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)