Calendar

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

Maciej Latek, Chief Technology Officer, trovero.io./
Ph.D. in Computational Social Science 2011
George Mason University

Industrializing multi-agent simulations:
The case of social media marketing, advertising and influence campaigns

Friday, October 12, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:  System engineering approaches required to transition multi-agent simulations out of science into decision support share features with AI, machine learning and application development, but also present unique challenges. In this talk, I will use trovero as an example to illustrate how some of these challenges can be addressed.

As platform to help advertisers and marketers plan and implement campaigns on the social media, trovero is comprised of social network simulations for optimization and automation and network population synthesis used to preserve people’s privacy while maintaining a robust picture of social media communities. Social network simulations forecast campaign outcomes and pick the right campaigns for given KPIs. Simulation is the only viable way to reliably forecast campaign outcomes: Big data methods fail to forecast campaign outcomes, because they are fundamentally unfit for social network data. Network population synthesis enables working with aggregate data without relying on data sharing agreements with social media platforms that are ever more reluctant to share user data with third parties after GDPR and the Cambridge Analytica debacle.

I will outline how these two approaches complement one another, what computational and data infrastructure is required to support them and how workflows and interactions with social media platforms are organized.

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.