Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R'
By Jan C. Thiele, Winfried Kurth, Volker Grimm. Journal of Artificial Societies and Social Simulation, 2014
312 citations recorded by the source corpus.
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.
Subfields: Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/f4a6846c7d5cdf07286abff7aa307352eb0b5b62
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In Management Science and Operations Research
Citations in the corpus
- Predicting monarch butterfly (Danaus plexippus) movement and egg-laying with a spatially-explicit agent-based model: The role of monarch perceptual range and spatial memory (2018, methodology): “Model parameterization and calibration involves optimizing parameters such that the model gives more realistic results (Thiele et al., 2014).”
- The Complexities of Agent-Based Modeling Output Analysis (2015, methodology): “Currently, the ABM practice of SA has entailed one or more of the following methods: one-parameter-at-a-time, elementary effects, standardized regression coefficients, meta-modelling, and variance-based decomposition (Thiele et al. 2014; ten Broeke et al. 2014).”
- Everything you need to know about agent-based modelling and simulation (2016, methodology): “In addition, new statistical methods and existing methods implemented for large-scale application of data analytics are needed to support agent-based modelling requirements (Thiele et al , 2014; ten Broeke et al , 2016).”