Calendar

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Sally Evans, Coordinator
University Dissertation and Thesis Services
Fenwick Library
George Mason University

University Dissertation and Thesis Services:
Here to help you submit your thesis or Dissertation CORRECTLY and ON TIME

University Dissertation and Thesis Services understands that there are many steps in the process toward graduation and it is their goal is to make the process as clear, easy, and stress-free as possible. After Ms. Evans’ presentation, you will have an opportunity to ask questions

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Keith Waters, Ph.D. Candidate
Schar School of Public Policy and Government
George Mason University

Endogenous Region Formation

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

William B. Rouse, Ph.D.
Alexander Crombie Humphreys Chair
School of Systems and Enterprises
Stevens Institute of Technology

Computational Social Science at Several Levels

Friday, December 8, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall

ABSTRACT: Computational social science can enable understanding – and design – of a wide variety of phenomena at an enormous range of levels. This lecture will address processes, organizations, ecosystems, and society for phenomena associated with disease and medicine, health and well being, and technology adoption, particularly in automobiles. The process level is addressed in terms of scaling and optimization of medical innovations. The level of organizations is considered in the context of health provider corporations’ responses to the Affordable Care Act. The ecosystem level is discussed in addressing population health – integrated delivery of health, education, and social services – in the highly fragmented US ecosystem. The society level is considered in terms of the expected disruptive impacts of driverless cars on automotive, insurance, and finance industries. Approaches to and challenges of modeling at these differing levels are discussed.

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Niloofar Jebelli, MAIS-CSS Student
George Mason University

Urban Development Through the Lens of Agent-Based Modeling

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

Abstract:   Cities are ever changing and growing phenomenon with many underlying complexities. Through its life cycle, a city experiences various forms of dynamics. Models allow for a better understanding of such complexities and dynamics. The model presented in this talk simulates the dynamics of certain processes such as: an urban market, agent interactions in that market, urban growth, sprawl and shrinkage and gentrification. The purpose of this model is to understand the behavioral pattern of the agents and demonstrate the life cycle of a city based on individual agents’ actions. This model is significant in its integration of various subsystems creating a larger system while observing developers’ behavior. Specifically, the model explores some well-known issues, including the Smith’s rent-gap theory, Burgess’s concentric zones model of urban growth, and Alonso’s bid rent theory. The main results from the model show that the agents move to and reside in properties within their income range, with similar neighbors. This is one of the first models that provides a new lens to explore urban development.

Faisal Hasan, General Manager, Data and Public Policy, CARFAX

Srinidhi Melkote, Head of Business Analytics CARFAX

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS

Leveling the Playing Field:  Information Asymmetry in the Used Vehicle Buying Process

Monday, January 29, 4:30-5:45
Exploratory Hall, Room 3301

Abstract

In 1970, Economist and Nobel Prize winner (2001) George Akerlof published a study: “The Market for “Lemons”: Quality Uncertainty and the Market Mechanism” (The Quarterly Journal of Economics, Vol. 84, No. 3. (Aug., 1970), pp. 488-500).   In the study, Akerlof attempts to show where, in a market where as seller of a product has more data/information than the buyer of the product, about the product’s quality, will potentially result in “an adverse selection of low-quality products.”   In no place is Akerlof’s theory more represented than in the used vehicle market, where buyers and sellers don’t always have the same information about a vehicle’s quality, potentially resulting in low-quality cars being bought and sold.   In 1986, CARFAX sought to begin to “level the playing field” between buyers and sellers of used vehicles by collecting, analyzing and making relevant data/information available in the marketplace to both buyers and sellers.

• What data does CARFAX collect?
• Why this data vs other data?
• How does the market drive the type of data collected?
• How does CARFAX analyze and make this data available to answer real world questions?

  • Is this car safe?

  • How much should I pay for it?

  • What’s it worth?

  • What’s the risk to insure it?

  • What’s the risk to finance it?

Faisal Hasan is the General Manager, Data & Public Policy at CARFAX.   In his over 17 years at CARFAX, Faisal has been responsible for helping to build CARFAX’s Vehicle History Database through public and private data acquisition efforts across North America, including overcoming legislative and regulatory hurdles to data access. Faisal focuses on CARFAX’s efforts to secure and analyze data to feed the CARFAX “Onetime to Lifetime” Game Plan and develop future CARFAX products. Faisal earned his B.A. in Government & Politics at George Mason University and his M.A. in Government at the Johns Hopkins University. Faisal has been a Fairfax County resident for over 35 years. He is married w/four kids, including a GMU Junior studying Biology and a 2017 GMU Kinesiology graduate.

Sri Melkote is Head of Business Analytics at CARFAX.  Sri has been at CARFAX for 2 years and leads teams responsible for valuation modeling, pricing analytics, marketing research, media measurement and optimization. He has over 12 years of data science experience. Prior to joining CARFAX, Sri developed dynamic pricing systems, personalized recommendation engines and inventory planning systems for the travel industry. He holds a Master’s degree in Mathematics from Purdue University and a Bachelor’s degree in Chemical Engineering from Indian Institute of Technology, Madras. He is the author of several journal articles and a recipient of the 2011 INFORMS Revenue Management and Pricing Practice Award.

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

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

Order without Optimality

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

ABSTRACT:  Long ago Hunter Mill Road in Reston, Virginia, was surrounded by farmland and little travelled. Where it crosses Colvin Run creek a single-lane bridge was adequate for the traffic at the time. However, as the region grew in the post-WWII era the traffic on Hunter Mill Road increased until the single-lane bridge is today a bottleneck for rush-hour traffic, southbound toward the Dulles Toll Road in the morning and northbound in the evening. There are no stop signs on either side of the bridge but warning signs that the bridge is too narrow to handle two vehicles moving in opposite directions at the same time. During periods of low traffic—queue length 0 or 1—the usual first-come-first-served (FCFS) driver discipline is observed at the bridge, by which the first vehicle to approach from either side has the right-of-way. However, during periods of heavy traffic, such as the morning and evening ‘rush hours’, a different behavioral regime is observed: pairs of cars moving in the same direction cross the bridge together, in single file. This departure from the FCFS protocol clearly permits higher vehicular flowrates over the bridge in each direction. However, from the layout of the bridge and vehicular dynamic we demonstrate that the globally optimal behavior for the system during busy times would involve larger numbers of vehicles moving together across the bridge, i.e., groups of 3, 4, or even 5 produce shorter wait times overall, dominating the 2-car norm. We argue that this is an example of a sub- optimal spontaneous order, and go on to suggest that there is little reason to believe that (m)any spontaneous orders operate at anything like peak performance. Essentially, spontaneous orders in this context represent behavioral norms for multi-agent coordination problems that improve on so-called anarchic solutions identified with Nash equilibria of the underlying non-cooperative game. The welfare improvements associated with following the emergent social conventions represent a kind of satisficing solution of the type identified by Herbert Simon as a good description of human behavior in a wide variety of social situations, bounded by cognitive constraints, limited information, and incentive problems. We conclude by arguing that F.A. Hayek implicitly associated his conception of spontaneous order with high welfare, but that this view is untenable. Rather, the more general idea of emergence, which makes no welfare assertions, seems to subsume spontaneous order as a very special case, suggesting that Hayek’s contribution to this area has been largely supplanted by more recent developments in the science of complex systems, as has been argued elsewhere.

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS

Feras A. Batarseh
Research Assistant Professor
Department of Geography and Geoinformation Science
College of Science
George Mason University

Why an open mind on open data can transform our collective intelligence

Monday, February 5,  4:30-5:45
Exploratory Hall, Room 3301

Abstract:  In 1822, the founding father James Madison said: “A popular government, without popular information, or the means of acquiring it, is but a prologue to a farce or a tragedy, or perhaps both”. Recent technological waves have evidently served Madison’s vision of government transparency. The latest advancements in Artificial Intelligence (AI), Data Science, and Machine Learning can make federal data openness a low hanging fruit. Moreover, the big data and open government initiatives (signed in 2012 and 2013) are major enablers for transforming government into a new era of intelligent and data-driven policy making. However, to be able to use data in reforming the political discussion, public federal data needs to devise the promised openness.

Besides benefiting government, Open Data benefits many other domains and applications of data science, such as healthcare, finance, and academia. For example, Open Data could lead to a general openness in science (i.e. Open Science), clearer experimental research, and begin reshaping the human knowledge in general. These topics and other facets will be discussed in this talk.

Bio:  Feras A. Batarseh is a Research Assistant Professor,Department of Geography and Geoinformation Science, College of Science, George Mason University in Fairfax, VA. His research spans the areas of Data Science, Artificial Intelligence, and Context-Aware Software Systems. Dr. Batarseh obtained his Ph.D. and M.Sc. in Computer Engineering from the University of Central Florida (UCF) (2007, 2011), and a Graduate Certificate in Project Leadership from Cornell University (2016). His research work has been published at various prestigious journals and international conferences. Additionally, Dr. Batarseh published and edited several book chapters.

https://www.amazon.com/Federal-Science-Advanced-Analytics-Agricultural/dp/0128124431

Dr. Batarseh has taught data science and software engineering courses at multiple universities including GMU, UCF as well as George Washington University (GWU). Prior to joining GMU, Dr. Batarseh was a Program Manager with the Data Mining and Advanced Analytics team at MicroStrategy, Inc., a global business intelligence corporation based in Tysons Corner, Virginia. During his tenure, he helped several clients make sense of their data and gain insights into improving their operations. For more information on his research, and contact details, please refer to these webpages: http://ferasbatarseh.com/

http://cos.gmu.edu/ggs/people/faculty-staff/feras-batarseh-2/

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Matthew Oldham, CSS PhD Student
Department of Computational and Data Sciences
George Mason University

DRAFTING AGENT-BASED MODELING INTO BASKETBALL ANALYTICS

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

ABSTRACT:  Sports analytics (SA) has experienced a meteoritic rise in recent years, with the trend forecast to continue. Modor Intelligence reports that the market was valued at USD 83.56 million in 2015, and is forecast to grow to USD 447.23 million by 2020. at the market was valued at USD 83.56 million in 2015, and is forecast to grow to USD 447.23 million by 2020.

The growth of sports analytics has raised a rich variety of research topics pertaining to basketball, including: how at the macro level the distribution of scoring activity is a mixture of random walk processes and power-law behavior (Gabel & Redner, 2012), and, at the individual level, the question of whether players develop hot-hands and how the player and their teammates react to its possible existence. While the erroneous belief regarding hot-hands was first identified by Gilovich, Vallone & Tversky (1985) it has remained an active field of research (Bar-Eli, Avugos, & Raab, 2006).

Agent-based modeling (ABM) has great potential to assist and inform those engaged in sports analytics but to date it has not been utilized. The advantage of ABM is that it allows researchers to assess, in a silicon laboratory, the micro-level interactions that give rise to verifiable macro outcomes. This is achieved through heterogeneous agents adapting and making decisions based on their environment, including considering spatial, temporal factors and interactions with other agents.

To support the use of ABM in sports analytics, I will present a 3-dimensional model of a basketball game, where the fundamentals of play including player and court positions, a shot clock, and shooting performance are all included. Additionally, player behavior in deciding whether to shoot, pass or dribble is partially predicated on assessing the length of a player’s shooting streak (designed to test the hot-hand effect), and the consideration they give to any streak, plus their franchise status, a feature identified in Burns (2004). The probabilistic nature of the model allows for insights into the dynamics of scoring actions following a random walk. The model captures extensive data which was used to calibrate and validate it against comparable statistics from the National Basketball Association (NBA).

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS

Michael Eichler
Strategic Planning Advisor
Metro Office of Planning
WMATA

Metrorail and Metrobus, Data Sources and Information Needs

Monday, February 12,  4:30-5:45
Exploratory Hall, Room 3301

Abstract:  Modernization of nearly all the technology that underlies the provision of rail and bus transit service over the past 30 years has resulted in a vast amount of data that until recently has been more or less neglected.  Meanwhile, challenges that face rail and bus transit systems continue to mount, from maintaining a state of good repair to capturing and keeping riders in the age of Uber/Lyft and bike share.  The key to providing safe, convenient, affordable, and reliable transit service into the next century lies in the hands of data scientists and policy analysts.  This talk will review the different data-generating technologies and the types of data they create, followed by an exploration of the pressing issues faced by transit agencies and the questions begging for answers. 

Bio:    Michael currently serves as Strategic Planning Advisor at WMATA in the Office of Planning’s Applied Planning Intelligence unit, where he focuses on transforming data into information to help inform policy and planning decisions.  He currently focuses on fare policy, crowding, GTFS data and online tools, and customer-focused performance metrics.  Before joining WMATA in 2010, he worked for Oracle Corporation, an IT start-up, and the Metropolitan Washington Council of Governments.   He holds a BS in Systems Analysis and Engineering from The George Washington University, and masters in City and Regional Planning and Transportation Engineering from UC Berkeley. 

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS

 Sean Mallon, Associate Vice President
Entrepreneurship and Innovation
George Mason University
and
Eric Koefoot
Founder and CEO of PublicRelay

The Journey and Stories of a Data Science Entrepreneur

Monday, February 19, 4:30-5:45
Exploratory Hall, Room 3301

This session will feature conversation between Sean Mallon, Mason’s AVP for Entrepreneurship and Innovation, and Eric Koefoot, founder and CEO of PublicRelay, a venture-backed data analytics and media intelligence startup based in McLean, VA. During the discussion we will explore a wide range of topics, ranging from what inspired the initial business idea, to customer discovery, to product development challenges, to fundraising, to customer acquisition strategies, and much more. This will be a highly interactive seminar and participants are encouraged to come with questions and personal experiences to share.

Sean Mallon, Associate Vice President, Entrepreneurship and Innovation, Office of the Provost. Photo by Ron Aira/Creative Services/George Mason University

Sean Mallon Bio: Sean Mallon is Mason’s Associate Vice President for Entrepreneurship and Innovation. Before joining Mason in 2016, Sean spent many years as an entrepreneur and early-stage technology investor. Sean hold a Bachelor’s in History from Princeton and an MBA from the Wharton School of the University of Pennsylvania.

Eric Koefoot Bio: Formerly the CEO of U.S. News Ventures, CEO at Five Star Alliance, CFO and later VP Global Sales at Washington Post Digital, Eric is the founder and CEO of PublicRelay and brings substantial media experience and understanding. Eric holds a Bachelor’s in Engineering from MIT and an MBA from the Sloan School at MIT.