Calendar

Abstract:

Hate and distrust are thriving on the Internet [1]. In addition to the political world, medicine and science are also under attack. Social media platforms such as Facebook have access to state-of-the-art software tools, yet appear unable to keep it under control. In this talk, I discuss why this might be using a simple theory of the functional properties of networks-of-networks. I also show how the ’sociophysics’ of heterogeneous objects offers a fresh understanding of such systems as interacting gels in a multi-dimensional space. I then discuss how this analysis is connected to the unexpected surge in online pro-ISIS support that arose several years ago [2].

[1] N.F. Johnson et al., Hidden resilience and adaptive dynamics of the global online hate ecology, Nature, Sept. 12, 2019
[2] N.F. Johnson et al., New online ecology of adversarial aggregates: ISIS and beyond, Science 352, 1459 (2016)

Bio:

Neil Johnson is a professor of physics at GW and heads up a new initiative in Complexity and Data Science which combines cross-disciplinary fundamental research with data science to attack complex real-world problems. He is a core member of GW’s new Knight Foundation-funded Institute for Data, Democracy and Politics. His research interests lie in the broad area of Complex Systems and ‘many-body’ out-of-equilibrium systems of collections of objects, ranging from crowds of particles to crowds of people and from environments as distinct as quantum information processing in nanostructures through to the online world of collective behavior on social media. He is a Fellow of the American Physical Society (APS) and is the recipient of the 2018 Burton Award from the APS. He received his BA/MA from St. John’s College, Cambridge, University of Cambridge and his Ph.D. as a Kennedy Scholar from Harvard University. He was a Research Fellow at the University of Cambridge, and later a Professor of Physics at the University of Oxford until 2007, having joined the faculty in 1992. Following a period as Professor of Physics at the University of Miami, he was appointed Professor of Physics at George Washington University in 2018. He presented the Royal Institution Christmas Lectures “Arrows of Time” on BBC TV in 1999. He co-founded and co-directed CABDyN (Complex Agent-Based Dynamical Systems) which is Oxford University’s interdisciplinary research center in Complexity Science, and an Oxford University interdisciplinary research center in financial complexity (OCCF).

Abstract:

Agent-based models are being created by researchers from increasingly diverse fields. While this wealth of different perspectives enriches the subject of these models nevertheless it poses problems and challenges such as the depth of knowledge required to construct these models’. An aspect where diversity is an issue is agent-decision-making. While there are cognitive architectures that have been validated in lab conditions against human data. These are too computationally expensive to be used for agent-based models with even moderate number of agents. Moreover these Cognitive Architectures do not specify the way with which to construct the environment and do not leverage the power that Agent-Based models can bring. As part of my master’s project I have come up with a framework that solves these issues; The Objects Memory Dynamics and Actions framework. The framework moves away from the use of unreliable subjective data (surveys and self-reports) to more objective forms of data (rate of exposure to objects). The framework integrates two cognitive architectures (ACT-R and Fast and Frugal) to include a learning computationally efficient agent, the framework specifies the objective data needed for agent learning and decision-making and how to estimate the unique rate with which an agent is exposed to an object in the environment. To test the framework I model Solar-Panel Technology adoption of households in Austin, Texas, and compare it to work done in the field.

Bio:

Fahad Aloraini is a current MAIS-Computational Social Science student and a prospective Computational Social Science Ph.D.. Fahad obtained his B.S in Psychology with a concentration in Human Factors and Applied Cognition in 2017 from George Mason University. Fahad’s research interests include Computational Modeling of Cognition, Energy Economics, and Agent-Based Modeling.

Abstract:

From the venation patterns of leaves to spider webs, roads in cities, and granular materials, the structure of many systems are influenced heavily by space. Accordingly, the analysis of the effects of space on structure and function is an active area in the study of networks and other complex systems. In this talk, I’ll give an introduction to topological data analysis (especially persistent homology) and discuss its application to spatial complex systems. As an illustrative example, I will present a case study using apocalyptic voting data from California in 2016.

Bio:

Mason Porter is a Mathematics Professor at the University of California, Los Angeles. He works on a range of topics in applied and theoretical mathematics. These include community structure in multidimensional networks, dynamical systems, granular material, topological data analysis, and social network analysis. His collaborators include Alex Arenas, Danielle Bassett, Andrea Bertozzi, Charlotte Deane, Heather Harrington, and Peter Mucha. He has been the recipient of numerous awards (Erdős–Rényi Prize of the Network Science Society, Whitehead Prize of the London Mathematical Society, Young Scientist Award for Socio- and Econophysics by the Deutsche Physikalische Gesellschaft) and is a SIAM fellow and a fellow of the American Physical Society. Prof. Porter has a BS from Caltech and a PhD from Cornell University.

Abstract:

The relationship between worker skills and wages is a problem of tremendous economic interest, making it critical to have effective measures of the skills, knowledge, and experience that a worker brings to production: a bundle of worker characteristics that economists refer to as human capital. Traditional models of human capital measures either divide workers into broad categories (e.g., laborers and management) or treat skills as a uni-dimensional measure of speed, education, or experience. However, in knowledge based production, the value a worker brings to production depends on both her individual skills and the interaction between them. Here, I present a network-based method for characterizing worker skills. I construct a human capital network, in which nodes are skills and two skills are connected if a worker has both or both are required for the same job. A worker’s human capital can be measured according to the position of her skills on the network. I illustrate this method using a novel dataset, gathered from an online freelance labor market. I show that workers with diverse skills earn higher wages than their peers with more specialized skills, and that those who use their diverse skills in combination earn the highest wages of all. I also show that network-based measures of human capital capture variation in wages beyond that captured by the skills individually. Finally, I will show how these same techniques can be used outside of the economic context, to quantify the relationship between the skills of collaborators.

Bio:

Kate is a visiting assistant professor at the University of Pittsburgh Department of Informatics and Networked Systems, in the School of Computing and Information. She uses the tools of network analysis and computational modeling to look at how skills and ideas interact in collaborative environments.

Abstract:

This will be a hands-on talk in which I will illustrate challenges and pitfalls of manipulating and statistically describing data which are extremely skew and possibly large in quantity. Motivated mainly by models from economics (e.g., firms and cities) and finance, datasets with millions of observations, both continuous and discrete, will be analyzed and plotted, with and without binning. There will be some discussion of parameter estimation but this will not be the primary focus of the talk. Heavy-tailed size distributions, the corresponding weighted-distributions, and the related ideas of moment distributions and size-biased sampling will all be discussed. Terminology such as ‘scaling’ and ‘scale-free’ will be unpacked and illustrated. The relation of Zipf-style rank-size plots to probability distributions will be developed. The competition between power law and lognormal distributions to represent heavy-tailed data will be addressed, including the ability of the lognormal to mimic a power law. Finite size effects and truncated distributions will also be discussed.

Abstract:

According to classical economic theory, removing money from the economy should result in prices falling. However, there is some evidence of so-called Cantillon effects where some prices initially move in the wrong direction. There exists an agent-based model that replicates this phenomenon using a network model. My focus is on the econometric plausibility of this model. I will demonstrate a preliminary statistical model to defend the assumptions made for the agent-based model.

Bio:

John Schuler is a fifth year PhD student in the Department of Economics at George Mason University. John graduated from American University with an MS in statistics in 2017 and from St. John’s College with a BA in liberal arts in 2009. He also spent a year at the University of Maryland, College Park studying mathematics post-baccalaureate, and more recently he spent the Spring 2018 semester as a visiting assistant professor at Washington College. His research interests involve the interface of computer science and economics as well as agent based macroeconomics.

Abstract:

The use of incentives in stormwater programs is a common feature that is used to motivate private property owners as well as land developers to adopt specific types of stormwater management infrastructure at the site or parcel level. While incentives for land developers, such as reduced plan review time or reduced plan review fee for projects that utilize specific BMPs, such as green stormwater infrastructure (GSI), are helpful policies in driving implementation of innovative stormwater practices, this implementation is limited to land development activity. Approximately 75% of existing impervious cover is associated with land development activities that took place prior to federal legislation focused on urban stormwater runoff. The implication of this is that a majority of impervious cover across the U.S. discharges runoff that is either inadequately managed or not managed at all. Until we address these existing areas, impacts from these areas will continue to impact our waters. This is reflected by evolving regulations that require a certain amount of existing impervious cover to be retrofitted to provide stormwater management. Many cities, such as Milwaukee, Seattle and Atlanta, also have retention volume goals as part of a regulatory program as well as an effort to increase the resilience and sustainability of urban areas.

The motivation to retrofit existing impervious areas is a driver to retrofit both public and private lands. Public rights-of-way (ROWs) are often challenging to work within, and there is a limited amount of public ROW available. Overall, 60% of land in the U.S. is privately held, with large portions of these areas located in large public parks in Mountain Region states. The result is that many states have private land ownership rates at 80% or higher; clearly this is a need to find ways to locate urban stormwater retrofits on private lands.

The default method of incentivizing private land owners to adopt onsite stormwater infrastructure is a stormwater fee reduction according to the 2018 Black and Veatch Stormwater Utility Survey. The limitation with this approach comes in when a community does not have a stormwater utility established, which is the case for at least 2/3 of the regulated stormwater entities in the country. And even if a utility exists, the fees are often not high enough to make economic sense for onsite adoption when considering payback periods and other financial metrics. The reason for this is simple – stormwater utility fees are set at a level/rate to pay for needed stormwater programmatic and implementation rather than to create an effective financial incentive for private parcel owners to adopt BMPs onsite. The result of this are participation rates in incentive-based stormwater infrastructure on-site investments of 2-5% or lower associated with traditional incentive programs, which also include cost-sharing and subsidy programs as well. Due to this reason, communities are considering market-based approaches, such as stormwater credit trading, that can reward private property owners in a more robust way for onsite BMP adoption.

While market-based programs hold much promise, the focus of research in this area has been (rightly) on program architecture and policies with the view of “if we build it, they will come”. However, this leaves a void in understanding on how parcel owners will respond to market-based option. Questions regarding the motivations for adoption, how decisions on adoption are made, and how adoption on parcels affect adoptions in neighboring areas or parcels. This presentation will outline research done to begin to address the “consumer behavior” view of BMP adoption. Specifically, a socio-economic model based upon cellular automata-style agent-based modeling will be presented to illustrate a method to capture the adoption of GSI across multiple urban neighborhoods that comprise a city-wide landscape.

This model – the Green Stormwater Infrastructure Social Spatial Adoption (G-SSA) model – provides insights on neighboring effects, spatial dynamics, and decision-making aspects of GSI adoption based upon social theory. Model sensitivity analysis highlights the significance of social and spatial model elements to overall GSI adoption rates and pattern. An applied G-SSA model has been developed and explored to simulate the complex emergent patterns for GSI adoption across a specific cityscape (Washington, D.C.). Applied G-SSA model output is consistent with expected model behavior as well as observed and document GSI adoption patterns in Washington, D.C.

Bio:

Seth Brown is the Principal and Founder of Storm and Stream Solutions, LLC, a consulting firm providing a range of services in the stormwater sector. He is the Executive Director of the National Municipal Stormwater Alliance (NMSA), which is a 501.c.3 representing state and regional organizations focused on stormwater issues. Seth is also the Senior Stormwater Advisor with the Water Environment Federation (WEF) where he supports national-level technical and policy initiatives associated with stormwater issues. He has a PhD from George Mason University in the Department of Civil, Environmental and Infrastructure Engineering and is a licensed professional engineer in the state of Maryland.

Abstract:

Cities are growing physically and digitally, faster than ever. The ever-growing population of cities, along with their intrinsic inaccessibility and inequity, has created difficulties with traffic, mobility, safety, health, pollution, and misinformation among many others. The physical and digital growth of cities outpaces the effort to address the aforementioned issues.

The growing popularity of online social networks (OSN) and World Wide Web (WWW) has remarkably expedited the information dissemination among individuals and groups. Digital data is the lifeblood of modern cities. Today, it’s being captured in large quantities at unprecedented rates via ubiquitous devices and sensors. Unfortunately, most of the generated data is wasted without extracting potentially useful information and knowledge because of the lack of established mechanisms that benefit from the availability of such data. That has turned the discussion from how the massive amounts of data are collected to how knowledge can be extracted from them.

Smart cities become smart not only because they automate routine functions serving the citizens, buildings, and traffic systems but also because they enable monitoring, understanding, analyzing and planning the city to improve the efficiency, equity, and quality of life for its citizens in real time. With physical and digital problems on one hand and big data on the other, smart cities strive to juxtapose them to find inexpensive solutions. How the digital data should be processed to help solve problems in cities remains one of the major areas of research and development in recent years and the focus of this talk

Bio:

Mahdi Hashemi, Ph.D. is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University. He leads the Machine Learning and Smart Cities Group, where he also specializes in intelligent transportation, spatial-temporal data and web/social media analytics. Dr. Hashemi earned his Ph.D. in Computing and Information from the University of Pittsburgh

Abstract:

Fire evacuation modeling benefits from application of social science both in terms of accuracy and external validation. I will discuss PrioritEvac, a novel agent-based model which incorporates social dimensions of fire evacuation and responses to fire and smoke. It uses individual priorities, making for a dynamic approach that allows greater agency and nuance. PrioritEvac is programmed in NetLogo and validated using extensive data collected from the Station nightclub fire. The statistical analysis of the results of the model indicate that, compared to historical patterns, it reproduces along multiple metrics including a mean of 114 deaths (std. dev.=38) over 50 runs, which puts the actual result of the fire within one standard deviation of the mean results of the simulation. Overall, the mean differential along all the metrics is 79, significantly outperforming all published ABM models of the Station nightclub fire that did not incorporate social relationships.

Bio:

Eileen Young is a PhD student in the Disaster Science and Management program. She graduated in 2016 from the University of Wisconsin at Whitewater with a Bachelor of Science in Liberal Studies and in 2019 from the University of Delaware with a Master of Science in Disaster Science and Management. She is currently engaged in research using agent-based modeling of social factors in evacuation from fire. Her interests are in collective behavior, resilience in vulnerable populations, and the evolving role of computing and big data in social science.

Abstract:

TBA

Bio:

TBA