Author: William Kennedy

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.

• *The programs and services offered by George Mason University are open to all who seek them. George Mason does not discriminate on the basis of race, color, religion, ethnic national origin (including shared ancestry and/or ethnic characteristics), sex, disability, military status (including veteran status), sexual orientation, gender identity, gender expression, age, marital status, pregnancy status, genetic information, or any other characteristic protected by law. After an initial review of its policies and practices, the university affirms its commitment to meet all federal mandates as articulated in federal law, as well as recent executive orders and federal agency directives.

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.

• *The programs and services offered by George Mason University are open to all who seek them. George Mason does not discriminate on the basis of race, color, religion, ethnic national origin (including shared ancestry and/or ethnic characteristics), sex, disability, military status (including veteran status), sexual orientation, gender identity, gender expression, age, marital status, pregnancy status, genetic information, or any other characteristic protected by law. After an initial review of its policies and practices, the university affirms its commitment to meet all federal mandates as articulated in federal law, as well as recent executive orders and federal agency directives.

GDELT: Event Data Meets Big Data, and How We Can Use It

David Masad, PhD Student
Computational Social Science Department
George Mason University

DATE: Monday, September 23, 2013
TIME: 4:00 p.m.
LOCATION: Lecture Room (Room 229)
Krasnow Institute Building
George Mason University, Fairfax, VA

ABSTRACT:
The Global Database of Events, Language and Tone (GDELT) is a new
dataset of detailed, politically-relevant events from around the world,
updated daily and freely available — over 250 million events, from 1979
to yesterday. It offers new opportunities to find spatial and temporal
patterns and trends in international events, develop new theories and
models, and even build and test forecasts of future events. As the
largest dataset of its kind, it also introduces new challenges, and
invites the application of tools from other fields and across disciplines.

This presentation will provide an introduction to GDELT and some of the
work that has already been done with it. It will touch on the history of
event data and on GDELT’s machine-coding approach (and how it compares
to human coding); it will describe some of the research that has already
utilized GDELT, and the strengths and weaknesses of the data that the
research community is discovering. Finally, it will discuss the future
of GDELT research, and invite questions and interdisciplinary discussion.

——————————————————————–
** Parking Note: The former meter/pay-station lot across Shenandoah
River Lane has been converted to a loading/unloading zone and can no
longer serve for Monday seminar visitor parking. Visitors should park
in one of the campus decks.

• *The programs and services offered by George Mason University are open to all who seek them. George Mason does not discriminate on the basis of race, color, religion, ethnic national origin (including shared ancestry and/or ethnic characteristics), sex, disability, military status (including veteran status), sexual orientation, gender identity, gender expression, age, marital status, pregnancy status, genetic information, or any other characteristic protected by law. After an initial review of its policies and practices, the university affirms its commitment to meet all federal mandates as articulated in federal law, as well as recent executive orders and federal agency directives.

The CSS seminar speaker for Friday, September 20 will be Heath Henderson, research fellow in the Office of Strategic Planning and Development Effectiveness at the Inter-American Development Bank.  Dr. Heath’s talk, entitled “Nicaragua, the Food Crisis, and the Future of Smallholder Agriculture” (abstract below), is scheduled to begin at 3:00pm 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.

Dr. Henderson holds a PhD in economics and an MA in international politics from American University in Washington, DC. His dissertation, titled “Nicaragua, the Food Crisis, and the Future of Smallholder Agriculture,” examined equity-efficiency tradeoffs in the distribution of agricultural landholdings in developing countries by focusing on the case of Nicaragua. Generally speaking, his research interests include agent-based modeling, agricultural development, and applied econometrics.

Abstract:    Empirical studies of agrarian production in developing countries find that smallholders possess a productivity advantage over large farms. Eswaran and Kotwal (1986) famously derive this inverse farm size-productivity relationship from the structure of agrarian production. Their model predicts that in otherwise equivalent economies a more egalitarian land distribution raises output and producer welfare. However, developing countries have recently experienced the rapid emergence of modern value chains. Recent research provides evidence that this transformation alters the welfare possibilities of agrarian economies. We therefore extend the Eswaran-Kotwal model by incorporating a modern value chain. Our results contradict previous sanguine conclusions about egalitarian distributions of the means of production. We observe a potential equity/efficiency tradeoff in the distribution of land.

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

• *The programs and services offered by George Mason University are open to all who seek them. George Mason does not discriminate on the basis of race, color, religion, ethnic national origin (including shared ancestry and/or ethnic characteristics), sex, disability, military status (including veteran status), sexual orientation, gender identity, gender expression, age, marital status, pregnancy status, genetic information, or any other characteristic protected by law. After an initial review of its policies and practices, the university affirms its commitment to meet all federal mandates as articulated in federal law, as well as recent executive orders and federal agency directives.