We are happy to once again celebrate Rick Riolo and his deep impact on the growth and development of the Center for the Study of Complex Systems. The Rick Riolo Memorial Fund (RRMF) is an annual undergraduate complex systems award, which gives us a great opportunity each year, to pay homage to this incredible mentor and his work in agent-based-modeling and complex systems more broadly.
The RRMF prize selection committee was so impressed with two of the submitted entries that we are awarding two first-place prizes. Due to the continued donations we have received from Rick’s past students and colleagues, we are in a position this year to award each prize winner the full first place amount of $1,000.
The two winning submissions were from Zhongming Jiang and Devanshi Shah.
Zhongming Jiang is awarded the RRMF Prize for his work on novel approaches to causal inference based on panel data in the challenging scenario of large numbers of units, short time series, and multiple outcomes (Large N, Small T, Multiple P - A Causal Matrix Completion Method for CRM Panel Data). The selection committee was impressed with the novelty of the approach, the rigor of the exposition, and the potential importance of the results.
Devanshi Shah is awarded the prize for her project entitled "The Utility of Agent Based Modelling in Identifying Neighborhood Effects of Democratization in Heavily Autocratic and Mixed Regime Regions". The committee was impressed with the clarity of her exposition of the assumptions underlying her agent-based modeling approach to particular questions regarding the evolution of authoritarianism.
Zhongming is a senior pursuing a double major in Statistics and Math in LSA. Zhongming has been working with Complex Systems affiliated faculty member Professor Fred Feinberg (UM Ross) and Dr. Longxiu Tian (UNC) His research lies primarily in quasi-experimental designs for observational data to discern causal effects, with particular emphasis on synthetic control and Bayesian Causal Inference. In his project “Large N, Small T, Multiple P: A Causal Matrix Completion Method for CRM Panel Data,” he used a wide variety of statistical methods, along with German Reunification data, to estimate the counterfactual of GDP Per Capita for West Germany had the reunification not occurred. Zhongming essentially constructed a "synthetic West Germany" via Bayesian Matrix Completion that he will shortly apply to full-scale Customer Relationship Management data, as well as other complex, time-dependent systems”.
“In conclusion, our model serves as a unifying platform for Synthetic Control Method and other quasi-experimental approaches in observational studies, contributing to significant methodological advancement and conceptual innovation in the study of complex systems.”
Devanshi Shah is a political science major who is also enrolled in the statistics minor. In her own words, Devanshi describes her work: “This project came about as a means to explore the mechanics of agent-based modeling via the Mesa Library in Python, and its utility as a method of research in the field of Political Science. As such, the central focus of this ABM lies in the topic of political neighborhood effects in international politics. Namely, this model aims to identify whether state preferences for the regime characteristics of their neighbors affect the status of their own regimes, and whether certain preferences can be created such that democratization can take root in heavily autocratic political neighborhoods, as well as neighborhoods of mixed regime type. Though this model remains a work in progress (and will as such continue to be refined over the coming months), it serves as a promising sign for the influence that democracies have in spreading their institutions across regions of significant authoritarianism.”
Congratulations to both winners.
