Thanks for your question, it is very relevant.
First I would like to clarify that despite motivating the presentation with empirical examples, we are developing a general model to capture the main entities and dynamics involved in extortion and violent activities in the context of civil wars. Thus, the model is supposed to be configurable and able to represent different civil war scenarios.
Because we intend to use the model to analyze the Somalian civil war, we will need to connect the model with empirical data. We plan to use empirical data for two purposes: (1) to calibrate the input parameters and (2) to evaluate the performance by fitting it with aggregate statistics of the model.
To calibrate the model, we will use some demographic and economic indicator to define the size of the economy and number of economic actors (i.e. Enterprises). Moreover, these indicators will allow us to develop a distribution function that will generate the Enterprises’ income during the simulation. These indicators will probably be extracted from datasets available at the World Bank [https://data.worldbank.org/country/somalia].
The calibration of the rebel groups will be more challenging as there are little or no information about them. Several of their behaviors will be based on theoretical studies in the fields of conflict and international relations instead of empirical data.
However, we will use data from Uppsala Conflict Data Program (UCDP) [https://www.pcr.uu.se/research/UCDP/] to characterize the different existing rebel groups in Somalia. We also found some studies describing the financial aspects of these groups that may help us to calibrate the percentage extorted from Enterprises. One example is the report
Keatinge, T. (2014). The Role of Finance in Defeating Al-Shabaab. Whitehall Report 2-14. London: Royal United Services Institute for Defence and Security Studies.
that estimates such percentage, which is characterized as corporate taxes, around 2.5% of the Enterprises’ profit.
Once we calibrate the input parameters, we will assess the performance of the model by fitting some aggregate statistics with empirical data. Our initial thought is to assess and validate the model based on the patterns of Enterprises fleeing the region and patterns of fights among rebel groups.
The empirical data about fleeing patterns will be extract from UNHCR (United Nations High Commissioner for Refugees) [http://unhcr.org]; while the empirical data about fights pattern will be extracted from the ACLED (Armed Conflict Location and Event Dataset) [https://www.acleddata.com/]. ACLED provides a dataset with all the violent events that ocurred in Somalia from 1997 to 2018 specifying the date, type of violent event, actors involved, and the region where it took place.
We are not advanced in the calibration and validation process yet and we know it is very challenging, e.g., the lack of data. There is still much uncertainty whether our plan on how to use the empirical data to calibrate and validate the model will work. Hence, I would appreciate any feedback about previous experiences trying to perform these tasks in similar models.