4  Methods

You are reading the work-in-progress of this report. This chapter is currently a dumping ground for ideas, and I don’t recommend reading it.

Original method (project)

Agent-based modeling will be employed to understand and predict system and agent behaviors under various conditions. A conceptual model will be constructed based on a literature review, outlining key agents and their relationships, along with other relevant parameters of interest. The agents will be Brazilian municipalities and the children within each municipality. The environment in which agents interact will be defined by the climatic conditions of each municipality. Parameters such as SUS coverage in the municipality, children’s dietary intake, HDI of the municipality, average income, and other characteristics of children and municipalities will be used to understand relationships among the main variables of interest. Mathematical models will be used to represent agent behavior, interaction rules, and system dynamics. The modeling will be implemented in Python, R, or NetLogo. Finally, the main findings will be summarized in a management platform developed in Excel or Power BI.

4.1 Framework

The agent-based model will be build and executed using the NetLogo framework (Wilensky, 1999). Data analysis will be performed using the R programming language (R Core Team, 2024) in conjunction with the nlrx R package (Salecker et al., 2019)).

4.2 Literature and software review

A systematic quantitative literature review (Pickering & Byrne, 2014) will be conducted to identify key studies relevant to this research topic. The review will focus on bibliometrics analysis of the studies and describe the mechanics of models similar to the research problem.

4.3 Model details

We will the adopt a generative, phenomenon-based, top-down approach to model building. This approach involves understanding emergent phenomena and integrating related components and interactions. The model aims to replicate these phenomena, generating data for comparison with empirical observations.

The ODD protocol (Overview, Design concepts, and Details) (Grimm, 2020) will be utilized to comprehensively describe the model. Detailed protocol specifics can be found in the Model description section.

See:

• https://www.comses.net/resources/standards/.
• https://jasss.soc.surrey.ac.uk/23/2/7/S2-ODD.pdf
• https://jasss.soc.surrey.ac.uk/23/2/7/S3-ODD.pdf.

4.4 Model analysis

The analysis will be based on the TRACE (“TRAnsparent and Comprehensive model Evaluation”) guidelines (Augusiak et al., 2014; Grimm et al., 2014; Schmolke et al., 2010).

See:

• https://www.comses.net/resources/standards/.
• https://jasss.soc.surrey.ac.uk/23/2/7/S6-ODD.pdf#page=4.23.

4.5 Reproducibility practices

The project will adhere to best practices for reproducibility in computational research.

The research compendium will be hosted on the Open Science Framework (OSF).

The codebase will be versioned using Git and hosted on GitHub, with a well-organized directory structure (see Marwick et al., 2018) and comprehensive documentation to facilitate reproducibility.

Dependencies will be managed using the renv package (Ushey & Wickham, n.d.) to ensure the R environment can be easily restored.

The Quarto publishing system (Allaire et al., n.d.) will be utilized to generate a fully reproducible report.

Additionally, the workflow will be managed using the targets package (Landau, 2021) to guarantee reproducibility of the analysis.

4.6 Data management

A data plan will be developed on DMPTool following the Digital Curation Centre template to ensure data quality and reproducibility.

4.7 Licensing

The code will released under the MIT license and the documents under the Creative Commons Attribution 4.0 International license.

4.8 Ethics information

This work will utilize publicly available data, and no human subjects will be involved. Therefore, no ethics approval is required.

Allaire, J. J., Teague, C., Xie, Y., & Dervieux, C. (n.d.). Quarto [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.5960048

Augusiak, J., Van den Brink, P. J., & Grimm, V. (2014). Merging validation and evaluation of ecological models to “evaludation”: A review of terminology and a practical approach. Ecological Modelling, 280, 117–128. https://doi.org/10.1016/j.ecolmodel.2013.11.009

Grimm, V. (2020). The ODD protocol: An update with guidance to support wider and more consistent use. Ecological Modelling, 428, 109105. https://doi.org/10.1016/j.ecolmodel.2020.109105

Grimm, V., Augusiak, J., Focks, A., Frank, B. M., Gabsi, F., Johnston, A. S. A., Liu, C., Martin, B. T., Meli, M., Radchuk, V., Thorbek, P., & Railsback, S. F. (2014). Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE. Ecological Modelling, 280, 129–139. https://doi.org/10.1016/j.ecolmodel.2014.01.018

Landau, W. (2021). The targets R package: A dynamic Make-like function-oriented pipeline toolkit for reproducibility and high-performance computing. Journal of Open Source Software, 6(57), 2959. https://doi.org/10.21105/joss.02959

Marwick, B., Boettiger, C., & Mullen, L. (2018). Packaging data analytical work reproducibly using R (and friends). The American Statistician, 72(1), 80–88. https://doi.org/10.1080/00031305.2017.1375986

Pickering, C., & Byrne, J. (2014). The benefits of publishing systematic quantitative literature reviews for PhD candidates and other early-career researchers. Higher Education Research & Development, 33(3), 534–548. https://doi.org/10.1080/07294360.2013.841651

R Core Team. (2024). R: A language and environment for statistical computing [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org

Salecker, J., Sciaini, M., Meyer, K. M., & Wiegand, K. (2019). The nlrx r package: A next-generation framework for reproducible NetLogo model analyses. Methods in Ecology and Evolution, 10(11), 1854–1863. https://doi.org/10.1111/2041-210X.13286

Schmolke, A., Thorbek, P., DeAngelis, D. L., & Grimm, V. (2010). Ecological models supporting environmental decision making: A strategy for the future. Trends in Ecology & Evolution, 25(8), 479–486. https://doi.org/10.1016/j.tree.2010.05.001

Ushey, K., & Wickham, H. (n.d.). Renv: Project environments [Computer software]. https://doi.org/10.32614/CRAN.package.renv

Wilensky, U. (1999). NetLogo [Computer software]. Center for Connected Learning and Computer-Based Modeling, Northwestern University. http://ccl.northwestern.edu/netlogo/