Generation of Reusable Synthetic Population and Social Networks

Building on our work on synthetic populations, Na (Richard) Jiang, Bill Kennedy, Hamdi Kavak, and myself have a new paper which was presented at the 2021 Annual Modeling and Simulation (ANNSIM) Conference entitled "Generation of Reusable Synthetic Population And Social Networks for Agent-Based Modeling." Rather than go into details about the paper, below is the abstract and as the conference was virtual, the presentation which accompanies the paper was prerecorded by Richard and is embedded below. If you want to find out more, at the bottom of the post there is a link to a draft of the paper.

Abstract: Within agent-based models, agents interact with each other (e.g., social networks) and their environment, and it is through such interactions more aggregate patterns emerge (e.g., disease outbreaks, traffic jams). While the popularity of agent-based modeling has grown, one challenge remains, that of creating and sharing realistic synthetic populations which incorporate social networks. To overcome this challenge, this paper introduces a new approach that creates a reusable synthetic population using the New York Metro Area as a study area. Our method directly incorporates social networks (i.e., connections within a family or workplace) when creating a synthetic population. To demonstrate the utility and reusability of the synthetic population and to highlight the role of social networks, we show two example applications: traffic dynamics and the spread of a disease. These applications demonstrate how our synthetic population method can be easily utilized for different modeling problems. 

Keywords: Synthetic Population, Agent-Based Modeling, New York, Traffic Dynamics, Disease Models.

  Full Reference: 

Jiang, N., Crooks, A.T., Kennedy, W.G., and Kavak, H. (2021), Generation of Reusable Synthetic Population And Social Networks for Agent-Based Modeling, 2021 Annual Modeling and Simulation Conference (ANNSIM). (pdf)

Kinetic Action and Radicalization

In the past we had posted on models of radicalization, but such models were rather abstract.  However in a recent paper entitled "Kinetic Action and Radicalization: A Case Study of Pakistan" which was presented at the  International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (or SBP-BRiMS for short) we take such work a step further. 

In the paper, Brandon Shapiro and myself develop and present a simple agent-based model informed by theory and calibrated using empirical data to explore the relationship between kinetic actions (i.e., drone strikes) and terrorist attacks in Pakistan from 2004 through 2018. The data itself came from the Bureau of Investigative Journalism data as our source for Pakistan drone strikes (i.e., kinetic actions) and the National Consortium for the Study of Terrorism and Responses to Terrorism( START)  Global Terrorism Database (GTD) as our source for terrorist incidents. 

Rather than try to pinpoint and define the motivating factors which might influence somebody down a path toward radicalization, our model that incorporated a distributed lag model to characterize the inter-dependencies between drone strikes and terrorist attacks observed in Pakistan. Based on parametric and validation tests, the model simulates a terrorist attack curve which approximates the rate and magnitude observed in Pakistan from 2007 through 2018. If this sounds of interest, below we provide the abstract to the paper, along with some images of model graphical user interface, the model logic and some of the results. The model itself was created in NetLogo and is available at: https://bit.ly/3qLJynv  (along with the data and detailed ODD of the model). At the bottom of the page you can find the full citation and a link to the paper.

Abstract. Drone strikes have been ongoing and there is a debate about their benefits. One major question is what is their role with respect to radicalization. This paper presents a data-driven approach to explore the relationship between drone strikes in Pakistan and subsequent responses, often in the form of terrorist attacks carried out by those in the communities targeted by these counter-terrorism measures. Our analysis of news reports which dis-cussed drone strikes and radicalization suggests that government-sanctioned drone strikes in Pakistan appear to drive terrorist events with a distributed lag that can be determined analytically. We then utilize these news reports to inform and calibrate an agent-based model which is ground-ed in radicalization and opinion dynamics theory. In doing so, we were able to simulate terrorist attacks that approximated the rate and magnitude ob-served in Pakistan from 2007 through 2018. We argue that this research effort advances the field of radicalization and lays the foundation for further work in the area of data-driven modeling and kinetic actions.

Keywords: Radicalization, Data-driven modeling, Drone strikes, Terrorism, Pakistan, Agent-based modeling.

Radicalization model’s graphical user interface.

The agent-based model flow diagram.

Terrorist attacks simulated by radicalization model qualitatively agree with real-world system.

Full Reference:

Shapiro, B. and Crooks, A.T. (2021), Kinetic Action and Radicalization: A Case Study of Pakistan, in Thomson, R., Hussain, M.N., Dancy, C.L. and Pyke, A. (eds), Proceedings of 2021 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation, Washington DC., pp 321-330. (pdf)