Wednesday, September 21, 2022

Mitigation of Supply Chain Disruptions by Criminal Agents

Since the outbreak of COVID, the role of supply chains has been brought front and center in many aspects of our daily lives. For example, the disruption to supply chains can significantly influence the operation of the world economy and this has been shown to permeate and affect a large majority of countries and their citizens. However, it is not just diseases outbreaks that can affect them, but also criminal agents. To this end at the 15th International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (or SBP-BRiMs for short), Abhisekh Rana, Hamdi Kavak, Sean Luke, Carlotta DomeniconiJim Jones and myself have a paper entitled "Mitigation of Optimized Pharmaceutical Supply Chain Disruptions by Criminal Agents."

The paper presents some initial results from a model that explores the disruptions to supply chains by a criminal agent and possible mitigation strategies. We construct a model of a typical pharmaceutical manufacturing supply chain, which is implemented via discrete event simulation. The criminal agent optimizes its resource allocation using a CMA-ES algorithm to maximize disruption to the supply chain. CMA-ES is part of a family of sample-based optimization techniques collectively known as evolutionary algorithms.  Broadly speaking, CMA-ES starts with a sample of random candidate solutions to optimize.  It then iteratively assesses the quality of each candidate solution, then performs resampling based on their quality to produce a new sample of candidates. By combining our supply chain model with our criminal agent, and by leveraging CMA-ES, we attempt to identify the main bottlenecks and the most vulnerable points in the pharmaceutical supply chain. Our findings show criminal agents can cause cascading damage and exploit vulnerabilities, which inherently exist within the supply chain itself. We also demonstrate how basic mitigation strategies can efficaciously alleviate this potential damage.  If this sounds of interest, below we provide the abstract to the paper, along with some of the key figures and at the bottom of the post the full reference and a link to the paper.

Abstract: 

Disruption to supply chains can significantly influence the operation of the world economy and this has been shown to permeate and affect a large majority of countries and their citizens. We present initial results from a model that explores the disruptions to supply chains by a criminal agent and possible mitigation strategies. We construct a model of a typical pharmaceutical manufacturing supply chain, which is implemented via discrete event simulation. The criminal agent optimizes its resource allocation to maximize disruption to the supply chain. Our findings show criminal agents can cause cascading damage and exploit vulnerabilities, which inherently exist within the supply chain itself. We also demonstrate how basic mitigation strategies can efficaciously alleviate this potential damage. 

Keywords: Pharmaceutical supply chains, Criminal agents, Evolutionary computation, Mitigation.

A simplified version of a typical pharmaceutical supply chain.

Design of the criminal agent.
Sample simulations for the baseline model, without any disruption, and attacks at the five main disruption points in the supply chain.

Summary statistics and sample simulations for CMAES optimized disruptions with and without mitigation in place.

Full Reference:

Rana, R., Kavak, H., Crooks, A.T., Domeniconi, C., Luke, S. and Jones, J. (2022), Mitigation of Optimized Pharmaceutical Supply Chain Disruptions by Criminal Agents, in Thomson, R., Dancy, C. and Pyke, P. (eds), Proceedings of the 2022 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation, Pittsburgh, PA., pp 13-23. (pdf)

 

Monday, September 19, 2022

Information propagation on cyber, relational and physical spaces about covid-19 vaccine

It seems that its been a quite some time that we posted about geosocial analysis but in a recent paper with  Fuzhen Yin  and Li Yin entitled "Information Propagation on Cyber, Relational and Physical Spaces about Covid-19 Vaccine: Using Social Media and the Splatial Framework" published in Computers, Environment and Urban Systems we revisit this line of work while at the same time linking it to Covid and vaccination debates. 

Specifically we examine the interaction between cyber, relational (i.e, networks between objects), and physical spaces using the Splatial framework. Through our analysis focused on New York State, we find that non-polarized vaccination debates were observed in cyber, relational, and physical spaces. Furthermore,  we found that while physical space users had less anti-vaccine stance than relational and cyber space users there were strong interactions are observed between physical–relational, and relational-cyber spaces.If this sort of thing interests you. Below we provide the abstract to the paper along with some figures which show the study area, our methodology and some of the results. While at the bottom of the post we provide the full reference and the link to the paper.

Abstract:

With the advent of social media, human dynamics studied in purely physical space have been extended to that of a cyber and relational context. However, connections and interactions between these hybrid spaces have not been sufficiently investigated. The “space-place (Splatial)” framework proposed in recent years allows capturing human activities in the hybrid of spaces. This study applies the Splatial framework to examine the information propagation between cyber, relational, and physical spaces through a case study of Covid-19 vaccine debates in New York State (NYS). Whereby the physical space represents the regional boundaries and locations of social media (i.e., Twitter) users in NYS, the relational space indicates the social networks of these NYS users, and the cyber space captures the larger conversational context of the vaccination debate. Our results suggest that the Covid-19 vaccine debate is not polarized across all three spaces as compared to that of other vaccines. However, the rate of users with a pro-vaccine stance decreases from physical to relational and cyber spaces. We also found that while users from different spaces interact with each other, they also engage in local communications with users from the same region or same space, and distance-based and boundary-confined clusters exist in cyber and relational space communities. These results based on the Splatial framework not only shed light on the vaccination debates but also help to define and elucidate the relationships between the three spaces. The intense interactions between spaces suggest incorporating people’s relational network and cyber presence in physical place-making.

Keywords: Covid-19, Vaccination, Social media, Social network analysis, Community detection, Urban informatics
Schematic representation of the three spaces: cyber, relational and physical spaces.

Map of study area (NYS) with the primary road system. Red dots denote collected vaccine-related tweets in NYS.

Research workflow to investigate the propagation of different opinions between three spaces: cyber, relational and physical spaces.

Network visualization of the eight top large communities in relational space. (A) Visualization of communities using ForceAtlas layout. (B) Project communities into physical space. Nodes without location information are placed outside of NYS.

The hybrid space network shows the information propagation between physical and relational spaces. (A) shows the network of all tweets, (B) shows the pro-vaccine tweets, and (C) shows the anti-vaccine tweets.
 
Full Reference:

Yin, F., Crooks, A.T. and Yin, L. (2022), Information Propagation on Cyber, Relational and Physical Spaces about Covid-19 Vaccine: Using Social Media and the Splatial Framework, Computers, Environment and Urban Systems. Available at: https://doi.org/10.1016/j.compenvurbsys.2022.101887.  (pdf)

Wednesday, August 31, 2022

Mesa-Geo: ABM and GIS in Python

In the past I have blogged a lot on creating geographically explicit models in NetLogo, Repast and Mason but not so much about models created in Python. Even though Python is growing in popularity and their exists an agent-based modeling framework in Python called Mesa (click here to see a paper on this). But this lack of blogging about geographically explicit agent-based models will be changing as I have been recently working with Boyu Wang (a PhD student here at UB) who together with others have been developing Mesa-Geo. To give you as sense of Geo-Meas, below are some example models that can be downloaded from https://github.com/projectmesa/mesa-geo or https://github.com/wang-boyu/agents-and-networks-in-python. These models range from rainfall flowing over a digital terrain model to Schelling types of models using points  and polygons as agents, to that of agents using road networks to navigate over an area.  In a future post we will go into more details but if you are interested in creating geographically explicit agent-based models in Python please check out the repositories.

 Mesa-Geo example models: (a) Rainfall, (b) Population, (c) Schelling (polygons) , (d) Schelling (points & polygons), and (e) Agents and networks.

Thursday, July 14, 2022

Drone strikes and radicalization

In the past we had posted on models of radicalization, but such models were rather abstract.  Building on this previous work Brandon Shapiro and myself have a new paper entitled "Drone Strikes and Radicalization: An Exploration Utilizing Agent-Based Modeling and Data Applied to Pakistan" which has recently been published in Computational and Mathematical Organization Theory journal. In the paper we develop and present an 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://www.comses.net/codebase-release/30540ae3-486b-44e4-8ff0-785575433af0/  (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:

The employment of drone strikes has been ongoing and the public continues to debate their perceived benefits. A question that persists is whether drone strikes contribute to an increase in radicalization. This paper presents a data-driven approach to explore the relationship between drone strikes conducted in Pakistan and subsequent responses, often in the form of terrorist attacks carried out by those in the communities targeted by these particular counter terrorism measures. Our exploration and analysis of news reports which discussed drone strikes and radicalization suggest that government-sanctioned drone strikes in Pakistan appear to drive terrorist events with a distributed lag that can be determined analytically. We leverage news reports to inform and calibrate an agent-based model grounded in radicalization and opinion dynamics theory. This enabled us to simulate terrorist attacks that approximated the rate and magnitude observed 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 drone strikes.  
Keywords: Radicalization, Data-driven modeling, Drone strikes, Terrorism, Pakistan , Agent-based modeling.
Pakistan radicalization model’s graphical user interface. From left to right: model input param- eters, the agents’ social network and resulting model outputs

The agent-based model flow diagram.

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

Full Reference: 

Shapiro, B. and Crooks, A.T. (2022) Drone Strikes and Radicalization: An Exploration Utilizing Agent-Based Modeling and Data Applied to Pakistan, Computational and Mathematical Organization Theory. Available at https://doi.org/10.1007/s10588-022-09364-1. (pdf)


Thursday, July 07, 2022

Call for papers: GeoSim 2022

The GeoSim 2022 workshop focuses on all aspects of geospatial simulation as a paradigm to understand, model, and predict spatial phenomena and aid decision making. New simulation methodologies and frameworks, not necessarily coming from the SIGSPATIAL community, are encouraged to participate. Also, this workshop is of interest to everyone who works with spatial data. The simulation methods that will be presented and discussed in the workshop should find a wide application across the community by producing benchmark datasets that can be parameterized and scaled. Simulated data sets will be made available to the community via the website.

The workshop seeks high-quality full (8-10 pages) and short (up to 4 pages) papers that will be peer-reviewed. Once accepted, at least one author is required to register for the workshop and the ACM SIGSPATIAL conference, as well as attend the workshop to present the accepted work which will then appear in the ACM Digital Library.

We solicit novel and previously unpublished research on all topics related to geospatial simulation including, but not limited to:
  • Disease Spread Simulation
  • Urban Simulation
  • Agent Based Models for Spatial Simulation
  • Multi-Agent Based Spatial Simulation
  • Big Spatial Data Simulation
  • Spatial Data/Trajectory Generators
  • Environmental Simulation
  • GIS using Spatial Simulation
  • Modeling and Simulation of COVID-19
  • Interactive Spatial Simulation
  • Spatial Simulation Parallelization and Distribution
  • Geo-Social Simulation and Data Generators
  • Social Unrest and Riot Prediction using Simulation
  • Spatial Analysis based on Simulation
  • Behavioral Simulation
  • Verifying, and Validating Spatial Simulation
  • Applications for Spatial Simulation


Workshop information

Submission deadline: September 01, 2022
Author Notification: September 27, 2022
Workshop date: November 01, 2022


Workshop website: http://www.geosim.org
Submission site: https://easychair.org/conferences/?conf=geosim2022