Calendar

Dates for the 2017-18 Academic Year

September 27th
October 10th
October 25th
November 14th [Cancelled] November 29th
December 12th
January 16th
January 31st
February 13th
February 28th
March 20th
April 3rd
April 18th
May 8th
May 23rd

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.

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/

Feras Batarseh

College of Science Research Exchange is held on Fridays throughout the 2017-18 academic year. Limited seating is available. Please register below.

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. 

October 10th
November 14th
Tuesday, December 12th
January 9th
February 13th
March 13th
April 10th
May 8th
June 12th

Dates for the 2017-18 Academic Year

September 27th
October 10th
October 25th
November 14th [Cancelled] November 29th
December 12th
January 16th
January 31st
February 13th
February 28th
March 20th
April 3rd
April 18th
May 8th
May 23rd

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR
Hilton Root, Professor
Schar School of Policy and Government
George Mason University

Networks and Change in Economic History
Friday, February 16, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall
ABSTRACT:  Prof. Root, a frequent visitor to the social complexity seminar, would like to discuss the conceptual framework of his forthcoming book Networks and Change in Economic History. He explores what the great transitions in human social organization share with other complex systems.
To characterize the essential properties of great transitions in history—those that fashion the fundamental institutions for economic growth—the book considers the economy as a network of networks in which the probability of extreme events increases as systems become more interconnected.
The book is divided into three distinct sections. The first advances theory and includes Chapter 1, which discusses the creation of global order; Chapter 2, which addresses the connection of economic history with other sciences, especially with study of complex systems; and Chapter 3, which connects economic history, complexity, and the study of human behavior. It is chapter 2 that he would like to discuss. The key concepts of this section are the juxtapositions, coexistence, and complementarities of order and disorder, and of closed systems within an open universe; the irreversibility of time; and the networked interdependence of different levels of social order. Chapter 2 is attached.
The Table of Contents is below. Interested seminar participants, please contact Prof. Root (hroot2@gmu.edu) for copies of other chapters
Introductory Remarks: Great Transitions in Economic History
Chapter One:            Five Transitions and the Formation of Social Order
Chapter Two:            Growth, Form, and Self-Organization in the Economy
Chapter Three:         Human Evolutionary Behavior and Political Economy
Chapter Four:           Network Dynamics of Institutional Formation in Europe and China
Chapter Five:            Why the Western Legal Tradition Is Not Replicable (with Cameron Harwick)
Chapter Six:             Disruptive Innovation and Networks in World History
Chapter Seven:         How China and the U.S. Think Differently About Markets, and Why It Matters for Globalization (with Baocheng Liu) 
Chapter Eight:          Global Networks Over Time: Where We Are in the History of Globalization (with Kevin Comer, Jack Goldstone and David Masad
Chapter Nine:           Will Growing Complexity Bring Diminishing Returns or Massive                          
Transformation?
Conclusion:             Can Western Nations Manage the Transformations Underway?
 
BIO:  Dr. Hilton Root is a policy specialist in international political economy and development, and a member of the faculty at the George Mason University Schar School of Policy and Government. His current research examines three related areas: (1) global power transition and the challenge of legitimacy; (2) the comparative and historical dynamics of state-building; and (3) the use of complexity models to understand the evolution of social institutions.