Abstract. In this paper we try to replicate the simulation results reported by Axelrod [1] in an influential paper on the evolution of social norms. Our study shows that Axelrod’s results are not as reliable as one would desire. We can obtain the opposite results by running the model for longer, by slightly modifying some of the parameters, or by changing some arbitrary assumptions in the model. This re-implementation exercise illustrates the importance of running stochastic simulations several times, of exploring the parameter space adequately, of complementing simulation with analytical work, and of being aware of the scope of our simulation models.

Traditional (i.e. analytical) modelling practices in the social sciences rely on a very well established, although implicit, methodological protocol, both with respect to the way models are presented and to the kinds of analysis that are performed. Unfortunately, computer-simulated models often lack such a reference to an accepted methodological standard. This is one of the main reasons for the scepticism among mainstream social scientists that results in low acceptance of papers with agent-based methodology in the top journals. We identify some methodological pitfalls that, according to us, are common in papers employing agent-based simulations, and propose appropriate solutions. We discuss each issue with reference to a general characterization of dynamic micro models, which encompasses both analytical and simulation models. In the way, we also clarify some confusing terminology. We then propose a three-stage process that could lead to the establishment of methodological standards in social and economic simulations.

Abstract. This paper describes a cross-cultural research project on the relation between how people conceptualize nature (their mental models) and how they act in it. Mental models of nature differ dramatically among and within populations living in the same area and engaged in more or less the same activities. This has novel implications for environmental decision making and management, including dealing with commons problems. Our research also offers a distinct perspective on models of culture, and a unified approach to the study of culture and cognition. We argue that cultural transmission and formation does not consist primarily in shared rules or norms, but in complex distributions of causally-connected representations across minds in interaction with the environment. The cultural stability and diversity of these representations often derives from rich, biologically-prepared mental mechanisms that limit variation to readily transmissible psychological forms. This framework addresses a series of methodological issues, such as the limitations of conceiving culture to be a well-defined system or bounded entity, an independent variable, or an internalized component of minds. 2 I. Introduction.

A novel approach to automata-based modeling for spatial systems is described: geographic automata and Geographic Automata Systems. We detail a framework that takes advantage of the formalism of automata theory and GI Science to unite cellular automata and multi-agent systems techniques, and provides a spatial approach to bottomup modeling of complex geographic systems that are comprised of infrastructure and human objects. The suitability of the framework is also discussed with reference to existing cellular automata and multi-agent systems models used in urban studies. Practical implementation of the framework is illustrated with reference to an object-based urban simulation environment and implementation of a popular socio-spatial segregation model.

Abstract. When building simulations of complex systems the task of validation is often overlooked. Validation helps provide confidence in the simulation by exploring the link between the models that we build and the real complex system. We investigate software engineering validation techniques from outside the area of complex systems to assess their applicability for the types of simulation we build. We then provide an example of how such techniques can be applied to a complex systems simulation we are building of cells migrating through the walls of blood vessels. We suggest that explicitly stating the modelling and simulation assumptions we make is key to the process of validation. Concluding, we highlight a possible process for validating complex systems that explicitly incorporates environmental aspects.

Scientists have increasingly employed computer models in their work. Recent years have seen a proliferation of agent-based models in the natural and social sciences. But with the exception of a few "classic" models, most of these models have never been replicated by anyone but the original developer. As replication is a critical component of the scientific method and a core practice of scientists, we argue herein for an increased practice of replication in the agent- based modeling community, and for widespread discussion of the issues surrounding replication. We begin by clarifying the concept of replication as it applies to ABM. Furthermore we argue that replication may have even greater benefits when applied to computational models than when applied to physical experiments. Replication of computational models affects model verification and validation and fosters shared understanding about modeling decisions. To facilitate replication, we must create standards for both how to replicate models and how to evaluate the replication. In this paper, we present a case study of our own attempt to replicate a classic agent-based model. We begin by describing an agent-based model from political science that was developed by Axelrod and Hammond. We then detail our effort to replicate that model and the challenges that arose in recreating the model and in determining if the replication was successful. We conclude this paper by discussing issues for (1) researchers attempting to replicate models and (2) researchers developing models in order to facilitate the replication of their results.

As part of research towards the CoSMoS unified infrastructure for modelling and simulating complex systems, we review uses of definitional and descriptive models in natural science and computing, and existing integrated platforms. From these, we identify requirements for engineering models of complex systems, and consider how some of the requirements could be met, using state-of-the-art model management and a mobile, process-oriented computing paradigm.

Geographic simulation is concerned with automata-based methodologies for 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 paradigm for integrating GIS and geosimulation into what we term Geographic Automata Systems (GAS), the latter fusing the two into full-blown, symbiotic systems. 1.

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Suburban sprawl, a relatively recent phenomenon, is among the most important urban policy issues facing contemporary cities. To date, a well-accepted rationale has not been settled on for explaining and managing the causes of sprawl. Our contention is that consideration of geography is essential—that geographical explanations offer much potential in informing the debate about sprawl. Similarly, spatial simulation could support sprawlrelated research, offering what-if experimentation environments for exploring issues relating to the phenomenon. Sprawling cities may be considered as complex adaptive systems, and this warrants use of methodology that can accommodate the space-time dynamics of many interacting entities. Automata tools are well-suited to representation of such systems, but could be better formulated to capture the uniquely geographical traits of phenomena such as sprawl. By means of illustrating this point, the development of a model for simulating the geographic dynamics of suburban sprawl is discussed. The model is formulated using geographic automata and is used to develop three sprawl simulations. The implications of those applications are discussed in the context of exploring geographic explanations of sprawl formation and the potential for managing sprawl by geographic means. Key Words: cellular automata, geosimulation, GIScience, multiagent systems, suburban sprawl. Urban systems are evolving and emerging in surprising ways. This is particularly true in the