Plug'n Simulate

Himmelspach, Jan; Uhrmacher, Adelinde M.

doi:10.1109/anss.2007.34

Cited by 110 publications

(73 citation statements)

References 7 publications

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“…JAMES II est doté de la technologie m&s la plus avancée, et offre donc une forte puissance de calcul, indispen-sable pour des simulations à grande échelle. JAMES II se base sur un concept de « plug'n simulate » (Himmelspach et Uhrmacher, 2007). Ce concept permet au développeur d'intégrer de nouveaux composants (tels que des plug-ins) dans une structure existante.…”

Section: Resultsunclassified

La microsimulation dans les projections de population

Willekens

2012

cqd

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Dans cet article, nous présentons un modèle de microsimulation qui permet de générer des histoires de vie individuelles cohérentes avec les projections de cohortes obtenues par le modèle multi-états à composantes de cohorte. Le modèle de microsimulation peut prendre en compte des différences individuelles que le modèle à composantes de cohorte ne peut traiter. Ces différences sont dues à une association de facteurs systématiques et de facteurs aléatoires. Nous nous intéressons principalement aux facteurs aléatoires. Le modèle de microsimulation utilise un modèle de survie de cohorte multi-états à temps continu et des tirages aléatoires à partir de distributions de temps d’attente exponentielles par morceaux. Si on prend comme paramètres des distributions les intensités de transition du modèle de survie de cohorte, les histoires de vie individuelles sont cohérentes avec les projections de cohorte. Les histoires de vie apportent un éclairage sur la dynamique démographique que la méthode des composantes ne peut proposer.A dynamic microsimulation model is presented to generate individual life histories that are consistent with cohort projections produced by the (multistate) cohort-component model. The microsimulation model can incorporate individual differences the cohort-component model cannot handle. The differences are outcomes of systematic factors and random factors. The paper focuses on random factors. The microsimulation model uses a continuous-time multistate cohort-survival model and random draws from piecewise exponential waiting time distributions. If the parameters of the distributions are the transition intensities of the cohort-survival model, then the individual life histories are consistent with cohort projections. The life histories provide insights in population dynamics that cannot be obtained from the cohort-component model

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Section: Resultsunclassified

La microsimulation dans les projections de population

Willekens

2012

cqd

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“…The pre-and post-processor are written in R (R Development Core Team 2012). The MicMac-Core (Zinn et al 2009) is implemented as a plug-in in JAMES II (JAva based Multipurpose Environment for Simulation), which is a multi-agent system and simulation platform, see Himmelspach and Uhrmacher (2007). In Section 5.5 we will use this microsimulation tool to illustrate and supplement analytical results of the model that was presented in Section 2.…”

Section: Microsimulation From Multi-state Modelsmentioning

confidence: 99%

Investigating healthy life expectancy using a multi-state model in the presence of missing data and misclassification

Hout¹,

Ogurtsova²,

Gampe³

et al. 2014

DemRes

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“…For each platform we tried to download the source or executable code and we tried to compile it and test it with the provided examples and templates. Some of the platforms cannot be included in our study because there is no available source code or downloadable executable (MACE3J [15], JAMES [17], SWAGES [24]), or because only a demonstration version is available (PDES-MAS [22,27]), or because there is a real lack of documentation (Ecolab [26]). It was thus not possible to build a new model in these platforms and thus to assess their parallel characteristics and performance.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Parallel and Distributed Multi-Agent Systems

Rousset

Herrmann

Lang

et al. 2014

Lecture Notes in Computer Science

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Simulation has become an indispensable tool for researchers to explore systems without having recourse to real experiments. Depending on the characteristics of the modeled system, methods used to represent the system may vary. Multi-agent systems are, thus, often used to model and simulate complex systems. Whatever modeling type used, increasing the size and the precision of the model increases the amount of computation, requiring the use of parallel systems when it becomes too large. In this paper, we focus on parallel platforms that support multi-agent simulations. Our contribution is a survey on existing platforms and their evaluation in the context of high performance computing. We present a qualitative analysis, mainly based on platform properties, then a performance comparison using the same agent model implemented on each platform.

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Plug'n Simulate

Cited by 110 publications

References 7 publications

La microsimulation dans les projections de population

La microsimulation dans les projections de population

Investigating healthy life expectancy using a multi-state model in the presence of missing data and misclassification

A Survey on Parallel and Distributed Multi-Agent Systems

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