Calendar

COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER
AND THE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES
(CSI 898-Sec 001)

Using Coarse-Grained Models to Access Expanded Length and Time Scales for Nanoscience Applications

K. Michael Salerno, Jr.
Center for Computational Materials Science
Naval Research Laboratory
Washington D.C.

November 14, 4:30 pm
Exploratory Hall, Room 3301
Fairfax Campus

Polymers and other soft materials have an important role as a coating for nanoscale building blocks like metallic nanoparticles (NPs) and nanorods. This coating mediates interactions between these building blocks and their environment. Atomistic molecular dynamics (MD) simulations are ideal for examining the role of chemistry and atomic interactions at the sub-nanometer scale, for example in the interactions between a NP and a solvent, or between pairs of NPs. Unfortunately, atomistic MD simulations are limited to lengths of order 50 nm and times of order 50 ns. The time scale limitation precludes modeling nanoscale self-assembly, and limits dynamic simulations to extremely high rates of deformation or thermalforcing. These simulations are also limited to sizes that represent a small number of NPs, making it impossible to model large assembled structures.

Faced with these limitations, we have developed coarse-grained (CG) models of polyethylene, a simple polymer used to coat NPs and nanorods. These models have enabled simulations of bulk polymer melts that overcome the limits of atomistic MD by providing a computational speedup of greater than 104 while retaining fundamental details at the sub-nanometer scale. These details produce the viscoelastic properties and semi-crystalline behavior that are intrinsic to polyethylene and that are missed by generic CG models. When applied to a NP coating the CG models capture the coating morphology, indicating the value of using these CG models in nanoscale applications.

Refreshments will be served at 4:15 PM.

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Find the schedule at http://www.cmasc.gmu.edu/seminars.htm

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Harold Walbert, CSS PhD Student
Department of Computational and Data Sceinces
George Mason University

Connecting R and NetLogo

Friday, November 18, 20016, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor Research Hall

This talk will focus on the specifics of how you connect R to NetLogo. The open source agent based modeling platform NetLogo is used to create many agent based models. R is a powerful open source programming language that offers a wide array of tools for cleaning, analyzing, modeling and visualizing data. The first example will focus on how to get started using the well known Schelling segregation model from the models library in NetLogo. The talk will progress with examples of how I have used these tools to analyze and understand my own agent based models. Topics covered will include data import (agents, networks), data cleaning, running parameter sweeps, repeatability and replication, as well as ways to integrate code with data to help with repeatability and replication.

The talk will be followed by a Q&A session along with light refreshments.

COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER
AND THE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (
CSI 898-Sec 001)

Topological Quantification of Microstructures

Tom Wanner
Department of Mathematical Sciences
George Mason University
Fairfax, VA

November 21, 2016, 4:30 p.m.
Exploratory Hall, Room 3301
Fairfax Campus

Many applied processes generate complex microstructures or patterns which are hard to quantify due to the lack of any underlying regular structure. These patterns may evolve with time or include some element of stochasticity. The resulting variations in the detail structure frequently force one to concentrate on rougher geometric features. From a mathematical point of view, several notions from algebraic topology suggest themselves as natural quantification tools in such a setting.   In this talk I will describe some of these tools, in particular homology and persistent homology, and how they can be efficiently computed using open source software. I will also present some applications motivated by materials science problems.

Refreshments will be served at 4:15 PM.

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Find the schedule at http://www.cmasc.gmu.edu/seminars.htm

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Friday, December 2, 3:00-4:30 p.m.
Center for Social Complexity Suite
Research Hall, 3rd Floor

Talha Oz, CSS PhD Student
Department of Computational and Data Sciences
George Mason University

Attribution of Responsibility and Blame Regarding a Man-made Disaster:  #FlintWaterCrisis

Attribution of responsibility and blame are important topics in political science especially as individuals tend to think of political issues in terms of questions of responsibility, and as blame carries far more weight in voting behavior than that of credit. However, surprisingly, there is a paucity of studies on the attribution of responsibility and blame in the field of disaster research.

The Flint water crisis is a story of government failure at all levels. By studying microblog posts about it, we understand how citizens assign responsibility and blame regarding such a man-made disaster online. We form hypotheses based on social scientific theories in disaster research and then operationalize them on unobtrusive, observational social media data. In particular, we investigate the following phenomena: the source for blame; the partisan predisposition; the concerned geographies; and the contagion of complaining.

This paper adds to the sociology of disasters research by exploiting a new, rarely used data source (the social web), and by employing new computational methods (such as sentiment analysis and retrospective cohort study design) on this new form of data. In this regard, this work should be seen as the first step toward drawing more challenging inferences on the sociology of disasters from “big social data”.

Yang Zhou, CSS PhD Student
Department of Computational and Data Sciences
George Mason University

The Origin of Agriculture in the Peiligang Culture: An Agent-based Modeling Approach

The emergence of agriculture played an important role in human history as it allowed people to move from a nomadic to a sedentary life. This not only provided abundant food, but also sufficient numbers of non-cultivating specialists, which are necessary conditions for the rise of civilization. However, questions about how and why agriculture originated have remained controversial. This paper explores the origin hypotheses of agriculture, using the canonical theory of social complexity as a framework to the transition from hunter-gatherer to agricultural societies in the region of the Peiligang culture in China based on existing literature, and develops an agent-based model to simulate the transition process. The model assumes that the combination of population growth and gaining knowledge on plants drove the transition to agriculture. Results show that based on the basic hypotheses and assumptions, the model is able to generate the key phases that are identical with the existing literatures.

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Robert Axtell, Professor
Computational Social Science
Department of Computational and Data Sciences

Foundations of Agent Computing for Economics and Finance

Friday, February 3, 2017
Center for Social Complexity Suite
3rd Floor, Research Hall

In many ways economics and finance are fields for which the strengths of agent computing should lead researchers to become ‘early adopters,’ at least in comparison to less quantitative social sciences. While this has happened to some extent, it is also true that methodological norms are strong in economics and finance, meaning that innovations will always face resistance. In this talk I will preview the ideas from a paper, co-authored with J. Doyne Farmer of the University of Oxford’s Complexity Economics Programme, geared toward enticing economists toward agent-based modeling. I will contrast the process-based, procedural explanations of social phenomena that result from agent computing with the substantive but often static explanations that are more conventional in economic theory. The role of agents who learn will be highlighted. Next, the ability of agent computing to use all available hardware resources—the ‘small compile time, large run time’ character of ABMs–will be surveyed and compared with standard numerical economics. Then we ask whether having better, more user friendly software could substantially increase the rate of adoption of agents among economists. Finally, we suggest that technologies for systematic parallelization of agent models might go far as a ‘killer app’ insofar as it would permit the creation of large-scale models applicable to a wide variety of policy problems. For each of these arguments an illustrative example will be provided. I will conclude with a brief discussion of bottlenecks that appear to be limiting the progress of agent computing in economics and finance at the present time.

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Howie Huang, Associate Professor
Department of Electrical and Computer Engineering
George Washington University

High-Performance Computing Systems for Big Graph Analytics

Friday, February 10, 3:00-4:30 p.m.
Center for Social Complexity Suite
Research Hall, 3^rd Floor

Big data are ubiquitous and graph-based datasets are especially interesting with broader impacts in social networks, biological networks, and cybersecurity. In this talk, I will discuss a number of our recent projects (Enterprise SC’15, iBFS SIGMOD’16, G-Store SC’16, and Graphene FAST’17) and present our efforts on designing and developing high-performance graph algorithms and systems. In particular, I will discuss several novel techniques on addressing the computational and I/O challenges in graph computing. Furthermore, I will present our ongoing work on utilizing these graph systems for understanding and analyzing complex network data.

Dr. Howie Huang is an Associate Professor in Department of Electrical and Computer Engineering at the George Washington University. His research interests are in the areas of computer systems and architecture, including cloud computing, big data, high-performance computing and storage systems. His work on big graph analytics has ranked highly on both the Graph500 and Green Graph500 benchmarks. Dr. Huang is a recipient of the NSF CAREER Award, Comcast Technology Research and Development Fund, NVIDIA Academic Partnership Award, IBM Real Time Innovation Faculty Award, and GWU School of Engineering and Applied Science Outstanding Young Researcher Award. His projects won the Best Poster Award at PACT’11, ACM Undergraduate Student Research Competition at SC’12, and a finalist for the Best Student Paper Award at SC’11. He completed his Ph.D. in the Department of Computer Science at the University of Virginia.

http://www.seas.gwu.edu/~howie/

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Kevin Comer, PhD Candidate
Computational Social Science
Department of Computational and Data Sciences

Assessing Adverse Selection in the Individual Health Insurance Market using Agent-Based Modeling and Simulation

Friday, February 17, 2017 = 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall

Adverse selection is the phenomenon of “rigged trades” created by asymmetric information between buyers and sellers. This becomes significant in the case of health insurance, where a buyer may know more about his or her health than the insurer. The concern is that widespread adverse selection may lead to a “death spiral”, where premiums become too costly for healthy people to afford, and the only subscribers left are unhealthy people requiring costly health care. Both before and after the passing of the Patient Protection and Affordable Care Act (PPACA, also known as “Obamacare”) in 2013, the concern for adverse selection has been assessed by a number of different methods of economic modeling, most notably game theory models, econometrics, and microsimulation. Kevin Comer utilizes agent-based modeling to assess the emergence and effects of adverse selection on a simulated individual health insurance market, and to test the parameters of already existing policy (coverage and individual mandates, risk corridors, medical loss ratios, and health insurance exchanges) to assess their long-term feasibility.

About the presenter:  Kevin Comer is a Senior Simulation Modeling Engineer at MITRE Corporation. He received his B.S in Systems Engineering and Economics from the University of Virginia, and his M.S. in Operations Research from George Mason University. He is currently a Computational and Social Science Ph.D. Candidate in the Department of Computational and Data Sciences at George Mason University. Kevin is scheduled to defend his dissertation in Spring 2017.

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Yang Zhou, CSS PhD Student
Department of Computational and Data Sciences
George Mason University

The Origin of Agriculture in the Peiligang Culture: An Agent-based Modeling Approach

Friday, February 24, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall

The emergence of agriculture played an important role in human history as it allowed people to move from a nomadic (i.e. hunter-gather) to a sedentary (i.e. agricultural) lifestyle. This shift in lifestyle not only provided abundant food, but also sufficient numbers of non-cultivating specialists, which are necessary conditions for the rise of a civilization. However, questions about how and why agriculture originated have remained controversial. This paper explores the origin hypotheses of agriculture, using the canonical theory of social complexity as a framework to study the transition from hunter-gatherer to agricultural societies in the region of the Peiligang in China based on existing literature, and develops an agent-based model to simulate the transition process. The model assumes that a combination of population growth and gaining knowledge on plants drove the transition from hunter-gatherer to agriculture. Results show that based on the basic hypotheses and assumptions, the model is able to generate the key phases that are identical with the existing literature on such a transaction.

This session will be live-streamed on the newly created CSS program YouTube channel.

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Salwa Ismail, PhD Student
Computational Social Science
Department of Computational and Data Sciences
George Mason University

Towards an ABM for Civil Revolution:
Modeling Emergence of Protesters, Military Decisions, and Resulting State of the Institution

Friday, March 3, 3:00-4:30 p.m.
Center for Social Complexity Suite
Research Hall, 3^rd Floor

The recent string of events in the Middle East, dubbed as Arab Spring transcended rapidly. There was no mechanism to predict them or their outcome. While there are a few models that forecast rebellion, most of them do not take into account the ability of different factors, such as emotional threshold, of both the citizens and military and their the ability to be influenced by vision of what is going around the agent geographically, along with the influence of media/communication channels, to form a realm of influence and affect the actions of the agents simultaneously. This paper explores an agent-based model whose agents react based on economic and emotional levels and a rebellion ensues. Once the rebellion has begun, there are several other factors in this agent-based model that decide the outcome of the rebellion including agents being killed, their geographic vision, their inclement towards news/media, being influenced by current events, and also their personality type of A or B; all these factors combined together affect the dynamics of the unanticipated revolution. The results of the model are rendered in a short duration of time, as one would expect of revolutions, except for those that plunder into a civil war state. The model could be used as one of many components for forecasting future rebellions that have a combination of factors present, as those discussed this paper.

This session will be live-streamed on the newly created CSS program YouTube channel.

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Antoine Mandel, Associate Professor
CES-Centre d’Economie de la Sorbonne

Endogenous Growth in Production Networks

Friday, March 24,2017, 3:00-4:30 p.m.
Center for Social Complexity Suite
Research Hall, 3^rd Floor

We investigate the interplay between technological change and macroeconomic dynamics in an agent-based model of the formation of production networks. On the one hand, production networks form the structure that determines economic dynamics in the short run. On the other hand, their evolution reflects the long-term impacts of competition and innovation on the economy. We account for process innovation via increasing variety in the input mix and hence increasing connectivity in the net- work. In turn, product innovation induces a direct growth of the firm’s productivity and the potential destruction of links. The interplay between both processes generate complex technological dynamics in which phases of process and product innovation successively dominate. The model reproduces a wealth of stylized facts about industrial dynamics and technological progress, in particular the persistence of heterogeneity among firms and Wright’s law for the growth of productivity within a technological paradigm. We illustrate the potential of the model for the analysis of industrial policy via a preliminary set of policy experiments in which we investigate the impact on innovators’ success of feed-in tariffs and of priority market access.