Calendar

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.

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

Craig Yu
PhD, Computer Science, UCLA 2013
Assistant Professor, Department of Computer Science
Volgenau School of Engineering, George Mason University

Synthesizing Human-centric Architectural Layouts
via Affordance Analysis and Crowd Simulations

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

All are welcome to attend.

Abstract: 

In this talk, I will discuss the recent progress of my team in devising computational design approaches for automatically generating human-centered architectural layouts for real-world design and virtual reality applications. For example, I will talk about the state-of-the-art procedural modeling techniques for generating large-scale architectural layouts that are optimized with respect to human navigation properties; and techniques for automatically generating interior designs for furnishing indoor scenes with furniture objects. In particular, I will discuss how human intentions and functionality considerations can be employed as the key criteria in generating 3D worlds. I will also discuss how human perceptual data tracked from virtual reality can be employed for creating personalized workspace design and for VR training.

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

Cody Buntain
Post Doctoral Researcher
New York University’s Social Media and Political Participation Lab

#pray4victims: Consistencies In Response to and Automatically
Identifying Diverse Information Needs During Disasters on Twitter

Friday, March 08, 2019, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

This talk presents commonalities in response across disasters in online social networks (OSNs) and Twitter specifically.

Brief Bio:

Cody Buntain received his PhD from the Computer Science Department at the University of Maryland and is a postdoctoral researcher with New York University’s Social Media and Political Participation Lab. His primary research areas apply large-scale computational methods to social media and other online content, specifically studying how individuals engage socially and politically and respond to crises and disaster in online spaces. Current problems he is studying include cross-platform information flows, network structures, temporal evolution/politicization of topics, misinformation, polarization, and information quality. Recent publications include papers on influencing credibility assessment in social media, consistencies in social media’s response to crises, the disability community’s use of social networks for political participation, and characterizing gender and direction in online harassment.

COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES RESEARCH

Hilton Root
Professor, Schar School of Policy and Government
George Mason University
and
Qing Tian
Assistant Professor, Computational and Data Sciences
George Mason University

Network Foundations of the Great Divergence

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

All are welcome to attend.

Abstract:

This paper uses a complex systems approach and network analytics to enrich our understanding of innovation systems in historical regimes causing the Great Divergence of East and West. In our analysis decentralization and competition between the European states and a unified China with Confucianism as the state philosophy provides the background for actors in the two systems to act and interact. We argue that Europe’s decentralized network topology facilitated innovations, while that of the centralized Confucian-state stifled innovations. We also show that in each, despite the motivation of the ruling class to strengthen its own power, its actions had unintended consequences that ultimately led to the destruction of the system.

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.

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).

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: