Calendar

Oral Defense of Doctoral Dissertation – Computational Social Science – Standardizing Complexity: Doctrine and Computation for Integrated Campaigning – Thomas Dietrich Pike @ Research Hall, Room 92, Fairfax Campus

Jun 21 @ 1:00 pm

Notice and Invitation
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Social Science
Department of Computational and Data Sciences
College of Science
George Mason University

Thomas Dietrich Pike
Bachelor of Arts, University of Arizona, 1999
Master of Arts, American Military University, 2009
Master of Science, National Intelligence University, 2010

Standardizing Complexity: Doctrine and Computation for Integrated Campaigning

Friday, June 21, 2019, 1:00 a.m.
Research Hall, Room 92

All are invited to attend.

Committee
Robert Axtell, Chair
Patrick Gillevet
William G. Kennedy

This dissertation examines the integration of complexity theory and computational tools into U.S. foreign policy. It identifies ways to improve the Department of Defense’s main analytic framework to ensure a more accurate reflection of complex systems and it provides a holistic assessment of the integration of computational tools into Joint campaigns. Based on this analysis, this dissertation advocates the incorporation of Agent Based Models (ABMs) as simulations to support both analysis and foreign policy development at all levels of the foreign policy enterprise. To aid this integration two Mesa based ABM libraries are provided. (1) Multi-level Mesa, the first Python based multi-level library to facilitate the integration and evolution of layered adaptive networks. This library goes beyond existing multi-level libraries by providing greater user flexibility and allowing for the integration and adaption of more complex networks. (2) Distributed Space Mesa, a first attempt at starting a Distributed Mesa meta-library. This library provides modest time improvements to spatial Mesa ABMs and critical lessons for the continued development of a suite of distributed Mesa libraries.

Categories: defense

RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES – Massive-Scale Models of Urban Infrastructure and Populations – Ben Intoy Full Stack Developer and Dan Baeder Data Scientist, Deloitte Consulting LLP @ CENTER FOR SOCIAL COMPLEXITY SUITE, 3RD FLOOR, RESEARCH HALL

Sep 6 @ 3:00 pm

RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES

Ben Intoy
Full Stack Developer

Dan Baeder
Data Scientist

Deloitte Consulting LLP

Massive-Scale Models of Urban Infrastructure and Populations

Friday, September 06, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

As the world becomes more dense, connected, and complex, it is increasingly difficult to answer “what-if” questions about our cities and populations. Most modeling and simulation tools struggle with scale and connectivity. We present a new method for creating digital twin simulations of city infrastructure and populations from open source and commercial data. We transform cellular location data into activity patterns for synthetic agents and use geospatial data to create the infrastructure and world in which these agents interact. We then leverage technologies and techniques intended for massive online gaming to create 1:1 scale simulations to answer these “what-if” questions about the future.

Bios:

Ben Intoy is a full stack developer at Deloitte Consulting LLP. He received his PhD in Physics at Virginia Tech in 2015 where he used high throughput computing simulations to study stability properties of cyclically competing species in varying spatial dimensions. Ben then went to the University of Minnesota, Twin Cities campus, as a postdoctoral research associate where he used tools he learned in his PhD to abstractly study the origin of life on earth and the probability of finding life elsewhere in the universe. In fall 2018 Ben went to the Deloitte Arlington VA Office to work on the FutureScape project (www.futurescape.ai).

Dan Baeder is a data scientist at Deloitte Consulting LLP, and has been on the FutureScape project since joining the firm last year. While at Deloitte, Dan has focused on the use of cellular phone geolocation data for the development of synthetic traffic models, as well as the application of geospatial analysis techniques to human behavior modeling. He is a noted R-phile in a sea of Python users. Dan received an MS in Public Policy and Management with a focus on data analytics from Carnegie Mellon University in 2018.

Categories: CSS/CSI COLLOQUIA

RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES – Kim McEligot – Sea Bright, NJ Reconstructed: Agent-Based Protection Theory Model Responses to Hurricane Sandy @ CENTER FOR SOCIAL COMPLEXITY SUITE, 3RD FLOOR, RESEARCH HALL

Sep 13 @ 3:00 pm – 4:00 pm

Research Colloquium on Computational Social Science/Data Science

Kim McEligot
PhD Candidate, Department of Systems Engineering and Operations Research
George Mason University

Sea Bright, NJ Reconstructed:
Agent-Based Protection Theory Model Responses to Hurricane Sandy

Friday, September 13, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

Coastal flooding is the most expensive type of natural disaster in the United States. Policy initiatives to mitigate the effects of these events are dependent upon understanding flood victim responses at an individual and municipal level. Agent-Based Modeling (ABM) is an effective tool for analyzing community-wide responses to natural disaster, but the quality of the ABM’s performance is often challenging to determine. This paper discusses the complexity of the Protective Action Decision Model (PADM) and Protection Motivation Theory (PMT) for human decision making regarding hazard mitigations. A combined (PADM/PMT) model is developed and integrated into the MASON modeling framework. The ABM implements a hind-cast of Hurricane Sandy’s damage to Sea Bright, NJ and homeowner post-flood reconstruction decisions. It is validated against damage assessments and post-storm surveys. The contribution of socio-economic factors and built environment on model performance is also addressed and suggests that mitigation for townhouse communities will be challenging.

Bio:

KIM McELIGOT is a PhD candidate in the Department of Systems Engineering and Operations Research at the George Mason University. His research interests include federation of computational fluid dynamics coastal flood modeling with geo-spatial agent-based modeling for individual and community level flood mitigation policy analysis. He holds an M.S. in Systems Engineering from Johns Hopkins University, and an M.A. in National Security and Military Affairs from the U.S. Naval War College. His email address is kmceligo@masonlive.gmu.edu.

Categories: CSS/CSI COLLOQUIA

RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES – Vadim Sokolov – Dimensionality Reduction for Agent Based Models @ CENTER FOR SOCIAL COMPLEXITY SUITE, 3RD FLOOR, RESEARCH HALL

Sep 20 @ 3:00 pm – 4:00 pm

Research Colloquium on Computational Social Science/Data Science

Vadim Sokolov
Assistant Professor
Department of Systems Engineering and Operations Research
George Mason University

Dimensionality Reduction for Agent Based Models

Friday, September 20, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

Bayesian algorithms such as Markov Chain Monte Carlo or Bayesian optimization can quickly become computationally prohibitive or even infeasible for high dimensional ABM problems. In many applications, however, the underlying dynamics of an ABM typically can be represented in a lower dimensional space. We will review existing linear and nonlinear dimensionality reduction methods, such as Laplacian eigenmaps and restricted Boltzmann machines. Further, we will present some new results for nonlinear dimensionality techniques based on deep learning models. We will demonstrate our approach in the context of Bayesian optimization algorithms applied to a transportation agent-based model. Finally, we discuss directions for future research.

Bio:

Vadim Sokolov is an assistant professor in the Systems Engineering and Operations Research Department at George Mason University. He works on building robust solutions for large scale complex system analysis, at the interface of simulation-based modeling and statistics. This involves, developing new methodologies that rely on deep learning, Bayesian analysis of time series data, design of computational experiments and development of open-source software that implements those methodologies. Inspired by an interest in urban systems he co-developed mobility simulator called Polaris that is currently used for large scale transportation networks analysis by both local and federal governments. Prior to joining GMU he was a principal computational scientist at Argonne National Laboratory, a fellow at the Computation Institute at the University of Chicago and lecturer at the Master of Science in Analytics program at the University of Chicago.

Categories: CSS/CSI COLLOQUIA

RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES – AnaMaria Barea – Exploring what is universally possible for life with a wealth of computational methods @ CENTER FOR SOCIAL COMPLEXITY SUITE, 3RD FLOOR, RESEARCH HALL

Sep 27 @ 3:00 pm – 4:00 pm

Research Colloquium on Computational Social Science/Data Science

AnaMaria Berea, PhD
Blue Marble Space Institute of Science Research Scientist
Visiting Research Assistant Professor
University of Central Florida

Exploring what is universally possible for life with a wealth of computational methods

Friday, September 27, 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 advances made at NASA/SETI Frontier Development Lab AI Research Accelerator with respect to understanding the coevolution of biospheres and atmospheres, from cellular metabolisms to planetary environments. The current challenge presented by the sample of 1 when we consider the evidence of life and living systems in the Universe is overcome by a series of computer simulations and simulated data that give enough potential combinations of stable biospheres to be explored by machine learning algorithms. While we are only scratching the surface now with respect to the promise and perils of constructing well informed simulations and feeding them into more advanced algorithms, this approach will hopefully help identify possible life on observable exoplanets identified in the Kepler and TESS data. Methodologically, we explored Monte Carlo simulations, agent-based models, machine learning and dockerization in a complex data architecture supported by Google, in order to simulate more than 250,000 stable atmospheres, 500 types of synthetic genomes (metabolic networks) and more than 37,700,000 chemical reactions.

Bio:

Anamaria Berea has a dual PhD in economics (2010) and computational social science (2012). Her research is focused on the emergence of communication in biological and social networks, by applying theories and methods from economics, complex systems and information theory to understand fundamental aspects of communication from cells to societies. Ms. Berea was one of the data scientists part of the NASA/SETI Frontier Development Lab (2017) in the heliophysics team and a data science mentor for the astrobiology team in the following year. She is the author of the book “Emergence of Communication in Socio-Biological Networks”, Springer, 2018. She is currently a Blue Marble Space Institute of Science research scientist and a Visiting Research Assistant Professor with the Complex Adaptive Systems Lab at the University of Central Florida.

Categories: CSS/CSI COLLOQUIA

RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES – Amira Al-Khulaidy – Corruption and the effects of influence within social networks: An agent-based model of the “Lava Jato” scandal @ CENTER FOR SOCIAL COMPLEXITY SUITE, 3RD FLOOR, RESEARCH HALL

Oct 4 @ 3:00 pm – 4:00 pm

Research Colloquium on Computational Social Science/Data Science

Amira Al-Khulaidy
with Valentin Vergara
CSS PhD Students
George Mason University

Corruption and the effects of influence within social networks: An agent-based model of the “Lava Jato” scandal.

Friday, October 04, 2019 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

Corruption, and more specifically corruption in Latin America, is a complex phenomenon which is affected by politics, social structures and institutions, as well as individual behaviors. The Lava Jato is a large-scale example of corruption in Brazil. As of early 2019, there is still an ongoing investigation surrounding what has been lauded as the largest corruption scheme in Latin America. Advances in data analysis, computation, and social networks have allowed progress to be made with these types of investigations. The Lava Jato case has been a clear example of how breaking up social networks and understanding the extent of crime and individual corruption has revealed webs of corruption that have influenced politics, as well as hindered economic developed in Brazil. The several layers of interactions between individuals and institutions can be difficult to grasp and understanding the patterns and relationships within complex large-scale phenomena, such as corruption can seem impossible. Agent-based models can help with understanding these complex behaviors and systems. By capturing the patterns and gaining a better understanding of how corruption emerges and is manifested, we can help inform policy, as well as create better tools and methods for crime prevention and detection.

Bio:

Amira Al-Khulaidy – Computational Social Science PhD student, MEd. in Education, University of Virginia Valentin Vergara – Computational Social Science PhD student, MSc. Natural Resource Economics, Universidad de Concepción, Chile

Categories: CSS/CSI COLLOQUIA

RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES – Neil Johnson – Slaying the Online Hydra of Hate, Distrust and anti-Science @ CENTER FOR SOCIAL COMPLEXITY SUITE, 3RD FLOOR, RESEARCH HALL

Oct 11 @ 3:00 pm – 4:00 pm

Research Colloquium on Computational Social Science/Data Science

Neil Johnson
Professor of Physics
George Washington University

Slaying the Online Hydra of Hate, Distrust and anti-Science

Friday, October 11, 2019 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

Hate and distrust are thriving on the Internet [1]. In addition to the political world, medicine and science are also under attack. Social media platforms such as Facebook have access to state-of-the-art software tools, yet appear unable to keep it under control. In this talk, I discuss why this might be using a simple theory of the functional properties of networks-of-networks. I also show how the ’sociophysics’ of heterogeneous objects offers a fresh understanding of such systems as interacting gels in a multi-dimensional space. I then discuss how this analysis is connected to the unexpected surge in online pro-ISIS support that arose several years ago [2].

[1] N.F. Johnson et al., Hidden resilience and adaptive dynamics of the global online hate ecology, Nature, Sept. 12, 2019
[2] N.F. Johnson et al., New online ecology of adversarial aggregates: ISIS and beyond, Science 352, 1459 (2016)

Bio:

Neil Johnson is a professor of physics at GW and heads up a new initiative in Complexity and Data Science which combines cross-disciplinary fundamental research with data science to attack complex real-world problems. He is a core member of GW’s new Knight Foundation-funded Institute for Data, Democracy and Politics. His research interests lie in the broad area of Complex Systems and ‘many-body’ out-of-equilibrium systems of collections of objects, ranging from crowds of particles to crowds of people and from environments as distinct as quantum information processing in nanostructures through to the online world of collective behavior on social media. He is a Fellow of the American Physical Society (APS) and is the recipient of the 2018 Burton Award from the APS. He received his BA/MA from St. John’s College, Cambridge, University of Cambridge and his Ph.D. as a Kennedy Scholar from Harvard University. He was a Research Fellow at the University of Cambridge, and later a Professor of Physics at the University of Oxford until 2007, having joined the faculty in 1992. Following a period as Professor of Physics at the University of Miami, he was appointed Professor of Physics at George Washington University in 2018. He presented the Royal Institution Christmas Lectures “Arrows of Time” on BBC TV in 1999. He co-founded and co-directed CABDyN (Complex Agent-Based Dynamical Systems) which is Oxford University’s interdisciplinary research center in Complexity Science, and an Oxford University interdisciplinary research center in financial complexity (OCCF).

Categories: CSS/CSI COLLOQUIA

RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES – Fahad Aloraini – Modeling Solar-Panel Technology adoption in Austin: a test of the power of integrating GIS and Cognitive modeling @ CENTER FOR SOCIAL COMPLEXITY SUITE, 3RD FLOOR, RESEARCH HALL

Oct 18 @ 3:00 pm – 4:00 pm

Research Colloquium on Computational Social Science/Data Science

Fahad Aloraini
CSS PhD student

Modeling Solar-Panel Technology adoption in Austin: a test of the power of integrating GIS and Cognitive modeling.

Friday, October 18, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

Agent-based models are being created by researchers from increasingly diverse fields. While this wealth of different perspectives enriches the subject of these models nevertheless it poses problems and challenges such as the depth of knowledge required to construct these models’. An aspect where diversity is an issue is agent-decision-making. While there are cognitive architectures that have been validated in lab conditions against human data. These are too computationally expensive to be used for agent-based models with even moderate number of agents. Moreover these Cognitive Architectures do not specify the way with which to construct the environment and do not leverage the power that Agent-Based models can bring. As part of my master’s project I have come up with a framework that solves these issues; The Objects Memory Dynamics and Actions framework. The framework moves away from the use of unreliable subjective data (surveys and self-reports) to more objective forms of data (rate of exposure to objects). The framework integrates two cognitive architectures (ACT-R and Fast and Frugal) to include a learning computationally efficient agent, the framework specifies the objective data needed for agent learning and decision-making and how to estimate the unique rate with which an agent is exposed to an object in the environment. To test the framework I model Solar-Panel Technology adoption of households in Austin, Texas, and compare it to work done in the field.

Bio:

Fahad Aloraini is a current MAIS-Computational Social Science student and a prospective Computational Social Science Ph.D.. Fahad obtained his B.S in Psychology with a concentration in Human Factors and Applied Cognition in 2017 from George Mason University. Fahad’s research interests include Computational Modeling of Cognition, Energy Economics, and Agent-Based Modeling.

Categories: CSS/CSI COLLOQUIA

Thu

Joint CDS, Math, and Physics Seminar – Prof. Mason Porter – Topological Data Analysis of Spatial Complex Systems @ Exploratory Hall, Room 3301, Fairfax Campus

Oct 24 @ 1:00 pm – 2:30 pm

Joint CDS, Math, and Physics Seminar

Mason Porter
Mathematics Professor
University of California, Los Angeles

Topological Data Analysis of Spatial Complex Systems

Thursday, October 24, 1:00 p.m.
Exploratory Hall, Room 3301, Fairfax Campus

All are welcome to attend.

Abstract:

From the venation patterns of leaves to spider webs, roads in cities, and granular materials, the structure of many systems are influenced heavily by space. Accordingly, the analysis of the effects of space on structure and function is an active area in the study of networks and other complex systems. In this talk, I’ll give an introduction to topological data analysis (especially persistent homology) and discuss its application to spatial complex systems. As an illustrative example, I will present a case study using apocalyptic voting data from California in 2016.

Bio:

Mason Porter is a Mathematics Professor at the University of California, Los Angeles. He works on a range of topics in applied and theoretical mathematics. These include community structure in multidimensional networks, dynamical systems, granular material, topological data analysis, and social network analysis. His collaborators include Alex Arenas, Danielle Bassett, Andrea Bertozzi, Charlotte Deane, Heather Harrington, and Peter Mucha. He has been the recipient of numerous awards (Erdős–Rényi Prize of the Network Science Society, Whitehead Prize of the London Mathematical Society, Young Scientist Award for Socio- and Econophysics by the Deutsche Physikalische Gesellschaft) and is a SIAM fellow and a fellow of the American Physical Society. Prof. Porter has a BS from Caltech and a PhD from Cornell University.

Categories: CSS/CSI COLLOQUIA

RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES – Katherine Anderson – Skill Networks and Measures of Complex Human Capital @ CENTER FOR SOCIAL COMPLEXITY SUITE, 3RD FLOOR, RESEARCH HALL