Calendar
During the fall 2018 semester, the Computational Social Science (CSS) and the Computational Sciences and Informatics (CSI) Programs have merged their seminar/colloquium series where students, faculty and guest speakers present their latest research. These seminars are free and are open to the public. This series takes place on Fridays from 3-4:30 in Center for Social Complexity Suite which is located on the third floor of Research Hall.
If you would like to join the seminar mailing list please email Karen Underwood.
COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR
Peter Revay, Ph.D. Candidate
Computational Social Science Program
Department of Computational and Data Sciences
George Mason University
Modeling the Co-Evolution of Culture, Signs and Network Structure: Theory and Applications
Friday, October 20,3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall
ABSTRACT: I focus on the drivers of diffusion and adoption of cultural traits, such as values, beliefs, and behaviors. I adopt an evolutionary view of cultural dynamics. I use concepts from dual-inheritance theories of cultural evolution to develop and test an agent-based model capable of simulating the changing distributions of cultural traits in a large population of actors over the course of prolonged periods of time. Particularly, I pay close attention to the mechanisms of indirectly biased transmission of traits and guided variation, which are both hypothesized to be significant aspects of cultural dynamics. Indirectly biased transmission consists of the adoption of specific trait variants on the basis of possession of initially unrelated external markers. Guided variation is then individual adaptation driven by self-exploration.
Furthermore, I make use of large publicly available datasets to validate my models. The first one of these is the database of bill co-authorship in the U.S. House of Representatives from 1973 to 2008. The other is a comprehensive dataset of scientific co-authorship in various disciplines stretching back for over a century.
The results show that cultural evolution models based on indirectly biased transmission and guided variation are suitable to explaining the dynamics of various complex social networks.
COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR
Bartley Richardson, PhD
Sotera Defense Solutions
Representation of Cyber Knowledge as Discrete Sequences
Monday, October 23, 4:30-5:45
Exploratory Hall, Room 3301
Abstract: As devices continue to gain network connectivity and require less interactivity from human operators, the nature of network transmissions is shifting and data is being created faster and with more fidelity than ever before. One way to view this data is in the context of a cyber language, analogous but semantically/syntactically different than a natural language. After sequences are constructed over a large dataset (PB), unsupervised machine learning and deep learning techniques are used to model communication, identifying typical behavior and flagging unlikely events. This seminar presents context for the foundations of this new approach to cyber anomaly detection as well as the enabling analytic techniques.
Dr. Richardson has nearly a decade of experience in Data Science, Cloud Computing, Software Development, and Machine Learning. He has served as both Department Chair and Visiting Professor at two universities and has published over 10 articles. He is currently serving as a principal data scientist, technical lead, and principal investigator on multiple government sponsored projects, including one DARPA research program.
COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR
Dale Brearcliffe, MAIS-CSS student
Computational Social Science Program
Department of Computational and Data Sciences
George Mason University
Parallelization of Entity-Based Models in Computational Social Science: A Hardware Perspective
Friday, October 27,3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall
ABSTRACT: The use of simulations in exploring theories and hypotheses by social scientists is well documented. As computer systems have grown in capacity, so have interests by social scientists in executing larger simulations. Social scientists often approach their simulation design from the top down by selecting an Entity-Based Model (EBM) framework from those that are readily available, thus limiting modeling capability to the chosen framework. Ultimately, the framework is dependent upon what is at the bottom, the hardware that serves as the foundation of the computing system. One underused hardware architecture supports the simultaneous execution of a problem split into multiple pieces. Thus, the problem is solved faster in parallel. In this seminar, a selection of parallel hardware architectures is examined with a goal of providing support for EBMs. The hardware’s capability to support parallelization of EBMs is described and contrasted. A simple EBM is tested to illustrate these capabilities and implementation challenges specific to parallel hardware are explored.
COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR
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)
Ages and Lifetimes of U.S. Firms: Why Businesses Should NOT be Treated Like People
Monday, October 30, 4:30-5:45
Exploratory Hall, Room 3301
ABSTRACT: Over the last 150 years American corporations have acquired many rights associated with individual citizens, such as free speech, the ability to make campaign contributions, and so on. In this talk I will quantify the age-related demographic properties of U.S. business firms and argue that the peculiar nature of firm aging suggests that businesses are very much unlike individual people. Specifically, using data on all 6 million U.S. firms having employees, I document that firm ages are discrete Weibull-distributed while firm lifetimes follow a closely-related distribution. Further, the hazard rates associated with firm survival are monotone declining according to a power law. From this the expected remaining lifetime can be computed and it will be demonstrated that this INCREASES as firms age. Specifically, while a new firm in the U.S. can expect to live for about 15 years, a firm that has survived 50 years can expect to live for 30 more. Finally, conditioning on firm size leads to even more extreme results: increasing firm size by a decade cuts the hazard rate in half. In sum these results suggest that firm aging is very different from biological aging and makes analogies between firms and people both quantitatively inaccurate and qualitatively wrong-headed. Technically, this talk will focus on the application of conventional demographic techniques to economic and financial data, including failure/survival analysis with censored data.
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
Shane Frasier, Ph.D.
Department of Homeland Security
Data Science and Cybersecurity at the Department of Homeland Security
Monday, TBA, 4:30-5:45
Exploratory Hall, Room 3301
ABSTRACT: Among its many responsibilities, the Department of Homeland Security works to improve the security of the computer networks of the federal government and our nation’s critical infrastructure. This will be a discussion of some of the ways in which that is done, and some of the ways in which data science can contribute to that goal.
COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR
Nathan M. Palmer, Ph.D. Candidate
Computational Social Science Program
Department of Computational and Data Sciences
George Mason University
A Simple Direct Estimate of Rule-of-Thumb Consumption using the Method of Simulated Quantiles and Cross Validation
Friday, November 3, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall
Campbell and Mankiw (1989, 1990) famously demonstrated that aggregate data supported a model of household consumption in which roughly 50% of agents followed an optimizing strategy while the other 50% followed a “rule of thumb” strategy, consuming their current income. This paper revisits that hypothesis using structural, micro-level, semi-parametric estimation and formally selecting between different models of agent behavior. I find strong evidence supporting a generalization of Campbell and Mankiw (1989, 1990)’s original conclusion: roughly 50% of the population behaves in a way similar to “rule of thumb” consumers, even when the data is allowed to dictate how severe that rule of thumb behavior is. In addition, this paper demonstrates the usefulness and flexibility of both the Method of Simulated Quantiles and K-fold cross validation for selecting between of agent behavior. This type of model selection is crucial for creating agents to populate robust, richly-featured agent-based models of macroprudential and macro-financial systems.
COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR
Jason M. Kinser, D.Sc.,
Chair Computational & Data Sciences
George Mason University
Image Operators – A World Premiere
Monday, November 6, 4:30-5:45
Exploratory Hall, Room 3301
ABSTRACT: The onslaught of digital detectors has created the ability to capture massive amounts of image data. Analysis techniques have been maturing for decades, but this new flood of image data will tax the foundations of information dissemination. Published descriptions of the image processes often consume much more real estate than does the scripts required to execute the processes. Furthermore, many published descriptions are imprecise. This talk will preview a new mathematical language solely dedicated to the fields of image processing and analysis. This language is coincident with implementations in Python and Matlab, thus there is a one-to-one correspondence between mathematical description and computer execution. This talk will present several examples and culminate with an interactive analysis of image processing protocols.
COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR
Benjamin J. Radford, PhD
Principal Data Scientist
Sotera Defense Solutions
Clustering Techniques for Unsupervised Machine Learning
Monday, November 13, 4:30-5:45
Exploratory Hall, Room 3301
Abstract: Cluster analysis represents a broad class of unsupervised algorithms that are applicable to a variety of data science problems. An overview of some clustering models is provided and example use cases for clustering are discussed. Multivariate Gaussian mixture models are then discussed in detail and estimation techniques are outlined. K-selection is also discussed in the context of Gaussian mixture models. The talk concludes with a short discussion about how clustering techniques might be used in the context of cybersecurity.
Dr. Radford is a Principal Data Scientist at Sotera Defense Solutions where he works on data-driven cybersecurity research programs for the Department of Defense. He received his Ph.D. in political science from Duke University in 2016. His research interests include political methodology, security and political conflict, the political implications of cyberspace, and automated event data coding. Dr. Radford’s dissertation demonstrated the semi-automated population of dictionaries for event-coding in novel domains. He is also an avid guitarist.
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
