Calendar

Notice and Invitation
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Social Science
Department of Computational and Data Sciences
College of Science
George Mason University

Ross Jeffrey Schuchard
Bachelor of Science, United States Military Academy, 2004
Master of Arts in Interdisciplinary Studies, George Mason University, 2015

Examining Adaptation in Complex Online Social Systems

Tuesday, June 18, 2019, 10:00 a.m.
Research Hall, Showcase
All are invited to attend.

Committee
Andrew Crooks, Chair
Robert Axtell
Arie Croitoru
Anthony Stefanidis
A. Trevor Thral

Online social systems, comprised of social media services and platforms including social networking (e.g. Facebook, LinkedIn), microblogging (e.g. Twitter, Sina Weibo) and crowdsourcing (e.g. Wikipedia, OpenStreetMap) applications, continue to gain traction among an ever-increasing global user base. The growing reliance upon online social systems to augment an individual’s daily workflow and the resulting interdependence between human and technical systems provide sufficient evidence to classify them as socio-technical systems. These interdependencies are complex in nature and are best defined from a complex adaptive system (CAS) perspective.

It is through a CAS lens that this dissertation examines two types of adaptation in online social systems using an array of Computational Social Science (CSS) tools. In the first type of adaptation, human actors are no longer the sole participants in online social systems, since social bots, or automated software mimicking humans, have emerged as potential threats to stifle or amplify certain online conversation narratives. This section of the dissertation addresses adaptation to these new types of actors by presenting a novel social bot analysis framework designed to determine the pervasiveness and relative importance of social bots within various online conversations. In the second form of adaptation, individual citizens and government entities modify their behaviors in relation to each other through censorship circumvention or detection. This section of the dissertation investigates the rise of digital censorship in online social systems, creating a new agent-based model inspired by the findings from an evaluation of a Turkish digital censorship campaign.

The social bot analysis framework results consistently showed that while users identified as social bots only comprised a small portion of total accounts within the overall research corpus, they account for a significantly large portion of prominent centrality rankings across all observed online conversations. Furthermore, bot classification results, when using multiple bot detection platforms, exhibited minimal overlap, thus affirming that different bot detection algorithms focus on the various types of bots that exist. Finally, the results of the Turkish digital censorship campaign showed marginal effectiveness as some Turkish citizens circumvented the censorship policies, thus highlighting an individual decision cycle to risk punishment and engage in online activities. The recognition of this citizen decision cycle served as the basis for the adaptation to digital censorship model, which used empirical evidence to stylize and template a simulation censorship environment.

Notice and Invitation
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Social Science
Department of Computational and Data Sciences
College of Science
George Mason University

Thomas Dietrich Pike
Bachelor of Arts, University of Arizona, 1999
Master of Arts, American Military University, 2009
Master of Science, National Intelligence University, 2010

Standardizing Complexity: Doctrine and Computation for Integrated Campaigning

Friday, June 21, 2019, 1:00 a.m.
Research Hall, Room 92

All are invited to attend.

Committee
Robert Axtell, Chair
Patrick Gillevet
William G. Kennedy

This dissertation examines the integration of complexity theory and computational tools into U.S. foreign policy. It identifies ways to improve the Department of Defense’s main analytic framework to ensure a more accurate reflection of complex systems and it provides a holistic assessment of the integration of computational tools into Joint campaigns. Based on this analysis, this dissertation advocates the incorporation of Agent Based Models (ABMs) as simulations to support both analysis and foreign policy development at all levels of the foreign policy enterprise. To aid this integration two Mesa based ABM libraries are provided. (1) Multi-level Mesa, the first Python based multi-level library to facilitate the integration and evolution of layered adaptive networks. This library goes beyond existing multi-level libraries by providing greater user flexibility and allowing for the integration and adaption of more complex networks. (2) Distributed Space Mesa, a first attempt at starting a Distributed Mesa meta-library. This library provides modest time improvements to spatial Mesa ABMs and critical lessons for the continued development of a suite of distributed Mesa libraries.

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.

Notice and Invitation
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Science and Informatics
Department of Computational and Data Sciences
College of Science
George Mason University

Swabir Silayi
Bachelor of Science, Fatih University, 2009
Master of Science, Fatih University, 2011
Master of Science, Old Dominion University, 2013

ELECTRONIC STRUCTURE AND DYNAMICS ANALYSIS OF NOBLE METALS BY A TIGHT-BINDING PARAMETRIZATION

Wednesday, December 04, 2019, 3:00 p.m.
Exploratory Hall, Room 3301

All are invited to attend.

Committee
Dr. Estela Blaisten – Committee Chair
Dr. Dimitrios A. Papaconstantopoulos
Dr. James Glasbrenner
Dr. Eduardo Lopez

Theoretical studies of the properties of materials are important as they serve to narrow the focus of what are normally time consuming and costly experimental searches. In modeling these materials, first-principles density functional methods have been proven to quite effective. They have the drawback of being computationally expensive and, to mitigate this, faster approaches have been developed such as the tight-binding model.

We have used the Naval Research Lab (NRL) tight-binding (TB) method to study the electronic and mechanical properties of the noble metals. The tight-binding Hamiltonians are determined from a fit that has a non-orthogonal basis and reproduces the electronic structure and total energy values of first-principles linearized augmented plane wave calculations. In order to perform molecular dynamics simulations, we developed new TB parameters that work well at smaller interatomic distances. We analyze fcc, bcc and sc periodic structures and we demonstrate that the TB parameters are transferable and robust for calculating additional dynamical properties which they had not been fitted to.

To do this, we calculated phonon frequencies and density of states at finite temperature and performed simulations to determine the coefficients of thermal expansion and the atomic mean squared displacement. The energies for vacancy formation were also calculated as were the binding energies for fcc-based, bcc-based and icosahedral clusters of different sizes. The results compared very well with experimental observations and independent first-principles density functional calculations.

Extending from the single element systems, we develop parameter sets for the Cu-Ag and Ag-Au noble metal binary alloys as well. These parameters were fit to the structures 2, 10, 12 − 3,3, with the and representing the different combinations of , and in addition to the fcc , and .

As an output of this extension to the binary systems, the following quantities were reproduced in good agreement with available experimental and theoretical values: elastic constants, densities of electronic states as well as the total energies of additional crystal structures that were not included in the original first-principles database. We also used this TB parametrization for the alloy systems to successfully perform molecular dynamics simulations and determined the energies for vacancy formation, temperature dependence of the coefficient of thermal expansion, the mean squared displacement and phonon spectra. In addition we show that these TB parameters work for determining binding energies and bond lengths of Cu-Ag fcc-like clusters.