Within Criminology, the process of crime displacement is usually explained by referring to the interaction of three types of agents: criminals, passersby, and guardians. Most existing simulation models of this process are agent-based. However, when the number of agents considered becomes large, population-based simulation has computational advantages over agent-based simulation. This paper presents both an agent-based and a population-based simulation model of crime displacement, and reports a comparative evaluation of the two models. In addition, an approach is put forward to analyse the behaviour of both models by means of formal techniques. 1.

Abstract. A classical debate in economics addresses the advantages and drawbacks of modelling from a macroeconomics perspective as opposed to modelling from a microeconomics perspective. Form a micro-perspective psychological aspects at an individual level can be taken into account in a differentiated manner. Within computer science and AI, a similar debate exists about the differences between agent-based and population-based modelling. This paper aligns both debates by exploring the differences and commonalities between population-based and agent-based modelling in economical context. A case study is performed on the interplay between individual greed as a psychological concept and global economical concepts. For this case, both agent-based and population-based simulations are performed. It is shown that under certain conditions the two approaches show similar results. 1

This paper reports a comparative evaluation of population-based simulation in comparison to agent-based simulation for different numbers of agents. Population-based simulation, such as for example in the classical approaches to predator-prey modelling and modelling of epidemics, has computational advantages over agent-based modelling with larger numbers of agents. Therefore the latter approaches can be considered useful only when the results are expected to deviate from the results of populationbased simulation, and are considered more realistic. However, there is sometimes also a silent assumption that for larger numbers of agents, agent-based simulations approximate population-based simulations, which would indicate that agentbased simulation just can be replaced by population-based simulation. The paper evaluates this assumption by a detailed comparative case study in epidemics. 1.

sustainable development.

Within Criminology, the process of crime displacement is usually explained by referring to the interaction of three types of agents: criminals, passersby, and guardians. Most existing simulation models of this process are agent-based. However, when the number of agents considered becomes large, population-based simulation has computational advantages over agent-based simulation. This paper presents both an agent-based and a population-based simulation model of crime displacement, and reports a comparative evaluation of the two models. In addition, an approach is put forward to analyse the behaviour of both models by means of formal techniques. 1.

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Abstract. Central research questions addressed within Criminology are how the geographical displacement of crime can be understood, explained, and predicted. The process of crime displacement is usually explained by referring to the interaction of three types of agents: criminals, passers-by, and guardians. Most existing simulation models of this process take a ‘local’ perspective, i.e., they are agent-based. However, when the number of agents considered becomes large, more ‘global’ approaches, such as population-based simulation have computational advantages over agent-based simulation. This article presents both an agent-based and a population-based simulation model of crime displacement, and reports a comparative evaluation of the two models. In addition, an approach is put forward to analyse the behaviour of both models by means of formal techniques. The results suggest that under certain conditions, population-based models approximate agent-based models, at least in the domain under investigation. Keywords: Agent-Based Modelling, Population-Based Modelling, Crime, Simulation. 1.

In her target article, Webb contrasts two kinds of models, which she calls animal and animat models, and argues that the latter are unfairly held to less strict standards of scientific relevance than the former, particularly

Terrorist organizations change over time because of processes such as recruitment and training as well as counter-terrorism (CT) measures, but the effects of these processes are typically studied qualitatively and in separation from each other. Seeking a more quantitative and integrated understanding, we constructed a simple dynamic model where equations describe how these processes change an organization’s membership. Analysis of the model yields a number of intuitive as well as novel findings. Most importantly it becomes possible to predict whether counter-terrorism measures would be sufficient to defeat the organization. Furthermore, we can prove in general that an organization would collapse if its strength and its pool of foot soldiers decline simultaneously. In contrast, a simultaneous decline in its strength and its pool of leaders is often insufficient and short-termed. These results and other like them demonstrate the great potential of dynamic models for informing terrorism scholarship and counter-terrorism policy making. 1

Nicodemi and Prisco recently proposed a model for X-chromosome inactivation in mammals, explaining this phenomenon in terms of a spontaneous symmetry-breaking mechanism [Phys. Rev. Lett. 99 (2007), 108104]. Here we provide a mean-field version of their model. 1