, Rosaria Conte and Cristiano Castelfranchi.

On cooperation in multi-agent systems

Cooperation is often presented as one of the key concepts which differentiates multi-agent systems from other related disciplines such as distributed computing, object-oriented systems, and expert systems. However, it is a concept whose precise usage in agent-based systems is at best unclear and at worst highly inconsistent. Given the centrality of the issue, and the different ideological viewpoints on the subject, this was a lively panel which dealt with the following main issues.

Agent-based modelling of ecosystems for sustainable resource managment

We present agent-based modelling and social simulation in particular application to ecosystem management. The steps in designing and building an agent-based model are discussed, as are the methodological problems typically encountered. Examples of relevant agent-based models are given. The task of integrated ecosystem management is considered and examples are given of agent-based modelling in this context. As a further illustration, consideration is given to a possible agent-based model of the Fraser River watershed in British Columbia, and to particular difficulties that it presents.

Systems theory, computer simulations and archaeology

Until comparatively recently, say fifteen years ago, interpretive archaeology was a discipline which made virtually no use of mathematical or scientific techniques and concepts. Few suggested that it should. It was hard to see how the complex and ill-structured problems facing archaeologists could be tackled other than by the direct application of their own experience and intelligence. The outlook has changed somewhat. Scientific techniques such as radiocarbon dating, pollen analysis, and geomagnetic surveying have provided new and valuable sources of information for the archaeologist. Mathematical techniques such as seriation analysis and factor analysis have been used to try to make objective, and more powerful, reasoning previously carried out on a purely intuitive basis. Most of these mathematical techniques are still experimental, and have yet to be established as standard archaeological tools. The electronic computer has been deeply involved in these developments. Archaeologists collect large quantities of data, and if numerical techniques are to be used at all then a computer is almost certain to be needed to carry through the calculations. Of course, the rapid increase over the last decade in the computing facilities generally available partially explains why these techniques are receiving ever more attention. Similar trends are apparent in allied subjects such as anthropology and social geography. In particular, location analysis has influenced archaeologists, with its emphasis on the study of all aspects of a population and its environment, and on the use of quantitative methods and models (Haggett I963). In the last few years some archaeologists have become aware of another subject which, it seems, they should study. This is systems theory, sometimes equated with cybernetics. Not only does systems theory itself involve abstract ideas a little difficult to grasp for those with a non-scientific background, but it brings in its train other subjects such as information theory and games theory which are highly mathematical in content, and yet which are nevertheless claimed to be of archaeological significance. An archaeologist who is aware of these developments finds himself, apparently, faced with the choice either of losing touch with some of the most interesting and important developments in his subject, or of embarking upon a course of study for which nothing in his training has prepared him. This paper is an attempt to help such an archaeologist understand what systems theory is, and what it has to offer. It is written from the standpoint of a computer scientist with a good working knowledge of the archaeologist and his problems.

Social simulation, agents and artificial societies

Computer simulation in support of the social sciences is far from new. But, with one or two exceptions, notably certain types of economic modeling, it has never become a major weapon in the social scientists armoury. However, in the last few years developments in intelligent agents and multiple agent systems have sparked a new interest in computer simulation in disciplines such as economics, anthropology and sociology. The key new opportunity is that something corresponding to the cognitive processes of members of a society may now be made explicit (in agents) in models. This enables studies to be made of the relationship between agent-level and society-level phenomena in a way not previously possible. However, important and interesting methodological difficulties arise, and it is these difficulties that are the main focus of the paper.

Can Agent-Based Modelling Really be Useful?

Agent-based modelling on a computer appears to have a special role to play in the development of social science and the formulation of social policy. It offers a means of discovering general and applicable social theory, and grounding it in precise assumptions and derivations, whilst addressing those elements of individual cognition that are central to human society. However, there are important questions to be asked and difficulties to be overcome in achieving this potential. What differentiates agent-based modelling from traditional computer modelling? What different types of agent-based models are there, and what are the structural relationships between them (if any)? Which model types should be used in which circumstances? If it is appropriate to use a complex model, for example one incorporating “deliberative” agents, how can it be validated? If it can only be validated in general terms, does this mean that we are forced into a “theory building” mode in which the focus of the investigation lies in the model’s properties? If so, what types of parameter space may a complex model have? How best can very large parameter spaces be explored? Some of these questions are here addressed and are illustrated by reference to recent agent-based models for the environment. A particular application is then considered in some detail: agent-based modelling of intervention strategies for integrated ecosystem management, especially management of the Fraser River watershed in British Columbia.

The MCS multi-agent testbed: developments and experiments

This paper describes the MCS multiple agent software testbed which has been developed as a research tool in the University of Essex, Department of Computer Science. Recent enhancements to the testbed are noted, and experiments using it are briefly reported and discussed. The experiments particularly concern the relationship between ’situated’ action and action resulting from ’predictive’ or ’strategic’ planning in multi-agent communities, and the impact of changing the content of agents’ social models on their behaviour and collective effectiveness. The experiments are partly oriented to the study of human social systems in prehistory.

Foreknowledge in Artificial Societies

This paper discusses and illustrates how artificial societies created within computers may be used to perform experiments which address significant issues in the philosophy of time and ktowledge. It is specifically targeted at the concept of foreknowledge, an agent’s unmediated and accurate (if partial) awareness of future facts or events. It asks the following preliminary questions as they may be posed in the context of an artificial society: can agents in a deterministic world have choice? is it possible for agents to have foreknowledge? can agents with foreknowledge have choice? Then the main question is posed: in which circumstances is foreknowledge beneficial or detrimental to a society of (rational) agents?

Modelling a Typical Guerrilla War

An agent-based model of a typical guerrilla war, the Iruba model, has been designed and implemented based upon published descriptions and theories of guerrilla warfare. Experimental results have been obtained with the model and conclusions drawn. A core feedback loop is detected. The possibility of using the Iruba model to predict the outcomes of specific guerrilla wars is discussed and it is suggested that such predictive models are feasible and are potentially useful tools for peacemakers

Formal methods and archaeological theory: A perspective

Abstract This paper briefly reviews some of the major developments in the use of formal methods in European and American archaeology over the past decade, looks at the relationship of these methods to archaeological theory and to the controversies concerning it, and then suggests ways in which some particular formal concepts and methods deriving from artificial intelligence research can contribute to the formulation and use of archaeological theory in the future.