Agent-based models are increasingly used to address questions regarding real-world phenomena and mechanisms; therefore, the calibration of model parameters to certain data sets and patterns is often needed. Furthermore, sensitivity analysis is an important part of the development and analysis of any simulation model. By exploring the sensitivity of model output to changes in parameters, we learn about the relative importance of the various mechanisms represented in the model and how robust the model output is to parameter uncertainty. These insights foster the understanding of models and their use for theory development and applications. Both steps of the model development cycle require massive repetitions of simulation runs with varying parameter values. To facilitate parameter estimation and sensitivity analysis for agent-based modellers, we show how to use a suite of important established methods. Because NetLogo and R are widely used in agent-based modelling and for statistical analyses, we use a simple model implemented in NetLogo as an example, packages in R that implement the respective methods, and the RNetLogo package, which links R and NetLogo. We briefly introduce each method and provide references for further reading. We then list the packages in R that may be used for implementing the methods, provide short code examples demonstrating how the methods can be applied in R, and present and discuss the corresponding outputs. The Supplementary Material includes full, adaptable code samples for using the presented methods with R and NetLogo. Our overall aim is to make agent-based modellers aware of existing methods and tools for parameter estimation and sensitivity analysis and to provide accessible tools for using these methods. In this way, we hope to contribute to establishing an advanced culture of relating agent-based models to data and patterns observed in real systems and to foster rigorous and structured analyses of agent-based models.

Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and R

https://hal.archives-ouvertes.fr/hal-00840744/document

OpenMOLE, a workflow engine specifically tailored for the distributed exploration of simulation models

The RNetLogo package delivers an interface to embed the agent-based modeling platform NetLogo into the R environment with headless (no graphical user interface) or interactive GUI mode. It provides functions to load models, execute commands, push values, and to get values from NetLogo reporters. Such a seamless integration of a widely used agent-based modeling platform with a well-known statistical computing and graphics environment opens up various possibilities. For example, it enables the modeler to design simulation experiments, store simulation results, and analyze simulation output in a more systematic way. It can therefore help close the gaps in agent-based modeling regarding standards of description and analysis. After a short overview of the agent-based modeling approach and the software used here, the paper delivers a step-by-step introduction to the usage of the RNetLogo package by examples.

https://www.jstatsoft.org/htaccess.php?volume=58&type=i&issue=02&paper=true

R Marries NetLogo: Introduction to the RNetLogo Package

The aim of this review is twofold. Firstly, we present the state of the art in dynamic modelling and model-based design, optimisation and control of food systems. The need for nonlinear, dynamic, multi-physics and multi-scale representations of food systems is established. Current difficulties in building such models are reviewed: incomplete, piecewise available knowledge, spread out among different disciplines (physics, chemistry, biology and consumer science) and contributors (scientists, experts, process operators, process managers), scarcity, uncertainty and high cost of measured data, complexity of phenomena and intricacy of time and space scales. Secondly, we concentrate on the opportunities offered by the complex systems science to cope with the difficulties faced by food science and engineering. Newly developed techniques such as model-based viability analysis, optimisation, dynamic Bayesian networks etc. are shown to be relevant and promising for design and optimisation of foods and food processes based on consumer needs and expectations.

/pdf/modelling-and-analysis-of-complex-food-systems-state-of-the-3x0g4poe43.pdf

Modelling and analysis of complex food systems: State of the art and new trends

L'evolution des villes est-elle le resultat de leurs multiples interactions ? C'est sur ce postulat que s'appuie la theorie evolutive urbaine (Pumain, 2000) pour analyser les processus de croissance des villes. Cette these, realisee dans un contexte inter-disciplinaire, vise a evaluer la validite d'une telle hypothese par la simulation informatique. Pour mener a bien ce projet, les savoirs accumules sur la dynamique des systemes de villes sont d'abord rassembles afin d'en extraire les grandes caracteristiques, synthetisees sous la forme de dix fait stylises majeurs. Deux modeles de simulation, SimpopLocal et SimpopNet, sont ensuite construits, documentes de maniere standardisee, et explores de maniere systematique. Ces modeles interrogent chacun un aspect precis de la theorie evolutive urbaine: la nature des interactions interurbaines pour le premier et leur support pour le second. Les besoins associes a leur necessaire evaluation ont guide la conception et la mise en œuvre de deux protocoles d'explorations systematiques inedits: une methode de calibrage automatisee, et un protocole d'analyse de sensibilite qui evalue individuellement la contribution de chaque mecanisme aux comportements simules. Ces deux formes d'exploration confrontent de facon systematique les resultats de simulation avec les connaissances actuelles. Elles montrent que ces modeles sont capables de rendre compte de processus cles de la dynamique des systemes des villes et prouvent pour la premiere fois la necessite de mecanismes d'interactions interurbaines pour simuler des croissances proches des evolutions de systemes reels.

Modélisation de la dynamique des systèmes de peuplement : de SimpopLocal à SimpopNet

In this paper we present OpenMOLE, a scientific framework providing a virtualized runtime environment for distributed computing. Current distributed execution systems do not hide the hardware and software heterogeneity of computing and data resources whereas OpenMOLE provides generic services to develop distributed scientific algorithms independently from the execution environment architecture. OpenMOLE uses abstraction layers to delegate computing tasks with the same high level interface for the major underlying architectures: local processors, batch systems, computational grids, Internet computing and cloud computing. The file access abstraction layer is another key feature helping a generic usage of the computation power provided by grids and clusters. The OpenMOLE framework has been tested with the exploration of a bacterial biofilm simulation with an individual-based model.

Declarative task delegation in OpenMOLE

This article describes Simexplorer, a computer framework for managing simulation experiments and, to some extent, the scientific quality of the modelling process. An information system, included in the framework, insures the traceability of the experiments and their reproducibility and thus contributes to the modelling process quality management. Moreover, this information system provides facilities for sharing and exchanging components of experiment scenarios. The authors illustrate the use of the framework on a simple example of modelling process.

SimExplorer: Programming Experimental Designs on Models and Managing Quality of Modelling Process

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Declarative task delegation in OpenMOLE

SimExplorer: Programming Experimental Designs on Models and Managing Quality of Modelling Process