The aim of this paper is to assist researchers in understanding the dynamics of simulation models that have been implemented and can be run in a computer, i.e. computer models. To do that, we start by explaining (a) that computer models are just input-output functions, (b) that every computer model can be re-implemented in many different formalisms (in particular in most programming languages), leading to alternative representations of the same input-output relation, and (c) that many computer models in the social simulation literature can be usefully represented as time-homogeneous Markov chains. Then we argue that analysing a computer model as a Markov chain can make apparent many features of the model that were not so evident before conducting such analysis. To prove this point, we present the main concepts needed to conduct a formal analysis of any time-homogeneous Markov chain, and we illustrate the usefulness of these concepts by analysing 10 well-known models in the social simulation literature as Markov chains. These models are: * Schelling's (1971) model of spatial segregation * Epstein and Axtell's (1996) Sugarscape * Miller and Page's (2004) standing ovation model * Arthur's (1989) model of competing technologies * Axelrod's (1986) metanorms models * Takahashi's (2000) model of generalized exchange * Axelrod's (1997) model of dissemination of culture * Kinnaird's (1946) truels * Axelrod and Bennett's (1993) model of competing bimodal coalitions * Joyce et al.'s (2006) model of conditional association In particular, we explain how to characterise the transient and the asymptotic dynamics of these computer models and, where appropriate, how to assess the stochastic stability of their absorbing states. In all cases, the analysis conducted using the theory of Markov chains has yielded useful insights about the dynamics of the computer model under study.

Engineers have traditionally been trained to solve design problems without regard to the potentially competing interests of other designers. But just as technology strategists are increasingly drawn into the technical minutiae of product development decisions, engineers are increasingly exposed to the competitive forces that shape their requirements and the resources at their disposal. I propose that architectural strategy—the application of strategic thinking to system design problems—should therefore be integrated with the theory and practice of engineering design. This task is made difficult by the fact that system architects and business strategists focus on problems at different levels of abstraction that lend themselves to different kinds of tools. My dissertation research bridges this gap in three ways. First, I develop a representation scheme, based on design structure networks, that allows economic relationships among system components to be expressed in a graphical language that is both visually intuitive and formally rigorous. Second, I model the evolution of designs as a sequence of “moves ” by designers that see and seek economic value. Although these system

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A new approach is introduced, whereby patently spatial simulation methodology is applied to simulating discrete, dynamic, and action-oriented spatial systems, combining cellular automata and multi-agent systems in a spatial context. In this paper, we propose a minimal prototype for integrating GIS and geosimulation into what we term Geographic Automata Systems (GAS), the latter sufficient for the formalization of the majority of abstract and real-world high-resolution models. A software environment for urban simulation, which directly implements GAS formalism, is briefly described and an example of its application is provided. 1.

The objectives of this paper are to define and classify different types of errors and artefacts that can appear in the process of developing an agent-based model, and to propose activities aimed at avoiding them during the model construction and testing phases. To do this in a structured way, we review the main concepts of the process of developing such a model – establishing a general framework that summarises the process of designing, implementing, and using agent-based models. Within this framework we identify the various stages where different types of errors and artefacts may appear. Finally we propose activities that could be used to detect (and hence eliminate) each type of error or artefact.

Abstract — The work being introduced is aimed at supporting the crucial activity of deciding what is to be done, and when, within an industrial, applied, real-world situation. More specifically: matching assorted tasks to applicable production units, and deciding the priority every job is to be given. The problem, common to many different industries, arises when a considerable amount of different articles must be produced on a relatively small number of reconfigurable units. Similar issues have a strong impact on an essential concern, eminently in the textile industrial domain: satisfying the always-in-a-rush customers, while keeping accessory production costs (set-up costs, machinery cleaning costs,...) under control, keeping at a minimum the losses related to wasteful resource-management practices, due to “under pressure ” decision making. Given the real-world situation, where human planners tend to

A. Smajgl 56 Modelling evolving rules for the use of common-pool resources in an agent-based model E. Johnston, Y. Kim and

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We propose that the emergent phenomenon know as “desakota”, the rapid urbanization of densely populated rural populations in the newly developed world, particularly China, can be simulated using agent-based models which combine both local and global features. We argue that deskota represents a surprising and unusual form of urbanization wellmatched to processes of land development that are driven from the bottom up but moderated by the higher-level macro economy. We develop a simple logic which links local household reform to global urban reform, translating these ideas into a model structure which reflects these two scales. Our model first determines the rate of growth of different spatial aggregates using linear statistical analysis. It then allocates this growth to the local level using developer agents who determine the transformation or mutation of rural households to urban pursuits based on local land costs, accessibilities, and growth management practices. The model is applied to

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