Calendar

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

By Christopher Carroll and Jacquelyn Kazil

Christopher Carroll, Professor
Department of Economics
Johns Hopkins University

Title: Introduction to The Economics ARK (Algorithmic Repository and toolKit)

Abstract:
The Econ-ARK/HARK toolkit is a modular and extensible open source toolkit for solving, simulating, and estimating heterogeneous-agent (HA) models in economics and the social sciences. Although the value of models of this kind has been clear both to academics and to policymakers for a long time, the code for implementing such models has so far been handcrafted and idiosyncratic. As a result, it may take years of human capital development for a new researcher to become proficient enough in these methods to contribute to the literature. The seminar will describe how the Heterogeneous Agents Resources and toolKit (HARK) eases this burden by providing a robust framework in which canonical examples of such models are solved. The toolkit provides object-oriented tools for representing heterogeneous agents, solution methods for solving or characterizing their dynamic choice problems, and a framework for representing the environment in which agents interact. The aim of the toolkit is to become the go-to resource for heterogeneous agent modelers, by providing a well-designed, well-documented, and powerful platform in which they can develop their own work in a robust and replicable manner.

Bio:
I am a professor of economics at JHU and co-chair of the National Bureau of Economic Research’s working group on the Aggregate Implications of Microeconomic Consumption Behavior. Originally from Knoxville, Tennessee, I received my A.B. in Economics from Harvard University in 1986 and a Ph.D. from the Massachusetts Institute of Technology in 1990. After graduating from M.I.T., I worked at the Federal Reserve Board in Washington DC, where I prepared forecasts for consumer expenditure. I moved to Johns Hopkins University in 1995 and also spent 1997-98 working at the Council of Economic Advisors in Washington, where I analyzed Social Security reform proposals, tax and pension policy, and bankruptcy reform. Aside from my current work at Hopkins and the NBER, I am also an associate editor at the Review of Economics and Statistics,(ReStat) the Journal of Business and Economic Statistics, (JBES) and the Berkeley Electronic Journal of Macroeconomics (BEJM).

My research has primarily focused on consumption and saving behavior, with an emphasis on reconciling the empirical evidence from both microeconomic and macroeconomic sources with theoretical models. (In addition to articles in economic journals, I’ve authored Encyclopedia Britannica articles on consumption related topics.) My most recent research has focused on the dynamics of expectations formation, particularly on how expectations reflect households’ learning from each other and from experts. This focus flows from a career-long interest in consumer sentiment and its determinants.

Jacquelyn Kazil
CSS PhD Student
Title: Mesa, Agent-based modeling library in Python 3

Abstract:
Python has grown significantly in the scientific community, but there is no tool or reusable framework to do agent-based modeling (ABM) in Python. While there are well-established frameworks in other languages, the lack of one in the Python language is at odds with the growth of Python in the scientific community. As a result, we created an ABM framework called Mesa in Python 3 with sustained contributions. Mesa is built to be modular, so the backend server, the frontend visualization and tooling, the batch runner, and the data collector are each separate components that can be upgraded independently from each other. In addition to this, Mesa is extensible and meant to be decoupled from domain specific add-ons. This empowers the community to develop features and add-ons independent of the core Mesa library. In this talk, Jackie will set the stage for her Ph.D by providing an overview Mesa’s past, present, and proposed future, along with how that fits in the ABM ecosystem of other tooling.

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Henry Smart, III, PhD candidate
Virginia Tech

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

Abstract:
Colorism is the allocation of privilege and disadvantage based on skin color, with a prejudice for lighter skin. This project uses agent-based modeling (computational simulation) to explore the potential effects of colorism on local policing. I argue that colorism might help to explain some of the racial disparities in the United States’ criminal justice system. I use simulated scenarios to explore the plausibility of this notion in the form of two questions: 1) How might colorism function within an organization; and 2) What might occur when managers apply the typical dilemmatic responses to detected colorism? The simulated world consists of three citizen-groups (lights, mediums, and darks), five policy responses to detected colorism, and two policing behaviors (fair and biased). Using NetLogo, one hundred simulations were conducted for each policy response and analyzed using one-way ANOVA and pairwise comparison of means. When the tenets of colorism were applied to an organizational setting, only some of the tenets held true. For instance, those in the middle of the skin color spectrum experienced higher rates incarceration when aggressive steps were taken to counter colorism, which ran counter to the expectations of the thought experiment. The study identified an opportunity to expand the description of colorism to help describe the plight of those in the middle of the skin color spectrum. The major contributions from this work include a conceptual model that describes the relationship between the distinct levels of colorism and it progresses the notion of interactive colorism. The study also produced conditional statements that can be converted into hypotheses for future experiments.

Computational Social Science Research Colloquium /Colloquium in Computational and Data Sciences 

Kieran Marray, Laidlaw Scholar 
St. Catherine’s College
University of Oxford

FORTEC: Forecasting the Development of Artificial Intelligence up to 2050 Using Agent-Based Modeling

Friday, August 31, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Kieran is a Laidlaw Scholar from St Catherine’s College, University of Oxford. He has been visiting the Center for Social Complexity over the summer to do research in complexity economics supervised by Professor Rob Axtell.

Due to a welcome reception for new and returning CDS student, there will be no colloquium on Friday, September 7. The next one will be held on Friday, September 14.  Speaker and topic to be announced later.

Computational Social Science Research Colloquium /Colloquium in Computational and Data Sciences

William Kennedy, PhD, Captain, USN (Ret.)
Research Assistant Professor
Center for Social Complexity
Computational and Data  Sciences
College of Science

Characterizing the Reaction of the Population of NYC to a Nuclear WMD

Friday, September 14, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:  This talk will again review the status of our multi-year project to characterize the reaction of the population of a US megacity to a nuclear WMD event 2 years into the project. Our approach has been to develop an agent-based model of the New York City area, with agents representing each of the 23 million people, and establish a baseline of normal behaviors before exploring the population’s reactions to small (5-10Kt) nuclear weapon explosions. We have the modeled the environment, agents, and their interactions, but there have been some challenges in the last year. I’ll review our status, successes, and challenges as well as near term plans.

Computational Social Science Research Colloquium /Colloquium in Computational and Data Sciences

Michael Eagle, Asst. Professor
Department of Computational and Data Sciences
George Mason University

Understanding Behavior in Interactive Environments: Deriving Meaningful Insights from Data 

Friday, September 21, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:  Advanced learning technologies are transforming education as we know it. These systems provide a wealth of data about student behavior. However, extracting meaning from such datasets is a challenge for researchers; and often impossible for the instructors. Understanding learner behavior is critical to finding, extracting, and acting on insight found in educational data. It is equally important to have strong evaluative methodologies to explore the effectiveness of new interventions, and pinpoint when, where, and precisely what students are learning.

This talk covers the ways I have combined human modeling, qualitative, quantitative (statistical and machine learning) methods enable researchers to make sense of behavior and to produce data-driven personalization. I will have a focus on modeling of humans in interactive problem-solving environments, such as intelligent tutoring systems, online courses, and educational video games. Combining results from experimental design, machine learning, and cognitive models results in large improvements to existing learning systems, as well as powerful insights for instructors and researchers on how students behave and learn in interactive environments.

Computational Social Science Research Colloquium /
Colloquium in Computational and Data Sciences

Robert Axtell, Professor
Computational Social Science Program,
Department of Computational and Data Sciences
College of Science
and
Department of Economics
College of Humanities and Social Sciences
George Mason University

Are Cities Agglomerations of People or of Firms? Data and a Model

Friday, September 28, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:  Business firms are not uniformly distributed over space. In every country there are large swaths of land on which there are very few or no firms, coexisting with relatively small areas on which large numbers of businesses are located—these are the cities. Since the dawn of civilization the earliest cities have husbanded a variety of business activities. Indeed, often the raison d’etre for the growth of villages into towns and then into cities was the presence of weekly markets and fairs facilitating the exchange of goods. City theorists of today tend to see cities as amalgams of people, housing, jobs, transportation, specialized skills, congestion, patents, pollution, and so on, with the role of firms demoted to merely providing jobs and wages. Reciprocally, very little of the conventional theory of the firm is grounded in the fact that most firms are located in space, generally, and in cities, specifically. Consider the well-known facts that both firm and city sizes are approximately Zipf distributed. Is it merely a coincidence that the same extreme size distribution approximately describes firm and cities? Or is it the case that skew firm sizes create skew city sizes? Perhaps it is the other way round, that skew cities permit skew firms to arise? Or is it something more intertwined and complex, the coevolution of firm and city sizes, some kind of dialectical interplay of people working in companies doing business in cities? If firm sizes were not heavy-tailed, but followed an exponential distribution instead, say, could giant cities still exist? Or if cities were not so varied in size, as they were not, apparently, in feudal times, would firm sizes be significantly attenuated? In this talk I develop the empirical foundations of this puzzle, one that has been little emphasized in the extant literatures on firms and cities, probably because these are, for the most part, distinct literatures. I then go on to describe a model of individual people (agents) who arrange themselves into both firms and cities in approximate agreement with U.S. data.

Computational Social Science Research Colloquium /
Colloquium in Computational and Data Sciences

J. Brent Williams

Founder and CEO
Euclidian Trust

Improved Entity Resolution as a Foundation for Model Precision 

Friday, November 2, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract: Analyzing behavior, identifying and classifying micro-differentiations, and predicting outcomes relies on the establishment of a core foundation of reliable and complete data linking. Whether data about individuals, families, companies, or markets, acquiring data from orthogonal sources results in significant matching challenges. These matching challenges are difficult because attempts to eliminate (or minimize) false positives yields an increase in false negatives. The converse is true also.

This discussion will focus on the business challenges in matching data and the primary and compounded impact on subsequent outcome analysis. Through practical experience, the speaker led the development and first commercialization of novel approach to “referential matching”. This approach leads to a more comprehensive unit data model (patient, customer, company, etc.), which enables greater computational resolution and model accuracy by hyper-accurate linking, disambiguation, and detection of obfuscation. The discussion also covers the impact of enumeration strategies, data obfuscation/hashing, and natural changes in unit data models over time.

Computational Social Science Research Colloquium /
Colloquium in Computational and Data Sciences

William Lamberti, CSI PhD Student
Department of Computational and Data Sciences
George Mason University

Classifying Pill Spies Using Storks 

Friday, November 9, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:  Simple and intuitive measures of shape are substantial challenges in image analysis and computer vision.  While measures of shape do exist, there are only a few intuitive and mathematically derived measures for other polygons.  In this talk, a measure, which we call shape proportions, for regular polygons and circles are shown.  From these proportions, we find the corresponding encircled image-histograms for classification purposes.  This method of using shape proportions and encircled image-histograms is called SPEIs (which is pronounced as ‘spy’).  An analysis using simulated and actual shape images were compared to ensure its utility.  Future work regarding applying SPEIs to NIH pill data using stratified over-representative k-folds cross-validation (abbreviated as STORKC, which is pronounced as ‘stork’) will be discussed.

Computational Social Science Research Colloquium /Colloquium in Computational and Data Sciences

Sanjay Nayar
CSS PhD Student

Title: Interlocking Directorates Analysis: Evidence from India BSE-100

Friday, November 30, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

Interlocked directorates among companies are common across the world and have been studied quite extensively in the Western World. This study focuses on interlocking directorates, also referred to as inter-organizational elite cooptation (Allen, 1974), among the top 100 publicly traded companies on the Bombay Stock Exchange (BSE-100) in India. The time period analyzed is between 2006 and 2010, the years spanning the recent great recession. While De (2012) looked at the performance effects of interlocking directorates within Indian business groups irrespective of their membership in BSE-100, it did not address in the analysis the key players, cliques, etc., the evolution of the interlocking over time, or any comparisons with the United States. This broad exploratory study is the first to look at the BSE-100 interlocking directorates’ network to see how it has or has not been dominated by a select group of individuals, companies or sectors during 2006-2010, along with the companies’ performances in the longer-term, given their position in the network. Some comparisons are also made with the US market using information available in published papers (Everard, 2002). This study also serves a secondary purpose of being an introduction to the interconnections between some of the biggest players in the Indian Economy/Stock Market and thus would also be of interest to those studying business in India.