Computational Social Science Program

This week's colloquium speaker will be Zachary Kessler, a Ph.D. student in George Mason University's Department of Economics and an accomplished agent-based modeler. Zachary's talk entitled "An Agent-based Model of the O-Ring Theory of Development: Issues with Endogenous Skill Matching" is scheduled from 3-4:30. Zoom details follow.

We hope you can join us on Friday, October 1 at 3:00 p.m.

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Date

Friday, October 1, 2021

Time

3:00 pm - 4:30 pm EDT (UTC-4:00)

Title

An Agent-based Model of the O-Ring Theory of Development: Issues with Endogenous Skill Matching

Speaker

Zachary Kessler

Abstract

Kremer’s O-Ring Theory of Development (1993) represents an important effort to explain a collection of stylized facts from the economic development literature, most critically the large skill disparities between countries. This paper tests the theory by utilizing an agentbased model to add endogenous choice to workers’ behavior and examines the robustness of its results, determining if a collection of agents can sort themselves into the predicted equilibrium under a variety of circumstances. The agent-based approach reveals that in a world where countries possess a heterogenous number of tasks in their respective production functions, the skill matching in Kremer’s model cannot be reached. Instead, workers sort into a Nash equilibrium rather than the efficient outcome. Further, this result is shown to be robust to changes in productivity.

Author Bios

Zachary is a Ph.D. student in George Mason University’s Department of Economics who is an accomplished agent-based modeler.

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Are you interested in urban analytics and ABM? Looking for a two-year postdoc? Former CSS Faculty member Andrew Crooks has such a position open at the University at Buffalo. For further details see: https://www.ubjobs.buffalo.edu/postings/29858

Mason's Online Pandemic MODeling Forum Friday, April 30, 3-4:30 p.m.

Elise Jing, Scientist
Sirius XM and Pandora

Characterizing Partisan Political Narratives about COVID-19 on Twitter

The COVID-19 pandemic is a global crisis that has been testing every society and exposing the critical role of local politics in crisis response. In the United States, there has been a strong partisan divide which resulted in polarization of individual behaviors and divergent policy adoption across regions. Here, to better understand such divide, we characterize and compare the pandemic narratives of the Democratic and Republican politicians on social media using novel computational methods including computational framing analysis and semantic role analysis. By analyzing tweets from the politicians in the U.S., including the former president, members of Congress, and state governors, we systematically uncover the contrasting narratives in terms of topics, frames, and agents that shape their narratives. We found that the Democrats' narrative tends to be more concerned with the pandemic as well as financial and social support, while the Republicans discuss more about other political entities such as China. By using contrasting framing and semantic roles, the Democrats emphasize the government's role in responding to the pandemic, and the Republicans emphasize the roles of individuals and support for small businesses. Both parties' narratives also include shout-outs to their followers and blaming of the other party. Our findings concretely expose the gaps in the "elusive consensus" between the two parties. Our methodologies may be applied to computationally study narratives in various domains.

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

Carmen Arleth Iasiello

Bachelor of Arts, American University, 2001
Master of Arts, Columbia University, 2003

An Agent-Based Modeling Approach for Human Resource Management

Tuesday, April 20, 2021
9:30 - 11:30 AM
All are welcome to attend.
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Committee
Andrew Crooks, Chair
Robert Axtell
William Kennedy
Sarah Wittman

Abstract: Computational social science methods and specifically agent-based modeling have increasingly been used within applied social science fields. A significant contributor to this trend has been the availability of fine-grained data about individual and social behavior. While data availability may aid this process, the true power of computational social science arises when data and theory are combined. Theories derived from different social science traditions vary in their development, testing methods, and interpretation of data. The applications of computational methods have largely excluded explicit consideration of what is gained or lost in the translation of theory derived from epistemic traditions that differ from that in computational social science. This dissertation addresses this gap in three ways. First, it defines a framework that may be used in the process of applying inductively-derived, qualitatively-developed theories. Second, it applies this framework by exploring management science theory. Third, it presents an agent-based model informed by a management science theory and validates it based on human resource management data. This application is presented as a replicable example of both epistemological translation and the computational methods applied.

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Oral Defense of Doctoral Dissertation - John Leung
Doctor of Philosophy in Computational Sciences and Informatics
Department of Computational and Data Sciences
College of Science
George Mason University

Thursday, 4/22/2021
10:00 AM - 12:00 PM

John Kalung Leung
Bachelor of Science, American University, 1984
Master of Science, Johns Hopkins University, 1990
Executive Master Business of Administration, University of Texas at Arlington, 2008

Emotion Aware Recommender Systems

Committee
Igor Griva, Chair
Jason Kinser
Estela Blaisten-Barojas
William Kennedy

ABSTRACT: Recommender Systems help users to overcome information overload by making predictions and recommendations that meet users' tastes and preferences. A user's mood influences his/her decision-making in choosing from a list of top-N recommended items. However, Recommenders do not track users' moods state when making top-N recommendations to users. Thus, users often found stale recommendations in the top-N list.

I proposed to enhance Recommender Systems by tracking users' moods state and make top-N recommendations based on the updated users’ and items’ emotion profiles. In recognition of several limitations: (1) emotion-labeled attributes are not readily available in datasets, (2) lack of standard definition for emotions and procedure to collect and label emotion metadata, (3) not all objects have a face for facial emotion detection and recognition despite facial micro-expression detection and recognition of basic human emotions are popular methodology to label a person's primary facial emotional expressions, I developed a text-based Tweets Affective Classifier model capable of emotion detection and recognition based on Ekman's six basic human emotions and neutral emotion. This model is then used to extrapolate basic human emotions from the subjective text of objects such as movie overview or product descriptions. Furthermore, I developed an innovative Affective Aware Pseudo Association Method (AAPAM) to pseudo connect disjoint objects in datasets within the same or different information domains.
This research has shown that an Emotion Aware Recommender could track users' moods in making subsequent top-N recommendations that contain serendipitous items, thus overcoming the cold-start and staleness issues confronted in the field. Using the Affective Index Indicator (AII) to pseudo connect disjoint users or items for making recommendations in Collaborative Filtering is shown to be more efficient than the traditional Collaborative Filtering computing through rating matrix. I further extended the APPAM to support decision-making strategies in a multi-user group. Finally, I found by applying users' and items' emotion profiles in a system simulcast group can improve the throughput of top-N recommendations.

Thursday, 4/22/2021
10:00 AM - 12:00 PM
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Mason’s Online Pandemic MODeling Forum
Friday, April 9, 2021
3:00 pm - 4:30 pm EDT (UTC-4:00)

Murat Tasci, Senior Research Economist, Research Department, Federal Reserve Bank of Cleveland

Unemployment in the Time of COVID-19: A Flow-Based Approach to Real-time Unemployment Projections | NBER

This paper presents a flow-based methodology for real-time unemployment rate projections and shows that this approach performed considerably better at the onset of the COVID-19 recession in the spring 2020 in predicting the peak unemployment rate as well as its rapid decline over the year. It presents an alternative scenario analysis for 2021 based on this methodology and argues that the unemployment rate is likely to decline to 5.4 percent by the end of 2021. The predictive power of the methodology comes from its combined use of real-time data with the flow approach.

Murat Tasci is a senior research economist in the Research Department of the Federal Reserve Bank of Cleveland. He is primarily interested in macroeconomics and labor economics. His current work focuses on labor market fluctuations over the business cycle, labor market policies and search frictions. Prior to joining the Cleveland Reserve Bank in 2006, Dr. Tasci was a Teaching and Research Assistant at the University of Texas at Austin. Dr. Tasci received a bachelor's degree in economics at Koc University in Istanbul, Turkey, and an MS and PhD in economics from the University of Texas at Austin.

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Mason Online Pandemic MODeling Forum
Friday, March 26, 2021

Eric Winsberg, Professor of Philosophy, University of South Florida

Models, Values, and Precaution: How should models guide policy?

Models have a played a prominent role in guiding Covid-19 mitigation policy, often being used to make confident and dire predictions. But models such as these often embed assumptions about values. They also can encode precautionary reasoning that emphasizes a particular balance of risks. How can we optimize the use of models to guide policy in a crisis? What has the last year taught us about expert testimony and “following the science”?

Colloquium on Computational Social Science/Computational Data Sciences - Mason Online Pandemic MODeling Forum

March 19, 2021
11:00 a.m. (EDT)

Verónica Acurio Vásconez
Associate Professor, University of Lorraine
Member of the Bureau d’Economie Théorique et Appliquée (BETA)

Macroepidemics and unconventional monetary policy

Despite the fact that the current covid-19 pandemic was neither the first nor the last disease to threaten a pandemic, only recently have studies incorporated epidemiology into macroeconomic theory. In our paper, we use a dynamic stochastic general equilibrium (DSGE) model with a financial sector to study the economic impacts of epidemics and the potential for unconventional monetary policy to remedy those effects. By coupling a macroeconomic model to a traditional epidemiological model, we are able to evaluate the pathways by which an epidemic affects a national economy. We find that no unconventional monetary policy can completely remove the negative effects of an epidemic crisis, save perhaps an exogenous increase in the shares of claims coming from the Central Bank (“epi loans”). To the best of our knowledge, our paper is the first to incorporate disease dynamics into a DSGE-SIR model with a financial sector and examine the effects of unconventional monetary policy.

Colloquium on Computational Social Science/Computational Data Sciences - Mason Online Pandemic MODeling Forum

Mar 12, 2021, 3:00 - 4:30 PM

Jidong Zhou, Associate Professor, Economics, Yale University and Fei Li, Associate Professor, Economics, University of North Carolina Chapel Hill

A Model of Crisis Management

We propose a model of how multiple societies respond to a common crisis. A government faces a ``damned-either-way'' policy-making dilemma: aggressive intervention contains the crisis, but the resulting good outcome makes people skeptical of the costly response; light intervention worsens the crisis and causes the government to be faulted for not doing enough. This dilemma can be mitigated for the society that encounters the crisis first if another society faces it afterward. Our model predicts that the later society does not necessarily perform better despite having more information, while the earlier society might benefit from a dynamic counterfactual effect.

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Fall 2021 CDS 290 Game and Sports Analytics Course!

https://science.gmu.edu/news/fall-2021-cds-290-game-and-sports-analytics-course