This repository offers access to CapMod, an agent-based model aimed at simulating an artificial society and generating survey-like data to analyse the performance of capability estimators.
We highly recommend users to read our article CapMod: A Simulated Society to Evaluate Empirical Estimators of Capabilities to become familiar with the model and its intended use. For more details, we recommend the supplemental material of the article, which includes the article (before formatting by publisher) and the ODD-protocol of the model.
The primary goal of CapMod is to provide new ways to test empirical approaches to estimate capabilities. In this sense, we offer different possibilities to use the model or the generated data to test and compare empirical models. The simplest way to make use of CapMod is to use the data generated for and used in our JDHC article. If you wish to adjust the parameters of the model or change elements of the model, you have to download the source code and install the model on your computer.
We strongly recommend starting with this option. In the folder data you find different datasets generated by the model. Currently we have uploaded the data that were generated for the JDHC article, which include 25 different random seeds, resulting in a total of 25 sets of datasets. Read the readme file carefully before using the data.
If you want to run and/or adapt the model, you will have to install the model on your local machine. Before downloading the model, you should install RepastSimphony. A quick guide to Repast can be found here.
In the folder model you can find different versions of the model. To simplify the installation, we simply provide the full project folder of the RePast-project. Currently we have the following models:
- v1_0_JHDC_Article: This is the model we have used to produce the data published in the JDHC article.
- Florian Chávez-Juárez & Jaya Krishnakumar (2020) CapMod: A Simulated Society to Evaluate Empirical Estimators of Capabilities, Journal of Human Development and Capabilities, DOI: 10.1080/19452829.2020.1850659