Calendar
During the fall 2018 semester, the Computational Social Science (CSS) and the Computational Sciences and Informatics (CSI) Programs have merged their seminar/colloquium series where students, faculty and guest speakers present their latest research. These seminars are free and are open to the public. This series takes place on Fridays from 3-4:30 in Center for Social Complexity Suite which is located on the third floor of Research Hall.
If you would like to join the seminar mailing list please email Karen Underwood.
Research Colloquium on Computational Social Science/Data Science
AnaMaria Berea, PhD
Blue Marble Space Institute of Science Research Scientist
Visiting Research Assistant Professor
University of Central Florida
Exploring what is universally possible for life with a wealth of computational methods
Friday, September 27, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
Research Colloquium on Computational Social Science/Data Science
Amira Al-Khulaidy
with Valentin Vergara
CSS PhD Students
George Mason University
Corruption and the effects of influence within social networks: An agent-based model of the “Lava Jato” scandal.
Friday, October 04, 2019 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
Corruption, and more specifically corruption in Latin America, is a complex phenomenon which is affected by politics, social structures and institutions, as well as individual behaviors. The Lava Jato is a large-scale example of corruption in Brazil. As of early 2019, there is still an ongoing investigation surrounding what has been lauded as the largest corruption scheme in Latin America. Advances in data analysis, computation, and social networks have allowed progress to be made with these types of investigations. The Lava Jato case has been a clear example of how breaking up social networks and understanding the extent of crime and individual corruption has revealed webs of corruption that have influenced politics, as well as hindered economic developed in Brazil. The several layers of interactions between individuals and institutions can be difficult to grasp and understanding the patterns and relationships within complex large-scale phenomena, such as corruption can seem impossible. Agent-based models can help with understanding these complex behaviors and systems. By capturing the patterns and gaining a better understanding of how corruption emerges and is manifested, we can help inform policy, as well as create better tools and methods for crime prevention and detection.
Bio:
Amira Al-Khulaidy – Computational Social Science PhD student, MEd. in Education, University of Virginia Valentin Vergara – Computational Social Science PhD student, MSc. Natural Resource Economics, Universidad de Concepción, Chile
Research Colloquium on Computational Social Science/Data Science
Neil Johnson
Professor of Physics
George Washington University
Slaying the Online Hydra of Hate, Distrust and anti-Science
Friday, October 11, 2019 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
[2] N.F. Johnson et al., New online ecology of adversarial aggregates: ISIS and beyond, Science 352, 1459 (2016)
Research Colloquium on Computational Social Science/Data Science
Fahad Aloraini
CSS PhD student
Modeling Solar-Panel Technology adoption in Austin: a test of the power of integrating GIS and Cognitive modeling.
Friday, October 18, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
Joint CDS, Math, and Physics Seminar
Mason Porter
Mathematics Professor
University of California, Los Angeles
Topological Data Analysis of Spatial Complex Systems
Thursday, October 24, 1:00 p.m.
Exploratory Hall, Room 3301, Fairfax Campus
All are welcome to attend.
Research Colloquium on Computational Social Science/Data Science
Katherine Anderson
Visiting Assistant Professor
Department of Informatics and Networked Systems
School of Computing and Information
University of Pittsburgh
Friday, October 25, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
Notice and Invitation
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Sciences and Informatics
Department of Computational and Data Sciences
College of Science
George Mason University
Christine Harvey
Bachelor of Science, Stockton University, 2011
Master of Science, Stockton University, 2013
Modeling, Simulation, and Analysis of the US Organ Transplant System
Tuesday, October 29, 2:00 p.m.
Exploratory Hall, Room 3301
All are invited to attend.
Committee
Robert Weigel, Dissertation Director
Andrew Crooks, Committee Chair
Hamdi Kavak
James Gentle
Analysis, modeling, and simulation of organ transplantation and donation can enhance the understanding of this complex system and guide strategic policy improvements. Four major research questions are addressed in this work: (1) how can we further enable data-driven research of the transplant system for future scientists?; (2) what demographic factors influence donations and access to transplantation?; (3) how do laws and policies affect organ donations?; and (4) how do certain patient advantages impact the overall system as well as those lacking advantages?
A data pipeline and associated software were developed and published that address how to further data-driven research of the transplant system for future scientists. This software simplifies access to and analysis of data from proprietary Organ Procurement and Transplantation Network (OPTN) Standard Transplant Analysis and Research (STAR) files to an open-source database format. These files contain data on every organ donor, waitlist registrant, and transplant recipient since 1987 in the US. This data pipeline directly facilitated the next phase of research which involved performing an analysis of the transplant system using this dataset. The exploratory data analysis scales transplant data to the relative populations to gain a better understanding of the differences between demographic groups and reveals important differences across education levels, gender, race, and ethnicity.
Demographic factors influencing organ donation and access to transplants are analyzed in this work through exploratory visualizations and predictive modeling. A visual exploratory analysis is presented which examines demographic features of organ donors and highlights differences in intersectional data across the population of donors compared to the relative population described by the US Census. Additionally, a random forest model is used to determine the features of patients on the waitlist for a kidney transplant to determine if certain attributes may inadvertently drive the allocation system. This model predicts patient outcomes based on features represented in the model with an accuracy above the zero-rule baseline. The analysis found that patient age, year of listing, body weight, and zip code are important factors in determining a patient’s outcome – other demographic factors such as race and gender were not important prediction features.
State and local laws, policies, and their impact on organ donation are evaluated through a statistical analysis that compares donations after the implementation of a policy to areas without the policy implementation. A database of state and local laws and policies and the years of implementation was developed to compare donations across the country. The results demonstrated that some policies can be correlated with a change in donation, but only for certain demographic subgroups in a population.
Finally, I built discrete event simulation models of a representative patient population to determine the impact of changes to the transplant system that can not be easily demonstrated in the real world. A transplant process model was developed to determine how increasing living and deceased donation overall and within racial sub-groups would impact the number of donors each year. Additionally, an agent-based queuing model was used to understand the impact of allowing patients to register within more than one area. This model provides a valuable tool for examining policy changes that shows the global and local impacts of multiple listing. The analysis found that multiply listed patients have improved access to transplants and are less likely to die while waiting for a transplant.
Research Colloquium on Computational Social Science/Data Science
Robert Axtell
Professor Computational Social Science PhD Program
Department of Computational and Data Sciences
George Mason University
Working with Heavy-Tailed Data: A Tutorial
Friday, November 01, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
Research Colloquium on Computational Social Science/Data Science
John Schuler
PhD Student
Department of Economics
George Mason University
The Econometrics of Prices in a Network Economy
Friday, November 08, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
Research Colloquium on Computational Social Science/Data Science
Dr. Seth Brown
Steam Solution LLC
National Municipal Stormwater Alliance
To Be or Not To Be: Introducing the Green Stormwater Infrastructure Social Spatial Adoption (G-SSA) Model
Friday, November 15, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
The use of incentives in stormwater programs is a common feature that is used to motivate private property owners as well as land developers to adopt specific types of stormwater management infrastructure at the site or parcel level. While incentives for land developers, such as reduced plan review time or reduced plan review fee for projects that utilize specific BMPs, such as green stormwater infrastructure (GSI), are helpful policies in driving implementation of innovative stormwater practices, this implementation is limited to land development activity. Approximately 75% of existing impervious cover is associated with land development activities that took place prior to federal legislation focused on urban stormwater runoff. The implication of this is that a majority of impervious cover across the U.S. discharges runoff that is either inadequately managed or not managed at all. Until we address these existing areas, impacts from these areas will continue to impact our waters. This is reflected by evolving regulations that require a certain amount of existing impervious cover to be retrofitted to provide stormwater management. Many cities, such as Milwaukee, Seattle and Atlanta, also have retention volume goals as part of a regulatory program as well as an effort to increase the resilience and sustainability of urban areas.
The motivation to retrofit existing impervious areas is a driver to retrofit both public and private lands. Public rights-of-way (ROWs) are often challenging to work within, and there is a limited amount of public ROW available. Overall, 60% of land in the U.S. is privately held, with large portions of these areas located in large public parks in Mountain Region states. The result is that many states have private land ownership rates at 80% or higher; clearly this is a need to find ways to locate urban stormwater retrofits on private lands.
The default method of incentivizing private land owners to adopt onsite stormwater infrastructure is a stormwater fee reduction according to the 2018 Black and Veatch Stormwater Utility Survey. The limitation with this approach comes in when a community does not have a stormwater utility established, which is the case for at least 2/3 of the regulated stormwater entities in the country. And even if a utility exists, the fees are often not high enough to make economic sense for onsite adoption when considering payback periods and other financial metrics. The reason for this is simple – stormwater utility fees are set at a level/rate to pay for needed stormwater programmatic and implementation rather than to create an effective financial incentive for private parcel owners to adopt BMPs onsite. The result of this are participation rates in incentive-based stormwater infrastructure on-site investments of 2-5% or lower associated with traditional incentive programs, which also include cost-sharing and subsidy programs as well. Due to this reason, communities are considering market-based approaches, such as stormwater credit trading, that can reward private property owners in a more robust way for onsite BMP adoption.
While market-based programs hold much promise, the focus of research in this area has been (rightly) on program architecture and policies with the view of “if we build it, they will come”. However, this leaves a void in understanding on how parcel owners will respond to market-based option. Questions regarding the motivations for adoption, how decisions on adoption are made, and how adoption on parcels affect adoptions in neighboring areas or parcels. This presentation will outline research done to begin to address the “consumer behavior” view of BMP adoption. Specifically, a socio-economic model based upon cellular automata-style agent-based modeling will be presented to illustrate a method to capture the adoption of GSI across multiple urban neighborhoods that comprise a city-wide landscape.
This model – the Green Stormwater Infrastructure Social Spatial Adoption (G-SSA) model – provides insights on neighboring effects, spatial dynamics, and decision-making aspects of GSI adoption based upon social theory. Model sensitivity analysis highlights the significance of social and spatial model elements to overall GSI adoption rates and pattern. An applied G-SSA model has been developed and explored to simulate the complex emergent patterns for GSI adoption across a specific cityscape (Washington, D.C.). Applied G-SSA model output is consistent with expected model behavior as well as observed and document GSI adoption patterns in Washington, D.C.