Research Interests

Use-inspired Research & Model Development Aimed Towards Complex System-level Interventions Using Agent-based Modeling & Social Network Analysis

1.1 Complex Systems Research

• Shaheen, J. A. E.  Are Agent-based Models Universal Approximators?, Academia Letters, 2021.https://doi.org/10.20935/al1142
• Axtell, Rob; Shaheen, J. A. E.; Agent-Based Models with Qualitative Data are Thought Experiments, Not Policy Engines, Futures & Foresight Science, 2021. https://doi.org/10.1002/ffo2.87
• Root, Hilton; Shaheen, J. A. E.; Tanca, Dersu; Vizzard, James. Scale and Complexity in Political Economy: A Question of Liberty, Journal of Interdisciplinary History, 2021. https://doi.org/10.1162/jinh_a_01664.

1.2 Use-inspired Basic Research

Use-inspired research is simply research where the knowledge product is inspired by how it will be used. In Pasteur’s quadrant: Basic science and technological innovation by Donald Stokes (1997) you’ll learn about Louis Pasteur who made significant contributions to microbiology by studying the dynamics and mechanisms by which anthrax, cholera, and even rabies evolve, and in so doing, learning how to intervene in those biological “systems”. It is often the case that scientists that come from traditional academic pathways foresee a tension between this “engineering” approach and the rest of Science. I find no such tension.

In fact, it’s probably true that at least in my experience, a use-inspired approach can help solve difficult problems not easily undertaken with the traditional scientific method. Having been trained as an engineer, a use-inspired engineering approach for Complex Systems is not only optimal but it is also natural to me.

1.3 Intervention as a Research Paradigm

Interventions as research has gained more popularity after the SARS-COV 2 pandemic, but I’ve been developing this approach prior to it and specifically at the intersection of networks (social network analysis) and agents (agent-based modeling). I have spent my postdoctoral studies focused on developing a generalized framework for intervening in systems. The way I describe it is using (James) Coleman’s boat which you can find in his 1998 book titled Foundations of Social Theory.

In most systems it is often the case that there is some state (system/aggregate level) variable, X, which is known and through some analysis we can predict some future state of that system, Y, which can be known. These system-level variables are some amalgamation/aggregation/composition or another operative function of some underlying grouping of micro-variables. For example, the system level variable can be the GDP of a country, and the underlying variables can be firm efficiency, access to education, population density and so on. Obviously, the level of abstraction can be taken lower and lower from the macro to the meso, to the micro, to the super micro, to the individual and even beyond.

 

My research paradigm is then an effort to identify the system’s structure, use computational methods to aggregate and perturb the lower-level variables and measure the outcome of a given intervention or a class of interventions. In other words, I compute the results of a given intervention given that I can understand how the system works under stationary conditions. I restrict myself to system that can be modeled using my core technical skills, agent-based and network techniques.

Recently, I gave a presentation on my approaches using published products from my postdoctoral studies, and the presentation slides are posted below so you can look through them.

 

2.0 Specific Projects (Chronological)

2.1 Policy: Information Operations & Social Media Warfare (2015-2018)

At the beginning of my research journey I relied more on heuristics for system-level intervention exploration. From that humble beginning several high-impact policy reports as well as some conference papers were delivered with a focus on social network analysis of social media networks.

• Shaheen, J. A. E. (2015). Network of Terror: How Daesh Uses Adaptive Social Networks To Spread its Message. In NATO Stratcom Centre of Excellence. 
• Shaheen, J. A. E. (2016). Reproduction of a Social Media Network Using an Agent-based Model: The Case of ISIS/ISIL/Daesh. International Network for Social Network Analysis First North American Network Conference, 1–17. Washington, DC.
• Zgryziewicz, Rafal; Grzyb, Tomasz; Fahmy, Shahira; Shaheen, J. A. E. (2015) Daesh Information Campaign and its Influence, NATO STRATCOM COE Online Journal ISBN: 978-9934-8582-1-5 
• Shaheen, J. A. E., (2018) Challenges of Growing Social Media Networks From the Bottom-Up Through the Agent Perspective, Proceedings of the Annual Simulation Symposium, Society for Computer Simulation International, 2018, https://doi.org/10.6084/m9.figshare.14109722

2.3 Growing High-fidelity Systems From the Bottom-up (2017-2019)

This was an area of interest during the early part of my doctoral studies. It began when I decided to model a ride sharing service (Uber or Lyft) in my local area in the hope that it would generate more efficient rides. This was somewhat a computational method project where I studied how transient model starting effects (artifacts) affect model outcomes. The project was published as a peer-reviewed book chapter.

• Shaheen, J. A. E. Simulating the Ride-sharing Economy: The Individual Agent Metro-Washington Area Ride-sharing Model, Complex Adaptive Systems: Views from the Physical, Natural, and Social Sciences, 2019. ISBN: 978-1-5108-6014-8, doi.org/10.1007/978-3-030-20309-2 7

 

 

 

2.2 Dissertation: Firms as Biological Creatures (2018-2019)

The title of my dissertation was “Data Exploration in Firm Dynamics: Firm Birth, Life, & Death Through Age Wage, Size and Labor” which you can find here (link) . The dissertation was primarily focused on analyzing over 4.2 Billion US Internal Revenue Service tax records generating over 6.2 Million unique firms and 90 Million non-unique firms over a 17 year period from a survival analysis perspective. This type of mathematical analysis is often found in the study of demographic population growth and decline of biological systems, such as humans or fish.

• Shaheen, J. A. E. (2019). Data Explorations in Firm Dynamics: Firm Birth, Life, & Death Through Age, Wage, Size & Labor. George Mason University.
• Shaheen, J. A. E. (2019). Emerging the U.S. Firm Size Distribution Using 4.2 Billion Individual Tax Records. 12th International RAIS Conference on Social Sciences and Humanities, 74–82. https://doi.org/10.5281/zenodo.2650983

 

2.3 Bottom-up System-level Interventions & Principled Detection Using Agent-based Modeling & Social Network Analysis (2019-2021)

Using a bottom-up approach of simulating a matching system using agent-based approaches and identifying the correct statistical tool from the social network analysis literature, my team of undergraduate students and I proposed a framework for assessing human-computer interactions through an online dating application use-case. The project represented the perfect example of use-inspired research in intervention methodology using agents and networks, and subsequently allowed for the expansion of my research theme into applied human-computer theory.

• Shaheen, J. A. E.; Henley, Collin; McKenna, Liam; Hoang, Steven; Abdulwahab, Fatma; Comparative Agent-based Simulations on Levels of Multiplicity Using a Network Regression: A Mobile Dating Use-case, Applied Sciences, Vol. 12 (4), 1982, 2022. https://doi.org/10.3390/app12041982

 

 

2.3 Top-down Perturbations of Network Interventions Using Centrality Heuristics (2019-2022)

In the previous project, I used a bottom-up approach to demonstrate intervention-focused use-inspired research using agent-based and network methods. However, that assumed that the modeler can always computationally describe the system as-a-whole even when given a principled statistical test is available. More importantly, the dominant centrality perspective in the social network analysis literature is heuristic and descriptive at best yet many network targeting decisions are often being made by government agencies based on unsupported rules of thumb. This was a difficult problem to solve and it took me many incremental attempts, each presented at appropriate venues to solicit feedback. In the end, it had to be a truly interdisciplinary solution drawing on social network analysis and network science.

• Shaheen, J. A. E., Examining Dark Networks Using Information Divergence, Joint Networks Conference: Networks 2021, 2021.
• Shaheen, J. A. E., Targeting in Networks: A Complex Problem, 3rd North American Social Networks Conference, 01/2021. doi.org/10.6084/m9.figshare.14110355
• Shaheen, J. A. E., Towards a Principled, Computational, and Risk-based Perspective in Dark Networks, Intelligence Community Academic Research Symposium & National Academies of Sciences, 2020. doi.org/10.6084/m9.figshare.14110310
• Shaheen, J. A. E., An Empirical Method for Evaluating the Robustness of Centrality Methods: The Case of Dark Networks, Intelligence Community Academic Research Symposium & National Academies of Sciences, 2019. doi.org/10.6084/m9.figshare.14110292
• Shaheen, J. A. E., Target Policy-making Under the Frame of Dark Networks: Strengths, Weaknesses, & Opportunities, The Conference on Politics & Computational Social Science of the American Political Science Association, 2019. doi.org/10.6084/m9.figshare.14110277