![]() If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. You can help adding them by using this form. We have no bibliographic references for this item. It also allows you to accept potential citations to this item that we are uncertain about. This allows to link your profile to this item. If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. See general information about how to correct material in RePEc.įor technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact. ![]() When requesting a correction, please mention this item's handle: RePEc:jas:jasssj:2013-150-2. You can help correct errors and omissions. Suggested CitationĪll material on this site has been provided by the respective publishers and authors. 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. 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. The Supplementary Material includes full, adaptable code samples for using the presented methods with R and NetLogo. 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. We briefly introduce each method and provide references for further reading. 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. To facilitate parameter estimation and sensitivity analysis for agent-based modellers, we show how to use a suite of important established methods. Both steps of the model development cycle require massive repetitions of simulation runs with varying parameter values. These insights foster the understanding of models and their use for theory development and applications. 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. Furthermore, sensitivity analysis is an important part of the development and analysis of any simulation model. 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.
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