Call for Abstracts: Geosimulation: Human Dynamics, AI-Enabled Agents, and Spatial Decision-Making

We are organizing sessions on "Geosimulation: Human Dynamics, AI-Enabled Agents, and Spatial Decision-Making" at the AAG Annual Meeting in New York City, February 8-12, 2027.

Building on our geosimulation sessions of the past three years, we continue to welcome a wide range of studies exploring simulation theories, data, methodologies, and frameworks. We are interested in case studies that apply geosimulation to real-world challenges, as well as research that develops and evaluates AI-enabled geosimulation models. Potential topic areas include, but are not limited to:

• Geosimulation Models and Applications
• Conceptual Geosimulation Models
• General-Purpose Geosimulation Frameworks
• AI and Geosimulation
• Agent Behavior, Decision-Making, and AI Agents
• Data Generation Frameworks
• Validation and Verification for Geosimulation
• Digital Twins
• Microsimulation
• Multi-Agent Systems
• System Dynamics Models

Interested in presenting? Submit your abstract to AAG by October 15, 2026. Then send your title, abstract (up to 250 words), and AAG abstract code to Boyu Wang (bwang44@buffalo.edu) and Ying Zhou (yzhou59@buffalo.edu) by November 5, 2026.

Organizers: Andrew Crooks, Na (Richard) Jiang, Fuzhen Yin, Boyu Wang, Ying Zhou, Joon-Seok Kim, Taylor Anderson, Yahya Gamal, Wenwu Tang, Sarah Wise, Yao-Yi Chiang, Sean Ahearn, Liliana Perez

Please share with colleagues and students who may be interested!

More details

EPB: Collaborations and Topics Over Decade

When Environment and Planning B: Urban Analytics (EPB) turned 50 we wrote a short commentary about how the topics in the journal have evolved over the years. In the latest issue of the journal we (Na Jiang, Joana Barros, Seraphim Alvanides, Jinlin Wu and myself) revisited this, while at the same time explored how collaborations and topics have been evolving along with how this links to the new Editorial Advisory Board which has recently been selected to reflect the journal’s thematic areas of focus and the breadth of expertise needed to guide it into the future. 

If you wish to find out more, we encourage you to read the commentary (a link is provided at the bottom of this post). However, to give a sense of some of the things we explore, below we show some of the images relating to the commentary ranging from our workflow to some of our results from the analysis of authors, paper titles and abstracts, and how through topic modeling and community detection we can explore how the collaboration networks and topics have changed over time. 

The workflow.

 

Visualization of the EPB author network.

Evolution of the largest 10 communities by decade: (a) The Number of Active Members; (b) The Number of Papers Published; (c) Michael Batty’s Collaboration Network.

Topics evolution overtime.

Full reference: 

Crooks, A.T., Jiang, N., Barros, J., Alvanides, S., & Wu, J. (2026). Environment and Planning B: Collaborations and Topics Over Decades, Environment and Planning B, 53(6), 1189-1199. https://doi.org/10.1177/23998083261474805. (pdf)

A Dual-LLM Supervised Workflow for Replicating Agent-based Models

In the past we have written about large language models (LLMs) and how these can be used in agent-based modeling. However, these previous posts only touched the surface on what is possible. One area we are currently exploring is how LLMs can aid in model replication, which is a major challenge in agent-based modeling. In the sense, there are countless agent-based models but very few are replicated and if  a model is to withstand the test of time, replication is needed. 

To this end, Boyu Wang, JoAnn Lee and myself have an extended abstract entitled: "A Dual-LLM Supervised Workflow for Replicating Agent-based Models: From NetLogo to Mesa" at the 2026 Social Simulation Conference. In this work we demonstrate how LLMs can be used to replicate an exiting model into another modeling package, Specifically, we take the Ya-TASERPS model which was initially implemented in NetLogo and re-implement it in Mesa via a  LLM workflow. 

The workflow uses two GPT-5.4 models with distinct LLM roles under continuous human mediation. One LLM role handles planning and evaluation, from checking source code, decomposing tasks into phases, and reviewing whether implementations are consistent with the source NetLogo model. The other LLM role performs the actual implementations in phases, as outlined by the first LLM role.

Each task phase defines acceptance criteria, proceeds through implementation and verification, and ends with human review before progression. The human researcher mediates both LLM roles, resolves ambiguities, and decides whether the resulting model state is acceptable. The aim is not towards full automation but to reduce prompt drift, limit uncontrolled rewrites, and keep generated code subordinate to explicit validation. 

If you want to read about our replication, why we chose the Ya-TASERPS model along with our findings, please feel free to read the paper and more information about this can be found at: https://github.com/wang-boyu/Ya-TASERPS.

The NetLogo-to-Mesa replication workflow utilizing two distinct LLM roles.

Large-run prosocial outcomes across the six user-controlled inputs in NetLogo and Mesa with the same set of 729 parameter combinations, 10 random seeds, and 1800 days per run. The two implementations preserve the same broad significance and directional patterns.

Full Reference: 

Wang, B., Lee, J. and Crooks, A.T.  (2026), A Dual-LLM Supervised Workflow for Replicating Agent-based Models: From NetLogo to Mesa. Social Simulation Conference 2026, Durham, UK. (pdf)

New Paper: Online Interactions, Mutual Assistance and the Power of Weak Ties

In the past we have written about disasters and how one can model peoples reactions to them or how one can mine social media or mobility data to explore people's responses to them. In a new paper published in the Annals of the American Association of Geographers, Fuzhen Yin, Lucie Laurian and Emmanuel Boamah and myself continue this line of research. Specifically we explore how people exchanged resources and coordinated mutual aid via Facebook during the 2022 Buffalo Blizzard.

The paper itself is entitled "Surviving the Buffalo Blizzard: Online Interactions, Mutual Assistance and the Power of Weak Ties" In the paper we describe how we manually collected blizzard-related conversations from two Facebook groups "Buffalo S.T.O.R.M" and "Buffalo Blizzard". After which we utilize machine-learning (e.g., support vector machines) to identify mutual-aid messages which were then categorized them into four groups: aid requests, aid offers, emotional support and other. From which we then constructed a social network of users interactions during the blizzard to identify aid requests, aid offers, and emotional support messages during a time of crisis. As such our study contributes to the growing literature on human dynamics by examining spontaneous mutual aid during the blizzard and highlights how online mutual assistance operated through ‘phygital’ (physical–digital) integration.

If this sounds of interest, and you wish to find out more with respect to our findings, below you can read the abstract to the paper, see some of the figures which describe our research methodology and results while at the bottom of the post you can find a link to the paper itself.

Abstract: 

In December 2022, Buffalo, New York, experienced a once-in-a-generation blizzard. The four-day lake-effect snow accompanied by storm-force winds knocked down power lines, halted emergency services in several towns and resulted in forty-seven fatalities of residents who lost heat and power or were trapped in the snow. In response to the storm, Buffalonians demonstrated strong solidarity through quickly self-organized Facebook groups to exchange resources and coordinate mutual aid. Our study examines the emergence of grassroots mutual assistance through online–offline interactions and its impact on resilience in the physical world. We manually collected blizzard-related conversations, used machine learning to identify mutual-aid messages, and applied social network analysis to examine users’ interactions. Our findings reveal that Facebook users delivered life-saving assistance through online conversations involving requesting and offering practical, informational, and emotional support. The Facebook blizzard communities developed networks of weak ties that expanded access to vital resources and facilitated the flow of information and materials among disconnected residents. This research highlights virtual spaces as digital urban commons where strangers can benefit from emerging social capital during crises. It also offers insights for emergency management agencies seeking collaborations with grassroots online communities to develop formal–informal mutual aid strategies for future crises.

Keywords: Mutual aid, winter storm/blizzard, crisis informatics, weak ties, social network analysis, machine learning, social media.

Diagram of analysis workflow.

Geographical distribution of places mentioned in the Blizzard Facebook groups. (a) Kernel density map based on precise point locations. (b) General areas at three spatial levels: neighborhoods in Buffalo (purple), cities and towns (blue), and county subdivisions (green). The orange dashed line delineates the boundary of the kernel density map. Line widths and label sizes are proportional to each area’s prevalence in the online discussions.

Classifying mutual aid messages into four categories: request for support, aid offers, emotional support and other (n=9,599).

Tripartite message network capturing the information flow from posts to comments, from comments to replies, and within replies.

Social network of mutual aid interactions. (1) Users’ degree centrality with a log-transformed x-axis. (2) Users’ betweenness centrality with a log-transformed x-axis. (3) Social networks of users’ online interactions, highlighting four clusters: A, B, C, D. (4) Size distribution of detected communities. (5) Users’ composition in detected communities.

Full Reference: 

Yin, F., Laurian, L., Crooks, A.T. and Boamah, E. (2026), Surviving the Buffalo Blizzard: Online Interactions, Mutual Assistance, and the Power of Weak Ties, Annals of the American Association of Geographers. https://doi.org/10.1080/24694452.2026.2707171 (pdf)

Evaluating the Feasibility of ChatGPT for Mapping Building Attributes

In the past we have written about using Multimodal Large Language Models (MLLMs)  like  ChatGPT for coding of models and also  analyzing images. One advantage we see for MLLMs is that unlike traditional approaches that require extensive expertise in computational analysis, such as computer vision and deep learning, MLLMs leverage pre-trained capabilities that simplify the analytical process. This accessibility enables a larger group of researchers to incorporate MLLMs in their analyses when it comes to studying the form and function of cities at scale. To this end, we (Qingqing Chen, Linda See and myself) have new book chapter entitled "Evaluating the Feasibility of ChatGPT for Mapping Building Attributes" published in the open access book: "Geography According to Foundation Models" edited by  Krzysztof Janowicz, Rui Zhu, GengchenMai, Song Gao. Yingjie Hu, Zhangyu Wang, Ling Cai and Lauren Bennett.

In this chapter we evaluate the potential of MLLMs, in our case ChatGPT, to extract building attributes (e.g., age, use and height) from Mapillary street view images.  We find that ChatGPT was good at extracting some information and less good in other cases. For example it identified correctly 87% of the residential buildings. We also discuss ways to improve the results (e.g., using higher quality street view images, altering and refining the prompts). If you wish to find out more about our findings we encourage you to read the chapter. To give you a better sense of this research, below we provide the abstract to the paper, our case study area along with our workflow and a sample of the results. Finally at the bottom of the post, you can find the full reference to the chapter along with a link to it. 

 

Abstract:

With increasing rates of urbanization, many challenges are emerging regarding urban sustainability such as the energy usage of buildings. Coinciding with this is the growing attention of urban climate models for energy demand estimation and climate adaptation strategies. However, the applicability of these models is constrained by the lack of detailed urban surface information. Therefore, creating comprehensive datasets that capture urban surface information at a granular scale is crucial for responding to our rapidly urbanizing world. Recent advancements in Multimodal Large Language Model (MLLMs) have opened new opportunities in urban studies, offering accessible methods for information extraction. In this chapter we explore the feasibility of ChatGPT to extract building attributes from images. Taking New York City as a case study, we collect building images from Street View Imagery and process them through ChatGPT by posing specific questions to extract building attributes (e.g., height, functions, age). These attributes are then compared with authoritative data. The proposed method helps address the current dearth of fine-grained surface data on urban issues, therefore enhancing the accuracy and utility of urban climate models. Overall, this study demonstrates the practical applications of ChatGPT in geographic knowledge extraction, advancing the understanding of MLLMs in geographic contexts, and more broadly to the discourse on Artificial Intelligence (AI) in urban modeling and climate science.

The spatial distribution of Mapillary images within the study area, shown on the left, and the distribution of images by variance showing increasing image quality on the right.

An overview of the research workflow.

Comparison of the building period of construction from the ground truth data and the classifications from ChatGPT. (a) A confusion matrix which details the distribution of buildings classified within each period by ChatGPT compared to the ground truth data; (b) A chord diagram illustrating the patterns of agreement and confusion among the categories.

Comparison of building type classifications. (a) A confusion matrix detailing the distribution of ChatGPT’s classifications against the hand labels from experts; (b) A chord diagram illustrating the proportion of classifications for each building types as labeled by experts compared to ChatGPT’s classifications.

A comparison of building heights from ChatGPT and the NYC Open Data. (a) The correlation of height between the ground truth and ChatGPT; (b) The distribution of ground truth heights and the predicted heights; (c) The difference in the heights.

Full Reference:

Chen, Q., See, L. and Crooks, A.T. (2026), Evaluating the Feasibility of ChatGPT for Mapping Building Attributes,  in Janowicz, K., Zhu, R., Mai, G., Gao, S., Hu, Y., Wang, Z., Cai, L., and Bennett, L. (eds), Geography According to Foundation Models, IOS Press, Amsterdam, The Netherlands, pp. 107-120. (pdf)

New Paper: Exploring Fear in Urban Environments

In the past we have written about how we have used social media to study a plethora of topics with respect the the form and function of cities among many other things. But one thing we have not explored is fear and more specifically fear of crime and how this can be mined through geosocial media. 

This has now changed with a new paper entitled "Exploring Fear in Urban Environments: Place and Space Analysis of Social Media Data" which has recently been published in Applied Geography.  In this paper, Ying Zhou and myself extract fear related posts from social media and examine the places and spaces where people experience fear, as well as the factors that contribute to it in New York City. 

We do this by utilizing Natural Language Processing (NLP) techniques for sentiment and text analysis, including a RoBERTa-based emotion classification model and the BERTopic model for topic modeling. The former model narrowed the raw data to those with the dominant emotion of fear, and the latter analyzed space- and place-related features that contribute to the fear sentiment. Then, the selected social media data were analyzed using spatial clustering methods (i.e., Hotspot Analysis (Getis-Ord Gi*) and Local Moran’s I) and compared with urban crime data for weekly trends and spatial patterns. As such the paper has the following research objectives:

1. exploring places where people expressed fear through social media; 
2. making comparisons between safety-related fear and crime from the perspective of both time and space; 
3. extracting urban environmental and social features that lead to fear.

If this sounds of interest, and you wish to find out more with respect to our findings, below you can read the abstract to the paper, see some of the figures which describe our research methodology and results while at the bottom of the post you can find a link to the paper itself. Finally the code we utilized in the paper can be found at https://osf.io/y7xfc/overview.

Abstract:

One goal of creating livable cities is to enhance public safety. While previous research in urban studies has focused on correlations between physical environments and crime, it has typically relied on criminal statistics. However, fear of crime is an emotional response to perceived risks rather than a direct reflection of crime levels, so it cannot be analyzed solely by crime data. Additionally, urban planning today has gradually shifted its focus from a top-down to a bottom-up approach, making it essential to understand and foster spaces where residents feel safe. This research examines the spaces and places where people experience fear, as well as the factors that contribute to it, in New York City. We utilized social media data to gather people’s expressions of the city and identified posts expressing fear emotion using the RoBERTa-based model and a rule-based classifier. Then, the selected social media data and crime were compared temporally by weekly trends and spatially by clustering methods (i.e., Hotspot Analysis (Getis-Ord Gi*) and Local Moran’s I). The results show that their temporal and spatial patterns partially have limited alignment. To delve into the origins of fear, we extend our analysis by adopting BERTopic to identify topics and summarize them into themes (e.g., places, transportation, people, others) to understand the bottom-up emergence of fear, thereby informing a people-centered approach to research on urban issues. 

Keywords: Social media; Natural language processing; Sentiment analysis; Urban environment.

Methodology framework.

An example of textual analysis on fear-related tweets: from machine-generated topics to human-interpreted themes describing fear in NYC.

Weekly trends comparison between safety-related fear and violent crime.

Clustering features analysis by the method of hotspot analysis (Getis-Ord Gi∗).

Full Reference: 

Zhou, Y. and Crooks, A.T. (2026), Exploring Fear in Urban Environments: Place and Space Analysis of Social Media Data, Applied Geography, 192: 104051 (pdf)

New Paper: Connecting senses: The cross-modal associations between smell and vision in understanding urban environments

In a previous post we wrote about how one can mine social media to uncover smells and how they shapes peoples perceptions of urban spaces. Building off this work we (Qingqing Chen, Ate Poorthuis and myself) have a new paper entitled "Connecting senses: The cross-modal associations between smell and vision in understanding urban environments" published in Geographical Analysis. 

In this paper we build upon this but at the same time move away from social media to explore the relationship between smell and vision. We do this utilizing street view imagery from New York City, in this case we are using Mapillary. A subset of these images were labeled with pre-defined smell categories (e.g.,  ‘Nature’, ‘Food’, ‘Transportation & Fuel’) in order to develop a deep learning smell classifier capable of classifying perceived smells from the images. We then use  advanced image processing techniques (e.g., ResNet50, VGG16, Inception-V3, MobileNet and EfficientNet.) to extract visual cues from the street view imagery to predict smells. 

If this sounds of interest, below we provide an abstract to the paper along with some of the figures which show our workflow and accompanying results. At the bottom of the post you can see the full referece to the paper, while at https://figshare.com/s/94cdec3b14c206e6d225 we provide our code to allow others to replicate or extend this to other areas. 

Abstract:   

Smell is a crucial yet understudied sensory dimension in urban environments, bridging tangible elements (e.g., exhaust, flowers) with intangible impacts on emotions, social interactions and well-being. While geographical and urban research increasingly acknowledges multisensory experiences, much of geospatial analysis still emphasized the visual dimension. This research advances spatial thinking by examining cross-modal associations between smell and vision in urban environments. Specifically, we utilize advanced image processing techniques to extract visual cues from street view imagery (i.e., Mapillary) and apply causal analysis to examine their effects on smell expectations recorded from participants. The results show that visual cues can predict smells in straightforward urban settings (e.g., parks or less densely populated areas). However, in complex urban environments, the predictive power of visual cues diminishes as diverse and overlapping scents obscure specific smells, even in visually distinct areas. These findings underscore the importance of a multisensory approach in urban analytics, enhancing our understanding of the interplay between sensory experiences and informing urban design strategies that integrate multiple senses to create engaging and inclusive environments. This is especially important for individuals with sensory impairments, such as anosmia or visual impairments, who rely on other senses to compensate for their perception of urban environments. 

Keywords: Smell and Vision; Cross-modal Associations; Multisensory Experiences; Image Processing; Street View Imagery (SVI)

An overview of research workflow.

The visual feature extraction framework based on key patterns identified from questionnaires.

Spatial distribution of images with different perceived smells and example participant notes identifying potential smell sources responsible for perceiving the dominant smells.

Identified important features for smell categories that are relatively indirect to be inferred from visual cues. Left: Identified important features; Right: Examples of feature  contribution in individual images.

Full Referece: 

Chen, Q. Poorthuis, A. and Crooks A.T. (2026), Connecting senses: The cross-modal associations between smell and vision in understanding urban environments, Geographical Analysis, 58 (3): e70046.. Available at https://doi.org/10.1111/gean.70046. (pdf)

Research Updates: AAG 2026

At the AAG Annual meeting this year, two of my students gave talks about their ongoing research. Ying Zhou presented her work with a talk entitled "Exploring the Relationship between Urban Morphology and People’s Emotions." In this talk, Ying showed how one could mine social media posts to gain a sense of how different emotions are spatially spread around a city using New York city as a case study. If this sounds of interest, below you can see the abstract of the talk, the research methodology and a sample of the results.  

Abstract: 

Urban morphology records physical information about spatial patterns (e.g., streets and land use) and their evolution over time, as well as human settlement information. People who live in or visit a city gain experiences through interaction with its spatial patterns, and these experiences influence people’s emotions. Therefore, it is necessary to explore the spatial relationships between urban morphology and people’s emotions. Taking New York City as a case study, this research uses social media data to obtain and locate people's emotions in different parts of the city. To extract the emotion relating to specific space, we use the RoBERTa-based model to label texts in social media with six primary emotions (i.e., happiness, sadness, fear, anger, surprise, and disgust). We then used DBSCAN to identify spatial clustering features of these emotions. Finally, we compared the clustered emotions with urban morphology (both in terms of both its form and function) and how such emotions evolve and change over a span of five years. Such analysis reveals the relationship between people’s emotions and broader setting that they inhabit (i.e., the city). Moreover, these works offer bottom-up insights into how urban morphology shapes people’s feelings, which can serve as feedback for urban planning and management.

 

Keywords: Urban Morphology, Emotion Detection, Spatial Analysis, Urban Studies.

While in another talk, Boyu Wang continues to add new functionality to the Mesa, a python agent-based modeling toolkit, this time in the form of utilizing large language models for agent-based decision making, with a talk entitled "Mesa-LLM: Generative agent-based modeling with large language models empowered agents" 

If this sounds of interest, below you can see the abstract of the talk, along with the Mesa-LLM architecture. While further details about Mesa-LLM can be found on Boyu's GitHub page: https://github.com/mesa/mesa-llm.

Abstract 

Agent‐based models (ABMs) have long been used to examine how individual behaviors give rise to aggregated social and spatial phenomena. Mesa, an open source ABM library in Python, provides modular components and browser based visualization to create and analyze agent based models in the PyData ecosystem. Agents’ behaviours in these models are often governed by rule-based decisions. The recent advancements of large language models (LLMs) have created a new paradigm, namely generative agent-based modeling, where LLMs are integrated as decision-making engines so that agents can communicate, negotiate, and decide based on natural language. In this paper, we introduce Mesa-LLM, an LLM extension to the Mesa framework. Its modular design allows users to customize reasoning, memory and planning components and plug in different LLMs (e.g., GPT, Gemini, Llama). We demonstrate Mesa-LLM through Epstein’s civil violence model. In contrast to the classical model where agents act based on calculated probabilities and pre-defined thresholds, agents through Mesa-LLM have their decisions articulated in natural language. This demonstration shows how an archetypal ABM can be enriched by language-based decision making to explore complex social dynamics such as protest escalation. Through this simple example, we highlight how incorporating LLMs into ABMs opens new possibilities for geographers to model human behavior from the bottom up by leveraging generative artificial intelligence (GenAI).

 Keywords: Agent-Based Modeling, Large Language Model, AI Agent, Python.

References 

Wang, B., Frisch, C., Nair, S., Kazil, J. and Crooks, A.T. (2026), Mesa-LLM: Generative Agent-Based Modeling with Large Language Models Empowered Agents, The Association of American Geographers (AAG) Annual Meeting, 17th –21th March, San Francisco, CA. (pdf)

Zhou, Y. and Crooks, A.T. (2026), Exploring the Relationship between Urban Morphology and People’s Emotions, The Association of American Geographers (AAG) Annual Meeting, 17th –21th March, San Francisco, CA. (pdf)

PySGN: A Python package for constructing synthetic geospatial networks

In previous posts, we have written about the generation of synthetic populations based on real world locations, and how such populations can have various types of networks associated with them. We have also written about network generation techniques in the past and keeping with this line of research, Boyu Wang, Taylor Anderson, Andreas Züfle and myself have a new paper in the Journal of Open Source Software entitled "PySGN: A Python Package for Constructing Synthetic Geospatial Networks"

In this paper we introduce a Python package that can generate geospatial networks which we have called PySGN (Python for Synthetic Geospatial Networks). For readers not familiar with geospatial networks, to quote from the online documentation we have put together:  

Geospatial networks are a type of network where nodes are associated with specific geographic locations. These networks are used to model and analyze spatial relationships and interactions, such as transportation systems, communication networks, and social interactions within geographic constraints. By incorporating spatial data, geospatial networks provide insights into how location influences connectivity and network dynamics.

PySGN generates synthetic geosocial networks that mimic the spatial relationships observed in real‑world networks as it embeds nodes in geographic coordinate space, modifies connection rules to decay with distance, and allows users to incorporate clustering and preferential attachment while respecting spatial constraints. Online we provide examples of creating Geospatial Erdős-Rényi, Watts-Strogatz & Barabási-Albert Networks along with ways to sample points based on a specified bounding box or specific polygon boundaries (examples of which are shown below). 

The package is intended for researchers and practitioners in fields such as urban planning, epidemiology, infrastructure resilience and social science who require robust tools for simulating and analyzing complex geospatial networks. In addition to the paper, we have also made available extensive documentation (along with examples of the various network types) at https://pysgn.readthedocs.io/en/ 

Examples of Geospatial Erdős-Rényi and Watts-Strogatz Networks.

Example of Geospatial Barabási-Albert Network based on different ordering strategies for how nodes are added to the network.

Examples of sampling points based on a bounding box or a specific set of polygons

Full Referece: 

Wang, B., Crooks, A.T., Anderson, T., and Züfle, A. (2026), PySGN: A Python Package for Constructing Synthetic Geospatial Networks. Journal of Open Source Software, 11(119), 9346, https://doi.org/10.21105/joss.09346 (pdf)

A hybrid simulation methodology for identifying and mitigating supply chain disruptions

Durring times of crisis, shocks to supply chains can propagate through the entire economy (e.g., global shortages of critical goods, such as personal protective equipment during COVID-19). At the same time, criminal organizations may disrupt and manipulate licit supply chains for financial gain or political objectives.  Thus there is a strong need for modeling and simulating not only supply chain operations but also malicious actors who may act to disrupt them. 

To explore this we (Abhisekh Rana, Raj Patel, Aman Goswami, Sean Luke, Alok Baveja, Carlotta Domeniconi, Benjamin Melamed, Fred Roberts, Weiwei Chen, Vladimir Menkov, Viswanath Narayan, James Jones, Hamdi Kavak and myself) have a new paper entitled  "Hybrid simulation methodology for identifying and mitigating supply chain disruptions" which was recently published in the Journal of Simulation. 

In the paper we introduce a novel hybrid modeling framework (implemented in MASON) designed to identify vulnerabilities across supply networks. Through the framework, we are able to analyze disruption scenarios  and evaluate mitigation strategies using a pharmaceutical supply chain model (i.e., PharmaSim). As such this paper and proposed framework provides a foundation for simulation-driven planning tools that help organizations anticipate risks and strengthen supply chain resilience.

If this sounds of interest, below we provide the abstract to the paper, some of the figures which show the supply chain we model and the simulation framework along with some results. While at the bottom of the page, you can find the full referece to the paper and a link to it, while the model itself is available at https://github.com/eclab/DES-Supply-Chain-demo. 

Abstract

Global disruptions have shown that shocks to supply chains can quickly ripple through entire economies, highlighting the need to identify vulnerabilities and evaluate mitigation strategies to build resilience. In this paper, we propose a simulation methodology, Hybrid Integrated Supply-Chain Simulation (HISS), to identify and mitigate potential disruptions in supply chains. We demonstrate HISS using a generic pharmaceutical supply chain model including sourcing, outsourcing, production, packaging, and distribution processes, created using MASON’s hybrid modeling capabilities. We classify disruptions from malicious actors and analyze their timing, impact, and scope. The simulation is further extended to modeling mitigation strategies and assessing their efficacy. Extensive optimization allowed us to identify worst-case disruptions and optimized safety stock strategies reduced impacts by a factor of five, while anomaly detection achieved a high recall of 0.966. The modeling approach proposed in this paper provides a basis for planning tools that support resilience and preparedness of supply chains.

Keywords: Hybrid simulation, supply chains modeling, resilience, optimization, evolutionary computation. 

Visual representation of pharmaceutical supply chain (PSC), which was used to code PharmaSim

Time series of daily production flow through the active pharmaceutical ingredient (API) Production node (resilience triangles are shown in red and the number of units on the vertical axis is in millions).

Overview of the software components and their interactions.

Sample time series of numbers of packaged units with anomalies due to (left) a disruption and due to (right) normal fluctuations (the number of units on the vertical axis is in millions).

Full reference:

Rana, A., Patel, R., Goswami, A., Luke, S., Baveja, A., Domeniconi, C., Melamed, B., Roberts, F., Chen, W., Crooks, A.T., Menkov, V., Narayan, V., Jones, J. and Kavak, H. (2026). A hybrid simulation methodology for identifying and mitigating supply chain disruptions. Journal of Simulation, 1–22. https://doi.org/10.1080/17477778.2026.2628944 (pdf)

Driving Anxiety and Visual Attention in Young Drivers

Over the last summer I participated in a research experience for undergraduate at the University at Buffalo (UB) hosted by the Geologic and Climate Hazards Center. In this program students spent several weeks at UB working with faculty on a diverse set of projects ranging from understanding snow events over the great lakes, forest die off to utilizing crowdsourced data to study dust events. One of the projects I was involved with resulted in a poster being presented at the 105th Transportation Research Board (TRB) Annual Meeting entitled "Driving Anxiety and Visual Attention in Young Drivers: A Driving Simulator Study". 

In this study Phoebe Schrag worked alongside Austin Angulo, Irina Benedykc, Gongda Yu, Daisha Cardenas, Hayden Radel (from UB's Transportation Research and Visualization Laboratory (TRAVL)) and myself to explore driving anxiety of young drivers between the ages of 18 and 25. Using eye-tracking data from a high-fidelity virtual reality (VR) driving simulator we explored the effects of self-reported driving anxiety on visual attention, decision-making, and cognitive load. We found that driving anxiety can impair situational awareness. If this sounds of interest and you want to find out more, below you can read the abstract to the poster along with a link to the actual poster itself. 

Abstract: 

Motor vehicle crashes have remained the second leading cause of death among adolescents and young adults in the United States. Although high crash rates are commonly attributed to inexperience, risk-taking behavior, and underdeveloped executive functions, the role of emotional factors such as driving anxiety remain under-explored. Driving anxiety, which is characterized by persistent fear or worry while driving, may have a significant impact on young or novice drivers due to their limited experience and developing emotional regulation abilities. However, existing research has relied heavily on adult samples, self-report measures, or clinical cases, rarely incorporating real-time behavioral data from young adults. This study addresses these gaps by using eye-tracking in a high-fidelity virtual reality (VR) driving simulator to objectively evaluate the effects of self-reported driving anxiety on visual attention, decision-making, and cognitive load. Thirty-one licensed drivers aged 18–25 were classified into anxiety and non-anxiety groups using a questionnaire with reference to the Driving Cognitions Questionnaire. Participants completed five mixed urban-rural scenarios (two dynamic, two static, and one repeated dynamic) while wearing a Varjo XR3 headset with iMotions eye-tracking monitoring. Key eye-tracking metrics (e.g., dwell time proportion, fixation duration and saccade count) were analyzed using scenario-specific Welch’s t-tests (α = 0.05). The results showed that anxious drivers had significantly fewer saccadic movements in high-demand scenarios, indicating reduced scanning and increased cognitive load. These findings demonstrate how driving anxiety can impair situational awareness and suggest that targeted psychological interventions could improve attentional control. This work informs emotionally adaptive driver training for young drivers.

KEYWORDS: Driving Anxiety, Eye-Tracking, Visual Attention, Young Drivers, Cognitive Load, VR Driving Simulation.

Full Reference:

Schrag, P., Yu, G., Cardenas, D., Radel, H., Angulo, A., Crooks, A.T. and Benedyk, I. (2026), Driving Anxiety and Visual Attention in Young Drivers: A Driving Simulator Study, 105th Transportation Research Board (TRB) Annual Meeting, 11th – 15th January, Washington DC. (poster pdf)