REGNAMO

This is the first serious system dynamics model I created. I built this model as part of my master’s thesis (Langarudi 2009) to analyze long-term economic impact of investments in oil and gas sector in Iran. Later, I collaborated with my professors and published a journal article (Langarudi et al. 2010) and a book chapter (Langarudi and Radzicki 2013) based on this model. I have based the model’s structural foundation on Ali Mashayekhi’s PhD dissertation (Mashayekhi 1978). For that, I had to rebuild Mashayekhi’s model from scratch as the original model was available only in DYNAMO format which was obsolete by the time. To build the oil and gas sectors of the model, I simplified and used what John Sterman had in his PhD dissertation model (Sterman 1982). You can download the models from the links below:

• Mashayekhi’s original model (Vensim and Stella) files
• My master’s thesis PDF and model (iThink) files

The second model I built was a translation of Homa Katouzian’s theory of arbitrary state and society (Katouzian 1997). The theory takes a very long-term historical perspective to explain swinging political and economic dynamics. The model was endorsed by Katouzian himself. Mike Radzicki and I published a paper to introduce and analyze the model (Langarudi and Radzicki 2018). The paper is also available in Farsi through Iran Nameh Journal (Langarudi and Radzicki 2016) thanks to Emad Taghipour, who translated the paper upon Katouzian’s request. You can download the model from the link below:

• Arbitrary state and society model (iThink) file

For the last chapter of my PhD dissertation, I created a model to revisit the natural resource curse hypothesis. This model is a simplified synthesis of many different theories that help explain different feedback structures involved in a resource dependent political economy. To learn more about this model and its results read Langarudi (2016, 2017, 2020) and Langarudi and Radzicki (2021). You can download the model from the link below:

• Natural resource curse model (iThink) file

We make decisions based on many different factors. Our rational decisions are usually based on what we perceive as the utility of that decision. How our perception is shaped over time is, however, a big question. Behvaioral scientists have been trying to explain perception using simple heuristics. One of these heuristics is peak-end, which suggests that what we perceive as utility of an experience is independent of the duration of our experience. For instance, what we remember as the joy of eating and ice-cream does not depend on how long it took us to finish eating it because we do not use all the instances of that memory. What we remember from it and thus report comes from only two instances of our experience, and that is (a) the peak, i.e., the most intense moment, of our experience, and (b) the last instance of our experience. This theory is in sharp contrast with how we capture perception in traditional system dynamics modeling, i.e., exponential averation (smoothing). So, I decided to model peak-end theory in system dynamics and see if it leads to significant differences. This model is reported and used in Langarudi and Bar-On (2018). There, we test the theory on an Electronic Health Records (EHR) dynamics model, which was created to investigate why and how process improvement initiatives and technologies such as EHR succeed or fail. You can download the models from the following links:

• Peak-End model (vensim) file
• Electronic Health Records

After I finished my PhD, I embarked on a postdoctoral journey followed by an assistant professor position at New Mexico State University from 2017 to 2022. There, my main research focus was on the interplay between water supply-demand, agriculture, and economic development. Our modeling team from New Mexico Water Resources Research Institute (NM WRRI) created a hydrosocial model for the Lower Rio Grande water planning region in New Mexico, which in my view can be calibrated and used for any semi-arid region with relatively significant agriculture and considerable dependency on groundwater. This model has been the basis of several publications, e.g., Langarudi et al. (2019), Langarudi, Maxwell, and Fernald (2021), and Bai, Langarudi, and Fernald (2021). You can download the model from the following link:

• Lower Rio Grande hydrosocial model (Stella) file

The last modeling project I led in New Mexico concerned state policy to sustain and stabilize oil tax revenues. Although the feedback structure of the model is simple, it involves several computational innovations, which are reported in Langarudi and Noor (2024). The model is written in Fortran and can be downloaded from the following link:

• New Mexico oil taxation model (Fortran) files