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The paper describes results from a simulation model of rural land use, focusing on how the relative advantages of imitative and non-imitative approaches to land use selection change under diierent circumstances. It is shown that the success ofìmitation' depends in quite complex ways on the type of imitation used, the strategies of other agents with which(More)
This article describes three agent-based social simulation models in the area of land-use change using a model documentation protocol, ODD, from the ecological literature. Our goal is to evaluate how well fitted it is to social simulations and how successful it might be in increasing communication between individual-and agent-based modellers. Such shared(More)
Reinforcement learners tend to repeat actions that led to satisfactory outcomes in the past, and avoid choices that resulted in unsatisfactory experiences. This behavior is one of the most widespread adaptation mechanisms in nature. In this paper we fully characterize the dynamics of one of the best known stochastic models of reinforcement learning [Bush,(More)
In this paper we explore the use of proposed semantic grid standards and methodology through deployment of a land use modelling service. The FEARLUS-G service architecture is presented which allows large scale simulation experiments to be distributed over the grid. We also discuss ontology support for simulation parameters, hypotheses and results that(More)
Bistromathics … is … a revolutionary new way of understanding the behaviour of numbers. … Numbers written on restaurant bill pads within the confines of restaurants do not follow the same mathematical laws as numbers written on any other pieces of paper in any other part of the Universe. 1 Introduction This paper will explore the effects of errors in(More)
This paper provides a framework that highlights the features of computer models that make them especially vulnerable to floating-point errors, and suggests ways in which the impact of such errors can be mitigated. We focus on small floating-point errors because these are most likely to occur, whilst still potentially having a major influence on the outcome(More)