Calendar

Computational Social Science Research Colloquium /
Colloquium in Computational and Data Sciences

Billy Lamberti
CSI PHD STUDENT
Department of Computational and Data Sciences
George Mason University

I Spy

Friday, April 5, 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 Social Science

Department of Computational and Data Sciences

College of Science

George Mason University

Gary Keith Bogle
Bachelor of Arts, University of California, Davis, 1990

Master of Arts, University of Illinois at Urbana-Champaign, 1995

Master of Science, Marymount University, 2003

 Polity Cycling in Great Zimbabwe via Agent-Based Modeling:  

The Effects of Timing and Magnitude of External Factors

Thursday, April 11, 2019, 1:00 p.m.

Research Hall, Room 92

All are invited to attend.

Committee
Claudio Cioffi-Revilla, Chair

William Kennedy

Amy Best

This research explores polity cycling at the site of Great Zimbabwe. It rests on laying out the possibilities that may explain what is seen in the archaeological record in terms of modeling what external factors, operating at specific times and magnitudes. What can cause a rapid rise and decline in the polity? This is explored in terms of attachment that individuals feel towards the small groups of which they are a part of, and the change in this attachment in response to their own resources and the history of success that the group enjoys in conducting collective action. The model presented in this research is based on the Canonical Theory of politogenesis. It is implemented using an agent-based model as this type of model excels at generating macro-level behavior from micro-level decisions. The results of this research cover the relationship between environmental inputs and the pattern of growth and decline of groups, the differences in group fealty and resources between successful groups and unsuccessful groups, the change in the number of groups throughout the simulation and the relationship between the probability of success in collective action and the success of the groups themselves. The input parameters to the model presented here are the collective action frequency (CAF) and environmental effect multiplier. The results show that a prehistoric polity can be modeled to demonstrate a sharp rise and fall in community groups and that the rise and fall emerges from the individual decision-making. Different sets of input parameters represent different environmental conditions, from the stable and predictable to less stable to quite unpredictable. Regardless of the environmental variability, the overall value of fealty experienced by community members moves in a similar fashion for all input sets. However, the more stable environment of Set A means the overall feelings of attachment to leadership do not fall as fast as they do in the more variable environments. In all, there is a two-stage process in which members in the community are sorted in to the surviving groups. Success in collective action leads to overall group success. The significance of this research is that it provides a basis for understanding that, while the archaeological record is incomplete, what happened in Great Zimbabwe lies within what has happened in other areas. What seems at first glance to be unusual can be explained through expected environmental and social factors that affect prehistoric societies on other continents. Furthermore, this research provides the basis for further quantifying the analysis of prehistoric societies by providing a model of laying out external factors along the lines of collective action frequencies and environmental effect multipliers.

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

John Bjorn Nelson
Bachelor of Science, University of Maryland, 2007

A Computational Model of Belief System Construction and
Expression with Applications to American Democracy

Friday, April 12, 2019, 1:30 p.m.
Research Hall, Room 162

All are invited to attend.

Committee
Claudio Cioffi-Revilla, Chair
William G. Kennedy
Jennifer N. Victor

This is a dissertation about people and their beliefs. It asks, how do beliefs form? Why do they change? How does the environment affect construction? What is the relationship between asocial experiences and the social exchange of information about them? And, how
do beliefs affect social structure? To interrogate these questions, I build an agent-based model with agent-to-nature and agent-to-agent interaction spaces. The payoff distributions associated with each context-action pair in nature are homogeneous. However, agent
exposure rates are heterogeneous. The agent-to-agent interactions allow for social information exchange, facilitating the discovery of best contexts and actions for selection. All agent expressions are sincere. However, to guard against error integration, agents sample
dynamic stereotypes over overt traits as proxies for experiential counterpart reliability. An expression is more receivable when aligned with social expectations than when it is not. This creates a recursive relationship whereby stereotypes affect belief and beliefs affect
stereotypes. I implement three stereotyping strategies and six different environments. The three stereotyping strategies — prosocial, informative, and discriminatory — operationalize different assumptions about social information processing. Five of the environments
progressively increase inherent structure. The sixth introduces broadcasts which synchronize contextual salience in social interactions.

Computational Social Science Research Colloquium /
Colloquium in Computational and Data Sciences

Melanie Swartz
Melanie Schwartz, CSS PhD Candidate
Department of Computational and Data Sciences
George Mason University

Emoji Use in Social Media during events

Friday, April 12, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

Emoji in social media add more information than just a pictograph to accompany words or convey emotion. Emoji use related to communication about collective social events can provide additional insight about our collective identity and social interactions. This friday Melanie Swartz will be presenting preliminary results and welcomes your feedback of her analysis on emoji use in social media for a number of events ranging from national, religious, protests, marches, celestial events,  global scheduled events such as International Women’s Day, and more. We look forward to seeing you in person for an engaging discussion on Friday as this event will not be recorded or live streamed.

Computational Social Science Research Colloquium /
Colloquium in Computational and Data Sciences

Francesca Tavaza, PhD
National Institute of Standards and Technology Materials Science and Engineering Division

The JARVIS project: Accelerating discovery of materials and validation of models
using classical, quantum and machine-learning methods

Friday, April 19, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

Identifying new materials for technological applications is the goal of the Material Genome Initiative (MGI). As a response, NIST started the JARVIS project, a combination of atomistic databases at the classical and quantum level, and machine learning models. JARVIS-DFT is a collection of physical properties computed using Density Functional theory (DFT) for about 30000 materials. For each material, we determined its heat of formation, conventional and improved DFT bandgaps, dielectric function, elastic, phonon, electronic and transport properties. Statistical analysis of such properties allows to identify novel trends as well as new materials with desirable
properties. JARVIS-FF is a database of classically computed properties, designed to facilitate the user in choosing the right classical force field (FF) fortheir investigation. It uses the LAMMPS code to compute the same property, for the same material, with as many force fields as available (more than 25000 classical force-field). We focused on quantities like relaxed structures, elastic properties, surface energies, vacancy formation energies and phonon vibrations. JARVIS-FF contains these calculations for more than 3000 materials, so that a direct comparison between FF is easily achieved. Lastly, using all the properties in JARVIS-DFT as a training set, and novel descriptors inspired by FF-fitting, we developed machine learning (ML) models for all the properties studied in JARVIS-DFT. This allows to make on the fly predictions, and, therefore, to use ML to pre-screen materials.

Short Bio:

• Undergraduate degree in Physics in Milan, Italy, 1993 (Universita’ Statale di Milano, Milano, Italy)
• Master in Material Science in Milan, Italy, 1996 (Universita’ Statale di Milano, Milano, Italy). Dissertation topic: Tight-binding modeling of Cobalt and Iron Silicides, including fitting of the tight-binding parameters.
• PhD in Physics at The University of Georgia, GA, USA in 2003 (PhD. Advisor: Prof. Davis Landau). Dissertation topic: Classical Monte Carlo simulations of Si and Si-Ge compounds under various conditions.
• PostDoc at NIST starting in  2003, focusing on Density Functional theory (DFT) modeling of mechanical properties in metals.
• Brief hiatus working at the Army Research Laboratory in 2008 for a short time, otherwise at NIST ever since I got there as a postdoc.
• Currently: running an atomistic modeling group (both classical and DFT modeling) focused on the investigation of specific, technological relevant materials (TaS2, TaSe2, Bi2MnSe4, for instance) as well as on compiling databases of material properties. My group extensively uses artificial intelligence (AI) tools to accelerate material discovery as well as to build novel force fields (physics-inspired, neuron network-based fitting of Si, Ge, SiGe, AlNi potentials).

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

Thomas P. Boggs
Master of Science, George Mason University, 2002
Master of Science, Virginia Tech, 1994
Bachelor of Arts, Virginia Tech, 1992
Bachelor of Science, Virginia Tech, 1991

Probabilistic Topic Modeling for Hyperspectral Image Classification

Monday, April 22, 2019, 3:00 p.m.
Exploratory Hall, Room 3301
All are invited to attend.

Committee
Jason Kinser, Chair
Igor Griva
Ronald Resmini
Robert Weigel

Probabilistic Topic Models are a family of mathematical models used primarily to identify latent topics in large collections of text documents. This research adapts the topic modeling approach to the unsupervised classification of hyperspectral images. By considering image pixels similarly to text documents and quantizing data for each spectral band to develop a spectral feature vocabulary, it is demonstrated that by using Latent Dirichlet Allocation with a hyperspectral image corpus, learned topics can be used to produce unsupervised classification results that often match ground truth better than the commonly used k-means algorithm. The
topic modeling approach developed is demonstrated to easily extend to classification of image regions by aggregating spectral features over spatial windows. The region-based document models are shown to account for the spectral covariance and heterogeneity of ground-cover classes, resulting in similarity to land use ground truth that increases monotonically with window size.

Multiresolution wavelet decompositions of pixel reflectance spectra are used to develop a novel feature vocabulary that more naturally aligns with material absorption and reflectance features, further improving classification results. The wavelet-based document modeling approach is evaluated against synthetic image data, a small AVIRIS image with 16 ground truth classes, and finally on practical-sized, overlapping AVIRIS and Hyperion images to demonstrate the utility of the models. Multiple wavelet bases and numbers of quantization levels are considered and for the data sets evaluated, it is determined that using the Haar wavelet with 10 quantization levels yields the best performance, while also producing easily interpretable topics. It is demonstrated that by omitting low-level wavelet coefficients, vocabulary size and model inference time can be significantly reduced without loss of accuracy.

The wavelet-based approach is extended by replacing quantization levels with simple thresholds for positive and negative wavelet coefficients, reducing the vocabulary size to two times the number of wavelet coefficients. The thresholded wavelet model provides accuracy comparable to the quantized wavelet model, while having significantly shorter inference time and supporting easily interpretable visualization of topics in the wavelet domain. By establishing appropriate model hyperparameters and omitting low-level wavelet
coefficients, the thresholded wavelet model provides better unsupervised classification results than previously developed quantized band models, has shorter model parameter estimation time, and has an average document word count smaller by a factor of 5 and a vocabulary smaller by a factor of 10

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

Matthew Oldham
Bachelor of Economics with Honours, University of Tasmania, 1995
Master of Arts in Interdisciplinary Studies, George Mason University, 2016

The Utilization of Computational Social Science
for the Benefit of Finance

Wednesday, April 24, 2019, 9:00 a.m.
Exploratory Hall, Room 3301

All are invited to attend.

Committee
Robert Axtell, Chair
Andrew Crooks
Edward Lopez
Richard Bookstaber

The ability to identify the mechanisms responsible for the behavioral characteristics of financial markets has remained an elusive pursuit. Further, the precise behavioral characteristics of financial markets remains a point of contention. Some practitioners proclaim that markets are efficient and the return profile of financial assets follow a Gaussian distributed random walk, while others suggest that markets are not efficient, with returns tending to be heavily skewed and markets record instances of extreme outlying events at a rate more than what the efficient school prescribes. A feasible explanation for why financial markets behave as they do is that they are a complex adaptive system (CAS), an approach where investors and firms are considered heterogeneous interacting agents (HIA), which contrasts against the single representative agent approach utilized in the efficient market (neoclassical economic) paradigm.

Firstly, this dissertation provides an overview of the basis of the efficient market framework (EMF) before presenting the need to pursue alternative methods. The principal alternative discussed is the utilization of Computational Social Science (CSS) tools to consider financial markets as a CAS. The primary impetus for the approach is the statistical imprint of a CAS – power-law distributions – are found in asset returns and various other economic variable related to financial markets, including the distributions of shareholders and firm size. Of the various CSS tools, the remainder of the dissertation presents two agent-based models aimed at addressing a variety of research, yet with a common theme of quantifying the effects of agents placing an increased focus on short-term factors, a phenomenon known as “short-termism.”

The first model considers the effects of investors forming an information network with each other in an agent-based artificial stock market. In turn, agents try and improve their investment performance by adjusting their connections; a process that involves cutting ties with those agents who provide poor quality information and connecting to the betterperforming investors. The crucial elements in the model are the timeframe over which the agents consider their performance; the interval between rewiring their connections; and
their tendency to follow the advice of their connections over other information sources. Through varying the effect of these elements meaningful insights into the dynamics driving the behavior of the financial markets, with the presence of even a small proportion of
short-term investors being responsible for a material increase in market volatility. A similar record occurred after reducing the interval between when investors adjust their information network.

An ambition research agenda underlies the implementation of the second model. The foundation for the model stems from the growing concern that the management of publicly listed firms is becoming preoccupied with the share price of their firm, thereby placing an
increased, and non-optimal, focus on their short-term earnings. To address this issue required the expansion of the existing agent-based artificial stock market approach to include many firms who have their earnings endogenously influenced by the market. To achieve the required expansion, the model has firms maintain growth expectations which they adjust after factoring in their most recent performance against those expectations and the movement in their firm’s share price. Firms also must allocate their limited resources between growing sales or margins. In terms of the investors, the model considers various investment styles, with individual styles and combinations responsible for generating greater volatility in the market and more extreme adjustments by management. Before undertaking the extensions, an extensive set of data relating to the size, growth, and performance of globally listed firms was collected and assessed. Consistent with previous research, the distributions, apart from growth, were heavily skewed. The growth distributions were found to be somewhat consistent with Laplace distributions, which is the existing growth distribution benchmark.

RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES

Ross Schuchard
Computational Social Science PhD Candidate
Department of Computational and Data Sciences

Bots in Nets: Empirical Comparative Analysis of Bot Evidence in Social Networks

Friday, April 26, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

The emergence of social bots within online social networks (OSNs) to diffuse information at scale has given rise to many efforts to detect them. While methodologies employed to detect the evolving sophistication of bots continue to improve, much work can be done to characterize the impact of bots on communication networks. In this study, we present a framework to describe the pervasiveness and relative importance of participants recognized as bots in various OSN conversations. Specifically, we harvested over 30 million tweets from three major global events in 2016 (the U.S. Presidential Election, the Ukrainian Conflict and Turkish Political Censorship) and compared the conversational patterns of bots and humans within each event. We further examined the social network structure of each conversation to determine if bots exhibited any particular network influence, while also determining bot participation in key emergent network communities. The results showed that although participants recognized as social bots comprised only 0.28% of all OSN users in this study, they accounted for a significantly large portion of prominent centrality rankings across the three conversations. This includes the identification of individual bots as top-10 influencer nodes out of a total corpus consisting of more than 2.8 million nodes.

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

E. André L’Huillier M.
Bachelor of Arts, Universidad Adolfo Ibañez, 2010
Master of Arts, Universidad Adolfo Ibañez, 2012
Blockbuster Emergence in Entertainment Platform Markets: Modeling
the History of the Video Game Industry in North America

Tuesday, April 30, 2019, 1:00 p.m.
Exploratory Hall, Room 3302
All are invited to attend.

Committee
Robert Axtell, Chair
Marshall Van Alstyne
William Kennedy
Eduardo López

Entertainment markets are typically dominated by blockbusters; which are characterized for being highly popular and financially successful over a vast majority of failures. Today, the entertainment industry has shifted into a platform model, where a similar concentration occurs. The expanding and disruptive placement of multi-sided business organization has modified many cultural markets, reshaping the way products are created, delivered, and consumed. Nevertheless, entertainment platforms still depend heavily on the existence of blockbusters. I study the history of video game industry with particular attention to the life cycle of platforms and blockbuster emergence. After an empirical analysis of the home console market and a literature review of its history, an agent-based model of the video game market is presented. The model aims to represent the complex behavior of the market’s heterogeneous actors. The design is based on platform economics, diffusion through social networks, and social influence; with an emphasis in decision making under high uncertainty. Results of the model successfully reproduce the main dynamics of the market in a simple behavioral representation. The simulation experiments indicate that peer influence in a multi-sided organization is sufficient to reproduce the industry’s life-cycles, its high concentration, and extreme uncertainty. Furthermore, results of the model display the combined effect of promotion and word of mouth; particularly on how mass promotion provides an increment in expectation while the tipping force of adoption usually depends
on social influence. Although the model is able to reproduce the emergence of blockbusters and market concentration in a completely uncertain market, the rule-based nature of its structure allows for future experiments that consider installed base factors, quality, or
asymmetries in market power. After the initial results of the base model, a series of extensions are presented to address additional issues of blockbuster formation in entertainment platforms. The extensions focus in the role of market segmentation in quality perception, the effect of uncertainty and consumer perception, and finally, an exploration on basic aspects of platform management. Results for the extension on consumer preferences and product features presents the complex interaction between sub-groups in the formation
of positive expectations and market concentration; where partial diversity of games properties is better than its extremes (i.e. fully heterogeneous or identical). Results on consumer perception experiments also provide evidence of a non-linear effect on adoption
and market behavior; with higher perception consumers are able to discriminate earlier without the generation of sufficient hype to form blockbusters or platform participation. Finally, the platform management section goes through matters of time of release, multihoming, and a price structure prototype. In general, results on these extensions present an important effect of externalities among platforms operations (e.g. the mutual hype generation when consumers multi-home or when platforms release at closer dates). Future
research on entertainment platforms should consider an empirical approach to describe preference and product heterogeneity, which may further inquire in a critical review of quality in markets with high uncertainty. Finally, the insights of the model are useful for the
study of other markets beyond the video games or the entertainment business. The insights provided and the model’s framework are relevant to any multi-sided system that sees a dominant herd behavior based in decision uncertainty like social media or platforms for
collective action.

RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES

Gary Bogle, PhD
Computational Social Science
George Mason University

Polity Cycling in Great Zimbabwe via Agent-Based Modeling:
The Effects of Timing and Magnitude of External Factors

Friday, May 03, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract: