Nigel Gilbert

Agent-based land-use models: a review of applications

Agent-based modelling is an approach that has been receiving attention by the land use modelling community in recent years, mainly because it offers a way of incorporating the influence of human decision-making on land use in a mechanistic, formal, and spatially explicit way, taking into account social interaction, adaptation, and decision-making at different levels. Specific advantages of agent-based models include their ability to model individual decision-making entities and their interactions, to incorporate social processes and non-monetary influences on decision-making, and to dynamically link social and environmental processes. A number of such models are now beginning to appear—it is timely, therefore, to review the uses to which agent-based land use models have been put so far, and to discuss some of the relevant lessons learnt, also drawing on those from other areas of simulation modelling, in relation to future applications. In this paper, we review applications of agent-based land use models under the headings of (a) policy analysis and planning, (b) participatory modelling, (c) explaining spatial patterns of land use or settlement, (d) testing social science concepts and (e) explaining land use functions. The greatest use of such models so far has been by the research community as tools for organising knowledge from empirical studies, and for exploring theoretical aspects of particular systems. However, there is a need to demonstrate that such models are able to solve problems in the real world better than traditional modelling approaches. It is concluded that in terms of decision support, agent-based land-use models are probably more useful as research tools to develop an underlying knowledge base which can then be developed together with end-users into simple rules-of-thumb, rather than as operational decision support tools.

How to build and use agent-based models in social science

The use of computer simulation for building theoretical models in social science is introduced. It is proposed that agent-based models have potential as a “third way” of carrying out social science, in addition to argumentation and formalisation. With computer simulations, in contrast to other methods, it is possible to formalise complex theories about processes, carry out experiments and observe the occurrence of emergence. Some suggestions are offered about techniques for building agent-based models and for debugging them. A scheme for structuring a simulation program into agents, the environment and other parts for modifying and observing the agents is described. The article concludes with some references to modelling tools helpful for building computer simulations.

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.

A Simulation of the Structure of Academic Science

The contemporary structure of scientific activity, including the publication of papers in academic journals, citation behaviour, the clustering of research into specialties and so on has been intensively studied over the last fifty years. A number of quantitative relationships between aspects of the system have been observed. This paper reports on a simulation designed to see whether it is possible to reproduce the form of these observed relationships using a small number of simple assumptions. The simulation succeeds in generating a specialty structure with ‘areas’ of science displaying growth and decline. It also reproduces Lotkas Law concerning the distribution of citations among authors. The simulation suggests that it is possible to generate many of the quantitative features of the present structure of science and that one way of looking at scientific activity is as a system in which scientific papers generate further papers, with authors (scientists) playing a necessary but incidental role. The theoretical implications of these suggestions are briefly explored.

MAS and Social Simulation: A Suitable Sommitment

The goal of this introduction is to point out several similarities and differences between the research fields of multi-agent systems and social simulation. We show that these fields are complementary in several aspects, thus each one can benefit from results that emerge from the other. We finish the introduction by presenting and classifying the contributions in this volume.

Men: The Forgotten Carers

The extent to which men are the primary carers of infirm elderly people and the amount of support men carers receive from the statutory and voluntary services relative to women carers is examined using data from the 1980 General Household Survey. It is shown that men make a larger contribution to caring than is often recognised.

Synthesizing experiences: lessons to be learned from internet-mediated simulation games

This article draws on published evaluations of Internet-mediated (I-M) educational, business, and policy games to establish an inventory of lessons for future I-M games. These three types of I-M games have important concerns in common: objectives, role-play, synchronicity, game facilitation, and participant interaction. Lessons of design and implementation derived from these experiences are identified and explored. Special attention is given to the development of strategic I-M policy games because I-M gaming literature has tended to ignore them in comparison with educational and business games, and they seem to require more guidance and support in three main areas: structure, motivation, and interaction.

SIMULATING KNOWLEDGE-GENERATION AND DISTRIBUTION PROCESSES IN INNOVATION COLLABORATIONS AND NETWORKS

An agent-based simulation model representing a theory of the dynamic processes involved in innovation in modern knowledge-based industries is described. The agent-based approach allows the representation of heterogenous agents that have individual and varying stocks of knowledge. The simulation is able to model uncertainty, historical change, effect of failure on the agent population, and agent learning from experience, from individual research and from partners and collaborators. The aim of the simulation exercises is to show that the artificial innovation networks show certain characteristics they share with innovation networks in knowledge intensive industries and which are difficult to be integrated in traditional models of industrial economics.

Contexts of Scientific Discourse: Social Accounting in Experimental Papers

Almost all analysis in the sociology of science has involved attempts to describe scientists’ social actions and ‘technical’ beliefs. For example, much effort has been devoted to investigating whether scientists, in the course of their research, act in a detached, impersonal, universalistic manner and whether these forms of action are required for the regular production of valid scientific knowledge (1). Other investigators have sought to provide definitive descriptions of the ‘main features’ of particular scientists’ beliefs as a preliminary to explaining the beliefs as having been moulded by the actors’ socially derived interests (2). In recent years, however, there has been a growing although by no means widespread recognition that neither social action nor technical belief in science can be identified unequivocally for the purposes of sociological analysis (3). This is because it has become increasingly clear that different scientists can and do give quite divergent, yet equally plausible, accounts of the ‘same’ act or the ‘same’ belief; and that particular actors tend to alter their accounts of their own and of others’ actions and scientific ideas as they respond to new social situations.(4). As a result, some sociologists concerned with the study of scientists’ meaningful actions, as distinct from scientists’ ‘behaviour’, have come to see that meaning does not reside in the actions themselves but in the context-dependent procedures of social accounting whereby actions are interpreted.

On the Social Organisation of Organisations

This paper considers a range of theoretical approaches to the understanding of organisations and the implications these views have for the design of computer supported cooperative work systems. Organisations have often been seen as structures which can be divided into hierarchically ordered parts or as networks of informal relations. Organisational theorists have also considered organisations to resemble organisms with needs for survival in potentially hostile environments or as information processors, with decision-making as their most important characteristic. More recently, developments in the social sciences have suggested that radical reconceptualisations are necessary for the study of work settings. Consequently, these developments have attracted attention due to their potential to inform system design. This paper reviews some of these efforts and comments on some of the outstanding problems that have to be overcome if studies of everyday work settings are to inform the design of systems to support collaborative work.