Calendar

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.

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

Yang Xu
Bachelor of Science, Nanjing Normal University, 2006
Master of Science, University of Nebraska-Lincoln, 2009

Almost Regular Graphs and Hamiltonian Cycles

Tuesday, December 4, 2018, 3:00 p.m.
Research Hall, Room 92

All are invited to attend.

Committee
Edward Wegman, Dissertation Director
Eduardo Lopez
Geir Agnarrson
Joseph Mar

This dissertation is third in a series aimed at seeking a method to optimized computer architectures for robustness and efficiency. HADI graphs were first introduced in Hadi Rezazad’s dissertation and were further examined in Roger Shores’ dissertation. This dissertation explores this particular class of graph structure in details and defines this graph structure in a mathematical way. Hadi Graphs are a subset of almost regular graphs with certain invariants. The bound of edge numbers is presented to ensure the new structure Hamiltonian. Another interesting alternative interconnect graph that is called hypercube is discussed in this dissertation. The main focus is to find how many edges can be removed but still retain the Hamiltonian property

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

Andrew Crooks, Assoc. Professor
Department of Computational and Data Sciences
George Mason University

Computational Modelling of Slums: Progress & Challenges

Friday, December  7,  3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

Over the last 50 years the world has increasingly become more urbanized, much of this growth has occurred in less developed countries, which often lack the resources to accommodate such growth. This has led to the growth of slums, which is estimated to be home for other 1 Billion people. The UN-Habitat projects slum population to double by 2030, which would make them home for 2 in 5 people living in cities. In this talk I will introduce slums, discuss their growth, and provide an overview on what progress has been made to studying and modeling them. This will lead to a discussion of a series of key challenges that need to be addressed if we are to tackle slums from a computational perspective.

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

Keith Waters, PhD
Schar School of Public Policy
George Mason University

Firm Formation and the Regional Allocation of Labor

Friday, February 01, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract:  The distribution of city-sizes within countries tends to follow a Pareto distribution that satisfies Zipf’s law. Geographically, larger cities tend to be located more distant from one another than smaller cities.  Working towards an explanation of these empirical observations, a geographic extension of Axtell’s agent-based model of endogenous firm formation is presented. The model introduces three components into the underlying model: migration costs, an urban productivity premium, and an urban congestion cost.

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

John Schuler
Economics PhD Student, George Mason University

Nonparametric Estimation of General Equilibrium Price Vectors

Friday, February 08, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

Agent-based economic modeling often requires the determination of an initial equilibrium price vector. Calculating this directly requires algorithms of exponential computational complexity. It is known that a partial equilibrium price can be estimated using a median of trades. This paper explores the possibility of a multivariate generalization of this technique using depth functions as well as alternative methods.

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
Department of Economic, College of Humanities and Social Sciences
George Mason University

Lifetime/Survival/Reliability/Duration Analysis for Computational Model

Friday, February 15, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

In a variety of computational models, structures arise, evolve, then disappear, perhaps replaced by other, comparable structures. For example, in some economic models firms form from the interactions of agents, operate for some period of time, and then exit. In housing models, households hold mortgages for finite periods of time before replacing them either due to refinancing or moving to a new house. In political (marketing) models the interests of parties (businesses) are aligned with certain segments of voters (consumers) for a period of time, until competition leads to realignment (brand switching). In environmental policy models specific polluting technologies have finite lifetimes and are eventually replaced by cleaner technologies. In disease models people are infected for varying lengths of time based on their health status, policies, etc. Traffic jams and conflicts have finite duration.

In this talk I will review the mathematical and statistical formalisms of lifetime analysis, also known as survival analysis in biostatistics and reliability analysis in engineering, focusing on the concepts most useful for computational models. Specifically while the former field has concerned itself with censored data (e.g., short clinical trials during which not all patient health outcomes can be observed), and the latter has focused on schemes to manage unreliable equipment, in computational modeling we often need to better understand both age and lifetime distributions of objects in our models, typically have large amounts of quasi-exhaustive ‘data,’ normally know some covariates, and usually work in discrete time.

I will go through the inter-relationships between survival, failure rate, and life expectancy functions, using parametric distributions to illustrate the main ideas. Then I will work through an extended example based on data concerning U.S. business firms, focusing on the connections between firm age and lifetime distributions, which ends with somewhat surprising conclusions, due to high failure rates among young firms (high ‘infant mortality’).

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

Douglas A. Samuelson
D.Sc., President and Chief Scientist, InfoLogix, Inc.
and
Russell R. Vane III
Future Planner, National Risk Management Center
Cybersecurity and Infrastructure Security Agency
US Department of Homeland Security

Garbage Cans, Lymph Nodes and Cybersecurity: Modeling Organizational Effectiveness

Friday, February 22, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

We re-examine and extend the well-known “Garbage Can Model” of Cohen, March and Olsen (1972).  They postulated that organizational choice can be well represented by a garbage can into which problems and solutions are thrown randomly.  When, by random mixing, a solution meets a problem, the problem is solved and removed from the venue.  In 2006, Folcik and Orosz presented an agent-based model of a lymph node, into which blood cells bring foreign substances and objects that are then neutralized by specialized immune system cells.  This model led several social scientists, notably Troitzsch (2008), to point out a strong resemblance to the garbage can model, but now adding the recognition that problems require skill sets which some, but not all, solvers possess.  Matching skill sets is critical to effective performance, and providing the right mix of solver skill sets enables the organization to perform effectively and economically.  We suggest ways to apply this approach to integrated man-machine systems intended to enhance information systems security.  One implication is that some approaches currently popular with policy-makers are highly unlikely to work.