Calendar

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR
Andrew Crooks, Associate Professor
Computational Social Science
Department of Computational and Data Sciences
George Mason University
ABM for Simulating Spatial Systems: How are we doing?
Friday, September 22,  3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall
 
While great advances in modeling have been made, one of the greatest challenges we face is that of understanding human behavior and how people perceive and behave in physical spaces. Can new sources of data (i.e. “big data”) be used to explore the connections between people and places? 
In this presentation,  I will review the current state of art of modeling geographical systems.  I will  highlight the challenges and opportunities through a series of examples that new data can be used to better understand and simulate how individuals behave within geographical systems.

COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR
 

Lauren Deason, PhD
Lead Data Scientist
Punch Cyber Analytics Group

Detecting Automated and Coordinated Activity in Cyber Logs at Scale

Monday, September 25, 4:30-5:45
Exploratory Hall, Room 3301
ABSTRACT: This presentation will detail novel analytic methods for processing time series data at scale using techniques drawn from digital signal processing and document matching. These methods can be applied to detect coordinated and automated cyber activity, to match patterns present in time series data, and to fuse together disparate datasets.
Dr. Deason holds a BS in Applied Mathematics from the University of Virginia, a MA in Mathematics with an emphasis in Real Analysis and Probability Theory from UC Berkeley, and a PhD in Economics from the University of Maryland, College Park. Dr. Deason has 10 years of experience in mathematical modeling and data science, spanning employment as a Professor of Mathematics, an Economist, and a Data Scientist. Dr. Deason’s past experience includes developing dynamic stochastic models within a game theoretic framework to explore the effects of trade policy uncertainty as well as estimating empirical models to explain various phenomena. More recently, Dr. Deason has developed multiple algorithms for detecting and classifying periodic and coordinated behavior in a variety of contexts on large data sets as part of DARPA’s Network Defense Program.
DEASON_gmu_pres
 

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

Join the College of Science Marketing and Communications team to get started, or refresh your skills, in editing on the College of Science Web Network.

We ask that you register, below, so that you have a COS Web Network login prior to the start of the training session. This is a hands-on learning session so you will receive a login if you do not already have one.

Computational Social Science
Robert Axtell, PhD
Computational Social Science Program, Department of Computational and Data Sciences,
College of Science
Department of Economics, College of Humanities and Social Sciences
Krasnow Institute for Advanced Study
George Mason University
Co-Director
Computational Public Policy Lab
Krasnow Institute for Advanced Study and Schar School of Policy and Government
External Professor, Santa Fe Institute (santafe.edu)
External Faculty, Northwestern Institute on Complex Systems (nico.northwestern.edu)
Scientific Advisory Council, Waterloo Institute for Complexity and Innovation (wici.ca)
Getting Younger by Growing Older: U.S. Firms Gain Longevity as they Age
Friday,  September 29, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall
 
Abstract:  Using data on the entire population of American firms I will first show that the distribution of firm ages is approximately stationary, with small ‘defects’ arising at the start of last decade’s Financial Crisis now propagating through the distribution. From these data I will derive the distribution of U.S. firm lifetimes and demonstrate that it has a specific structure that conforms to economists’ intuitions about new (and small) firms having higher failure probabilities than older (and larger) firms. I will then demonstrate that the large body of statistical theory on ‘survival analysis’ is directly applicable to firms. Specifically, I will focus on firm hazard functions and empirically show that for the U.S. this is a power law over a wide range of ages, ostensibly a new finding, declining with age. This permits computation of the expected remaining lifetime of firms as a function of their age, an INCREASING function, implying that American firms gain longevity as they get older, a very non-biological type of aging. Conditioning on firm size produces further results. Specifically, using the Cox ‘proportional hazards’ specification, the reduction in failure probability associated with larger size is quantified. At the end I will demonstrate that an ABM of firm dynamics can be calibrated to reproduce all of these features of U.S. firms.
Rob Axtell earned an interdisciplinary Ph.D. degree at Carnegie Mellon University, where he studied computing, social science, and public policy. His teaching and research involves computational and mathematical modeling of social and economic processes. Specifically, he works at the intersection of multi-agent systems computer science and the social sciences, building so-called agent-based models for a variety of market and non-market phenomena.
His research has been published in the leading scientific journals, including Science and the Proceedings of the National Academy of Sciences, USA, and reprised in Nature, and has appeared in top disciplinary journals (e.g., American Economic Review, Computational and Mathematical Organization Theory, Economic Journal), in general interest journals (e.g., PLOS One) and in specialty journals (e.g., Journal of Regulatory Economics, Technology Forecasting and Social Change.) His research has been supported by American philanthropies (e.g., John D. and Catherine T. MacArthur Foundation, Institute for New Economic Thinking) and government organizations (e.g., National Science Foundation, Department of Defense, Small Business Administration, Office of Naval Research, Environmental Protection Agency). Stories about his research have appeared in major magazines (e.g., Economist, Atlantic Monthly, Scientific American, New Yorker, Discover, Wired, New Scientist, Technology Review, Forbes, Harvard Business Review, Science News, Chronicle of Higher Education, Byte, Le Temps Strategique) and newspapers (e.g., Wall St. Journal, Washington Post, Los Angeles Times, Boston Globe, Detroit Free Press, Financial Times). He is co-author of Growing Artificial Societies: Social Science from the Bottom Up (MIT Press) with J.M. Epstein, widely cited as an example of how to apply modern computing to the analysis of social and economic phenomena.

COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR
Kirk Borne, PhD
Principal Data Scientist and an Executive Advisor
Booz Allen Hamilton
Smart Data for Smart Science
Monday,  October 2, 4:30-5:45
Exploratory Hall, Room 3301
ABSTRACT:  Smart data are essential when faced with massive-scale data collections. “Smart” refers to data that are tagged or indexed with meaning-filled metadata that carry information about the semantic meaning of the data, its applications, use cases, content, context, and more. Such meta-tags enable efficient and effective discovery, description, and delivery of the right data at the right time, both to humans and to automatic processes.
Dr. Borne advises and consults with numerous organizations, agencies, and partners in the use of data and analytics for discovery, decision support, and innovation. Previously, he was Professor at George Mason University (GMU) for 12 years in the CSI and CDS programs, where he did research, taught, and advised students in data science. Prior to that, Dr. Borne spent nearly 20 years supporting data systems activities on NASA space science research programs, including a role as NASA’s Data Archive Project Scientist for the Hubble Space Telescope.
Recently, Dr. Borne was ranked #2 worldwide among all Big Data experts to follow.  http://ipfconline.fr/blog/2017/05/22/fine-list-of-50-top-world-big-data-experts-to-follow-in-2017-with-moz-social-score/

Commonwealth at Risk: Mason’s Weather and Climate Research Helps Virginia Predict and Prepare for the New Normal

James L. Kinter

Director, The Center for Ocean-Land-Atmosphere Studies
Professor of Climate Dynamics, George Mason University
Chair, Department of Atmospheric, Oceanic, and Earth Sciences
http://cola.gmu.edu/kinter/

Everybody talks about the weather, but nobody does anything about it. This old saying neglects the fact that everybody is inadvertently doing something about the weather – we are changing it. The reality of global climate change is now undeniable and the likelihood is high that human activities have caused the majority of the changes observed in the past half-century. The citizens and businesses of the Commonwealth of Virginia are not exempt from the changes Earth will experience during the rest of this century – there will be a new normal and we all need to prepare for it. The most obvious impacts of our changing climate on Virginia are already evident in the Tidewater region, with rising sea levels increasing nuisance floods and exacerbating the effects of tropical storms that infrequently reach the shores of the Chesapeake.

Less immediate, but potentially more damaging are the relentlessly hot summers we can expect by mid- to late-21^st century. And there will be impacts on our economy as the coal mining industry dwindles and Virginia businesses begin to exploit our sun and wind resources to generate renewable energy. Knowing where, when, and by how much climate will change in the Commonwealth, and where and how to exploit natural resources, are critically important aspects of planning and preparing for changes that are decades in the making. Prof. Kinter will describe Mason research that is focused on this question, emphasizing what we have learned about predicting future weather and climate in the mid-Atlantic region and what remains to be done to advance our understanding to actionable predictions.

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How do I get there? View directions and parking information on the Center for the Arts website.

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR
Xiaoyi Yuan, PhD Student
Computational Social Science Program
Department of Computational and Data Sciences
George Mason University
Quantifying the Social Debates of Anti-Vaccination on Twitter
Friday, October 6, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall
 
Measles is one of the leading causes of death among young children. In many developed countries with high measles, mumps, and rubella (MMR) vaccine coverage, measles outbreaks still happen each year. Social media has been one of the dominant information sources to gain vaccination knowledge and thus has also been the focus of the “anti-vaccine movement”. This talk is about two of my recent research projects on this topic. The first one will be introduced briefly, which is an agent-based model demonstrating how a small amount of online anti-vaccine sentiment could have the power of increasing the probability of measles outbreaks significantly. This research inspired me to investigate details of communicative pattern of “anti-vacciners” by analyzing a large twitter dataset (660892 tweets) after the California Disneyland measles outbreak in 2015. This second research has two main parts: first, in order to identify “anti-vacciners”, I used supervised learning to label each tweet as either positive, neutral, or negative opinion towards vaccination. The linear support vector machine model shows good performance on this dataset with an accuracy score of 72% on test data. Second, Louvain’s method for community detection of the retweet network shows the common pattern of social media communities; i.e., overall fragmented but with a few large communities. By investigating the opinion distribution in big communities, however, I discovered that they are highly overlapped, especially within “anti-vacciners”, meaning that they have more frequent communication within their own opinion group than with others. What’s useful for health communication strategies is to look further into the brokers–those who stand between two or more communities. At the end of the talk, I will address details of analyzing the brokerage as well.