Calendar

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

Kieran Marray, Laidlaw Scholar 
St. Catherine’s College
University of Oxford

FORTEC: Forecasting the Development of Artificial Intelligence up to 2050 Using Agent-Based Modeling

Friday, August 31, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Kieran is a Laidlaw Scholar from St Catherine’s College, University of Oxford. He has been visiting the Center for Social Complexity over the summer to do research in complexity economics supervised by Professor Rob Axtell.

Due to a welcome reception for new and returning CDS student, there will be no colloquium on Friday, September 7. The next one will be held on Friday, September 14.  Speaker and topic to be announced later.

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

William Kennedy, PhD, Captain, USN (Ret.)
Research Assistant Professor
Center for Social Complexity
Computational and Data  Sciences
College of Science

Characterizing the Reaction of the Population of NYC to a Nuclear WMD

Friday, September 14, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:  This talk will again review the status of our multi-year project to characterize the reaction of the population of a US megacity to a nuclear WMD event 2 years into the project. Our approach has been to develop an agent-based model of the New York City area, with agents representing each of the 23 million people, and establish a baseline of normal behaviors before exploring the population’s reactions to small (5-10Kt) nuclear weapon explosions. We have the modeled the environment, agents, and their interactions, but there have been some challenges in the last year. I’ll review our status, successes, and challenges as well as near term plans.

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

Michael Eagle, Asst. Professor
Department of Computational and Data Sciences
George Mason University

Understanding Behavior in Interactive Environments: Deriving Meaningful Insights from Data 

Friday, September 21, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:  Advanced learning technologies are transforming education as we know it. These systems provide a wealth of data about student behavior. However, extracting meaning from such datasets is a challenge for researchers; and often impossible for the instructors. Understanding learner behavior is critical to finding, extracting, and acting on insight found in educational data. It is equally important to have strong evaluative methodologies to explore the effectiveness of new interventions, and pinpoint when, where, and precisely what students are learning.

This talk covers the ways I have combined human modeling, qualitative, quantitative (statistical and machine learning) methods enable researchers to make sense of behavior and to produce data-driven personalization. I will have a focus on modeling of humans in interactive problem-solving environments, such as intelligent tutoring systems, online courses, and educational video games. Combining results from experimental design, machine learning, and cognitive models results in large improvements to existing learning systems, as well as powerful insights for instructors and researchers on how students behave and learn in interactive environments.

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

Robert Axtell, Professor
Computational Social Science Program,
Department of Computational and Data Sciences
College of Science
and
Department of Economics
College of Humanities and Social Sciences
George Mason University

Are Cities Agglomerations of People or of Firms? Data and a Model

Friday, September 28, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:  Business firms are not uniformly distributed over space. In every country there are large swaths of land on which there are very few or no firms, coexisting with relatively small areas on which large numbers of businesses are located—these are the cities. Since the dawn of civilization the earliest cities have husbanded a variety of business activities. Indeed, often the raison d’etre for the growth of villages into towns and then into cities was the presence of weekly markets and fairs facilitating the exchange of goods. City theorists of today tend to see cities as amalgams of people, housing, jobs, transportation, specialized skills, congestion, patents, pollution, and so on, with the role of firms demoted to merely providing jobs and wages. Reciprocally, very little of the conventional theory of the firm is grounded in the fact that most firms are located in space, generally, and in cities, specifically. Consider the well-known facts that both firm and city sizes are approximately Zipf distributed. Is it merely a coincidence that the same extreme size distribution approximately describes firm and cities? Or is it the case that skew firm sizes create skew city sizes? Perhaps it is the other way round, that skew cities permit skew firms to arise? Or is it something more intertwined and complex, the coevolution of firm and city sizes, some kind of dialectical interplay of people working in companies doing business in cities? If firm sizes were not heavy-tailed, but followed an exponential distribution instead, say, could giant cities still exist? Or if cities were not so varied in size, as they were not, apparently, in feudal times, would firm sizes be significantly attenuated? In this talk I develop the empirical foundations of this puzzle, one that has been little emphasized in the extant literatures on firms and cities, probably because these are, for the most part, distinct literatures. I then go on to describe a model of individual people (agents) who arrange themselves into both firms and cities in approximate agreement with U.S. data.

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

J. Brent Williams

Founder and CEO
Euclidian Trust

Improved Entity Resolution as a Foundation for Model Precision 

Friday, November 2, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract: Analyzing behavior, identifying and classifying micro-differentiations, and predicting outcomes relies on the establishment of a core foundation of reliable and complete data linking. Whether data about individuals, families, companies, or markets, acquiring data from orthogonal sources results in significant matching challenges. These matching challenges are difficult because attempts to eliminate (or minimize) false positives yields an increase in false negatives. The converse is true also.

This discussion will focus on the business challenges in matching data and the primary and compounded impact on subsequent outcome analysis. Through practical experience, the speaker led the development and first commercialization of novel approach to “referential matching”. This approach leads to a more comprehensive unit data model (patient, customer, company, etc.), which enables greater computational resolution and model accuracy by hyper-accurate linking, disambiguation, and detection of obfuscation. The discussion also covers the impact of enumeration strategies, data obfuscation/hashing, and natural changes in unit data models over time.

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

William Lamberti, CSI PhD Student
Department of Computational and Data Sciences
George Mason University

Classifying Pill Spies Using Storks 

Friday, November 9, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:  Simple and intuitive measures of shape are substantial challenges in image analysis and computer vision.  While measures of shape do exist, there are only a few intuitive and mathematically derived measures for other polygons.  In this talk, a measure, which we call shape proportions, for regular polygons and circles are shown.  From these proportions, we find the corresponding encircled image-histograms for classification purposes.  This method of using shape proportions and encircled image-histograms is called SPEIs (which is pronounced as ‘spy’).  An analysis using simulated and actual shape images were compared to ensure its utility.  Future work regarding applying SPEIs to NIH pill data using stratified over-representative k-folds cross-validation (abbreviated as STORKC, which is pronounced as ‘stork’) will be discussed.

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

Joseph Shaheen

Bachelor of Science, Murray State University, 2003
Master of Professional Studies, Georgetown University, 2011
Master of Business Administration, Georgetown University, 2013

Data Explorations in Firm Dynamics:
Firm Birth, Life, & Death Through Age, Wage, Size & Labor

Monday, November 26, 2018, 12.30 p.m.
Research Hall

All are invited to attend.

Committee
Robert Axtell, Dissertation Director
Eduardo Lopez
John  Shortle
William Rand
Marc Smith

A better understanding of firm birth, life, and death yields a richer picture of firms’ life-cycle and dynamical labor processes. Through “big data” analysis of a collection of universal fundamental distributions and beginning with firm age, wage and size, I discuss stationarity, their functional form, and consequences emanating from their defects. I describe and delineate the potential complications of the firm age defect–caused by the Great Recession—and speculate on a stark future where a single firm may control the U.S. economy. I follow with an analysis of firm sizes, tensions in heavy-tailed model fitting, how firm growth depends on firm size and consequently, the apparent conflict between empirical evidence and Gibrat’s Law. Included is an introduction of the U.S. firm wage distribution. The ever-changing nature of firm dynamical processes played an important role in selecting the conditional distributions of age and size, and wage and size in my analysis. A closer look at these dynamical processes reveals the role played by mode wage and mode size in the dynamical processes of firms and thus in the firm life-cycle. Analysis of firm labor suggests preliminary evidence that the firm labor distribution conforms to scaling properties—that it is power law distributed. Moreover, I report empirical evidence supporting the existence of two separate and distinct labor processes—dubbed labor regimes—a primary and secondary, coupled with a third unknown regime. I hypothesize that this unknown regime must be drawn from the primary labor regime—that it is either emergent from systemic fraudulent activity or subjected to data corruption. The collection of explorations found in this dissertation product provide a fuller, richer picture of firm birth, life, and death through age, wage, size, and labor while supporting our understanding of firm dynamics in many directions.

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

Doug Reitz
Bachelor of Science, Pennsylvania State University, 1995
Master of Science, Binghamton University, 2007

Atomistic Monte Carlo Simulation and 
Machine Learning Data Analysis of 
Eutectic Alkali Metal Alloys 

Tuesday, November 27, 2018, 10:00 a.m.
Research Hall, Room 92

All are invited to attend.

Committee
Estela Blaisten-Barojas, Dissertation Director
Igor Griva
Dmitri Klimov
Howard Sheng

Combining atomistic simulations and machine learning techniques can significantly expedite the materials discovery process. Here an application of such methodological combination for the prediction of the configuration phase (liquid, amorphous solid, and crystalline solid), melting transition, and amorphous-solid behavior of three eutectic alkali metal alloys (Na-K, Na-Cs, K-Cs) is presented.  It is shown that efficient prediction of these properties is possible via machine learning methods trained on the topological local structural properties alone.  The atomic configurations resulting from Monte Carlo annealing of the eutectic alkali alloys are analyzed with topological attributes based on the Voronoi tessellation using expectation-maximization clustering, Random Forest classification, and Support Vector Machine classification.  It is shown that the Voronoi topological fingerprints make an accurate and fast prediction of the alloy thermal behavior by cataloging the atomic configurations into three distinct phases: liquid, amorphous solid, and crystalline solid.  Using as few as eight topological features the configurations can be categorized into these three phases.  With the proposed metrics, arrest-motion and melting temperature ranges are identified through a top down clustering of the atomic configurations cataloged as amorphous solid and liquid.

The methodology presented here is of direct relevance in identifying or screening unknown materials in a targeted class with desired combination of topological properties in an efficient manner with high fidelity.  The results demonstrate explicitly the exceptional power of domain-based machine learning in discovering topological influence on thermodynamic properties, and at the same time providing valuable guidance to machine learning workflows for the analysis of other condensed systems.  This statistical learning paradigm is not restricted to eutectic alloys or thermodynamics, extends the utility of topological attributes in a significant way, and harnesses the discovery of new material properties.

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

Sanjay Nayar
CSS PhD Student

Title: Interlocking Directorates Analysis: Evidence from India BSE-100

Friday, November 30, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

Interlocked directorates among companies are common across the world and have been studied quite extensively in the Western World. This study focuses on interlocking directorates, also referred to as inter-organizational elite cooptation (Allen, 1974), among the top 100 publicly traded companies on the Bombay Stock Exchange (BSE-100) in India. The time period analyzed is between 2006 and 2010, the years spanning the recent great recession. While De (2012) looked at the performance effects of interlocking directorates within Indian business groups irrespective of their membership in BSE-100, it did not address in the analysis the key players, cliques, etc., the evolution of the interlocking over time, or any comparisons with the United States. This broad exploratory study is the first to look at the BSE-100 interlocking directorates’ network to see how it has or has not been dominated by a select group of individuals, companies or sectors during 2006-2010, along with the companies’ performances in the longer-term, given their position in the network. Some comparisons are also made with the US market using information available in published papers (Everard, 2002). This study also serves a secondary purpose of being an introduction to the interconnections between some of the biggest players in the Indian Economy/Stock Market and thus would also be of interest to those studying business in India.