Category: Seminar

Presenter:  Dr. Ingmar Weber, Qatar Computing Research Institute

Date:  Tuesday, February 18

Time:  1-2 p.m.

Location:  Research Hall Suite 373-381

Hosted by: The Center for Social Complexity and Department of Computational Social Science

“What Can Twitter Tell Us About the ‘Real World'”? (abstract follows)

Ingmar Weber is a senior scientist in the Social Computing Group at the Qatar Computing Research Institute (QCRI). He enjoys interdisciplinary research that uses “big data” and computer science methods to address research questions coming from other domains. His work focuses on how user-generated online data can be used to answer questions about society at large and the offline world in general. During his academic career he has gradually moved further South with stops at 52.2°N (Cambridge University), 49.2°N (Max-Planck Institute for Computer Science), 46.5°N (EPFL), 41.4°N (Yahoo! Research Barcelona) and 25.3°N (QCRI). Ingmar is co-organizer of the “Politics, Elections and Data” (PLEAD) workshop at CIKM 2012 and 2013, contributor to a WSDM 2013 tutorial on “Data-driven Political Science”, co-editor of a Social Science Computing Review special issue on “Quantifying Politics Using Online Data”, co-organizer of a CIKM 2013 tutorial on “Twitter and the Real World” and PC Co-Chair of SocInfo 2014. He has published more than 60 peer-reviewed articles and his research has been featured on Financial Times, New Scientist, Foreign Policy, Al Jazeera and other media. He loves chocolate, enjoys participating in the occasional ultra-marathon/triathlon and tweets at @ingmarweber.

Abstract:  Due to Twitter’s global popularity and the relative ease with which large amounts of tweets can be collected and analyzed, more and more researchers turn to Twitter as a data source for studies in Computational Social Science. But at the same time it is obvious that Twitter users are not representative of the overall population. So the question arises what Twitter can really tell us about the “Real World” beyond teens’ obsession with Justin Bieber. In this talk, I will give an overview of some past and present research done at the Qatar Computing Research Institute (QCRI) which tries to find links between the online world and the offline world.

The first line of work looks at political tension in Egypt. Is it possible to quantify tension in a polarized society and maybe even predict outbreaks of violence? Based on our methodology we find evidence that monitoring the extreme poles can give indications about periods of violence. [1, 2] – Joint work with Kiran Garimella and Alaa Batayneh.

Migration is one the major driving forces behind demographic changes around the world. In this second line of work we turn to online data and digital methods to see if we can quantify certain aspects of migration for a large number of countries and faster than typical reporting latencies of often more than a year. [3] – Joint work with Emilio Zagheni, Bogdan State and Kiran Garimella
A popular saying is that you are what you eat. We study if you also tweet what you eat and if it is possible to study food consumption using Twitter. Here, we are particularly interested in questions related to obesity and if there are “networks effects”, but also in questions related to demographic influences such as i

Presenter:  Ken Comer, PhD Candidate, Systems Engineering & Operations Research, Volgenau School of Engineering

Date/Time:  Friday, February 14, 2014 – 3pm

Location: Center for Social Complexity Suite, 3rd floor of Research Hall

Abstract:    In the design process of an agent-based model the pattern chosen for the activation of the agents is an important choice. Every model design must include – either explicitly or implicitly – the conditions under which each agent will call its methods and update its state. Often, however, this is not described in literature and some model designers do not even make this design decision explicitly. Three agent-based models described in the literature in three separate domains were replicated and the impact of various activation schemes on the emergent population patterns and dynamics was analyzed. It was demonstrated that the choice of activation type is important for the outcome behavior of the model and should be stipulated in any published description of an agent-based model. In some experiments the differences noted, while significant, were only statistical. In others they led to substantial differences in either outcomes or model behavior. Further investigation showed that sophisticated activation schemes can become powerful tools to produce unexpected or unpredicted behavior of multi-agent systems. Thus, activation becomes more than an inconvenient detail to be dealt with during design, and is shown to be a source of exploratory variation as modelers of self-organizing social systems seek to match the behavior of natural systems.

 

Date:  Friday, February 7, 2014
Time:  3:00pm
Location:  Center for Social Complexity, 3rd floor Research Hall
Presenter:

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

Abstract: Complex social dynamics drive our social and public health systems, and understanding these dynamics can be critical for designing effective public policies. Advanced computational modeling such as agent-based modeling (ABM) is increasingly used to understand multi-level determinants of complex public health challenges and to design effective responses (including early application in infectious disease and new work in obesity, tobacco control, and widening health disparities). This presentation will include an overview of several recent NIH-funded research studies, including work from three national research networks–National Collaborative on Childhood Obesity Research (NCCOR), Models of Infectious Disease Agent Study (MIDAS), and Network on Inequality, Complexity, and Health—in which the presenter participates.

On the Therapeutic Mechanisms of Deep Brain Stimulation for Parkinson’s Disease:  Why High Frequency?

Dr. Srivedi Sarma is Assistant Professor, Institute for Computational Medicine, Department of Biomedical Engineering, at Johns Hopkins.

Monday, February 3, 2014

4:00pm

Room 229, Krasnow Institute Building

 

Program by Bruce Desmarais, Ph.D., Political Science and Computational Social Science, University of Massachusetts.

Date: January 15, 2014
Location: Lecture Room 229
Krasnow Institute
George Mason University
(GPS address: 4461 Rockfish Creek Lane, Fairfax, VA)
Time: Lecture 10:30am-noon
Workshop 1:45-6pm

LECTURE
Dr. Desmarais will present a talk entitled Modeling Interpersonal Government Communication Networks. Attendance at the lecture is free but registration is required at http://tinyurl.com/mvvreup. If the link is not working, it means the attendance limit has been reached.

WORKSHOP
Participants will learn exponential random graphs modeling (ERGM) using R, including dynamical (temporal) models. To participate, you will need to have basic knowledge of R and your own laptop computer. Attendance at the workshop is free but registration is required at http://tinyurl.com/k4vpvu7. If the link is not working, it means the attendance limit has been reached.

CONTACTS
If you have questions about this event, please contact Jose Manuel Magallanes (jmagalla@gmu.edu) or Jennifer Victor (jvictor3@gmu.edu).

This event is jointly sponsored by the Center for Social Complexity and the Department of Public and International Affairs.

Presenters:  Mateusz Zawisza and Bogumił Kaminski, Decision Support and Analysis Division, Warsaw School of Economics

Date:  December 6

Time:  3pm

Location:  Center for Social Complexity Suite, 3rd floor of Research Hall

Abstract:   We consider the model of duopoly price competition with switching cost and clients’ bounded price perception. Firms optimize their prices by maximizing profits in the long– or short–term planning horizon. Customers demand exactly one unit of homogenous product. Customers incur a switching cost, if they decide to change their current supplier. Moreover, customers are featured by bounded price perception, which results in making random errors, while trying to find the cheapest product. The aim of research is to evaluate customers’ switching cost with respect to equilibrium price, which is calculated by simulation methods. We show that the influence of switching cost is conditioned on customers’ price perception and firms’ planning horizon in an interactive, nonlinear and non–monotonic fashion. We find out that the impact of switching cost differs substantially between short– and long–term planning horizon regime. Therefore, we identify the phase–transition driven by companies’ discount factor.

SPEAKER:  W. Jonathan Lederer, Professor of Physiology & Director of the Center for Biomedical Engineering and Technology, University of Maryland School of Medicine

DATE:  Monday, December 2, 2013

TIME:  4:00 p.m.

LOCATION:  Lecture Room (Room 229)

Krasnow Institute Building

George Mason University, Fairfax, VA

 

 

Title: An Agent-Based Model for the Outbreak, Spread and Containment of Tuberculosis” CSS Seminar
Presenter: Parth Chopra, Student, Thomas Jefferson High School for Science and Technology.
Location: Center for Social Complexity suite, 3rd floor, Research Hall
Time: 3pm

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

Abstract: Tuberculosis (TB) is the second most common form of death from an infectious disease, but yet it is still unknown exactly how it outbreaks and spreads within a population. An ABM, with humans as agents, was created and applied to the TB problem to see what epidemiological dynamics may occur, and what could be learned about the disease. It was coded in MASON, and an SEIR (Susceptible-Exposed-Infectious-Recovered) submodel was developed specifically for TB progression. The slum of Kibera, Kenya (the largest urban slum in Africa, and an area where TB and HIV is particularly rampant) was chosen as a test-case, and its geospatial and demographic information was used for calibration. The model successfully went through VV&T (Verification, Validation and Testing) using established techniques of Qualitative Agreement, Face Validation, and Extreme Input Testing. Preliminary results obtained from standard model runs show that TB epidemics progress in staircase patterns of emergence and stabilization. Furthermore, it was found that TB was creating static hotspots, or pockets of dense disease concentration, from where it was spreading. The results and lessons gleaned from the model can be easily incorporated into current health policies to mitigate TB’s negative impact. Lastly, the research shows the potential of ABMs in investigating infectious diseases.

Date: Friday, October 25, 2013
Time: 3pm
Location: Research Hall, Suite 373-381 (Center for Social Complexity)

The speaker will be Jennifer Victor, Assistant Professor of Political Science, Department of Public and International Affairs, George Mason University. Professor Victor’s talk entitled ““Bridging the Information Gap: Legislative Member Organizations as Social Networks in the United States and Elsewhere”” (abstract below) is scheduled to begin at 3:00 in the Center for Social Complexity Suite located on the 3rd floor of Research Hall. The talk will be followed by a Q&A session along with light refreshments.

Abstract: Why do legislators invest scarce time and resources into forming and maintaining voluntary groups that provide few obvious benefits? Legislative member organizations (LMOs)—such as caucuses in the US Congress and intergroups in the European Parliament (EP)—exist in numerous law-making bodies around the world. Yet unlike parties and committees, LMOs play no obvious and pre-defined role in the legislative process. “Bridging the Information Gap” argues that LMOs provide legislators with opportunities to establish social relationships with colleagues with whom they share a common interest in an issue or theme. The social networks composed of these relationships, in turn, offer valuable opportunity structures for the efficient exchange of policy-relevant information between legislative offices. Building on classic insights from the study of social networks, the authors demonstrate that LMO networks are composed of weak, bridging ties that cut across party and committee lines, thus providing lawmakers with access to otherwise unattainable information and make all members of the network better informed. Building on a comparative approach, the book provides an overview of the existence of LMOs across advanced, liberal democracies and offers two nuanced case studies of LMOs in the European Parliament and the U.S. Congress. These case studies rely on a mixed method set-up that garners the respective strengths of social network analysis, sophisticated statistical methods, and careful qualitative analysis of a large number of in-depth interviews.

Please visit www.css.gmu.edu to see list of upcoming seminar speakers.

Date:  Friday, September 27, 2013

Time:  3:00pm

Location:  Center for Social Complexity Suite 373-381, Research Building

Presenter:  Chenna Reddy Cotla, CSS PhD Candidate

Title:  Learning in Linear Public Goods Games: A Comparative Analysis

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

Abstract:    This paper examines learning in repeated linear public goods games. Experimental data from previously published papers is considered in testing several learning models in terms of how accurately they describe individuals’ round-by-round choices. In total 18 datasets are considered and each dataset differs from the others in at least one of the following aspects: marginal per capita return, group size, matching protocol, number of rounds, and endowment that determines the number of stage-game strategies. Both ex post descriptive power of learning models and their ex ante predictive power are examined. Descriptive power of learning models is examined by comparing mean quadratic scores computed for each dataset using the parameters that are estimated using all datasets. Predictive power of the learning models is evaluated by comparing mean quadratic scores computed for each dataset using parameters estimated using the other datasets. The following learning models are considered to model individual level adaptive behavior: reinforcement learning, normalized reinforcement learning, stochastic fictious play, normalized stochastic fictious play, experience weighted attraction learning (EWA), self-tuning EWA, individual evolutionary learning and Impulse matching learning. In addition to these prominent learning models, this paper also introduces a new learning model: Experience weighted attraction learning with inertia and experimentation (EWAIE). The main result is that EWAIE outperforms the other learning models in modeling individuals’ round-by-round choices in repeated linear public goods games. Furthermore, while all the learning models out-perform a random choice benchmark, only EWA and EWAIE out-perform the empirical choice frequencies in predicting behavior, which indicates that they adjust their individual level predictions more accurately over time.