A Reminder – computability limits on “vibe coding” ABMs (using LLMS to do the programming for us) By Bruce Edmonds Introduction Machine-learning systems, including Large Language Models (LLMs), are algorithms trained on large datasets rather than something categorically different. Consequently,…

New Contribution: A Reminder – computability limits on “vibe coding” ABMs by Bruce Edmonds. rofasss.org/2026/02/12/v... #rofasss #ABM #agentbased #programming #LLMs #AI #vibecoding #computability

Why Object-Oriented Programming is not the best method to implement Agent-Based Models By Martin Hinsch Research Department of Genetics, Evolution and EnvironmentUniversity College London Introduction A considerable part of the history of software engineering consists of attempts to …

New Contribution: "Why Object-Oriented Programming is not the best method to implement Agent-Based models" by Martin Hinsch. rofasss.org/2026/02/03/oop #oop #abm #objectoriented #agent-based #agentbased #rofasss #simulation #modelling

Modelling Deep Structural Change in Agent-Based Social Simulation By Thorid Wagenblast1, Nicholas Roxburgh2 and Alessandro Taberna3 1 Delft University of Technology, 0009-0003-5324-37782 The James Hutton Institute, 0000-0002-7821-18313 CMCC Foundation - Euro-Mediterranean Center on Climate Change, RFF-CMCC European Institute on Economics and the Environment, 0000-0002-0207-4148 Introduction Most agent-based models (ABMs) are designed around the assumption of a broadly stable system architecture. Whether exploring emergent dynamics or testing the effects of external interventions or stressors, such models typically operate with a fixed ontology – predefined agent types, attribute classes, behavioural repertoires, processes, and social and institutional structures.

Post by Thorid Wagenblast, Nick Roxburgh and Alessandro Taberna on "Modelling Deep Structural Change in ABSS" #abm #socialsimulation #rofasss

Make some noise! Why agent-based modelers should embrace the power of randomness By Peter Steiglechner1, Marijn Keijzer2 1 Complexity Science Hub, Austria; steiglechner@csh.ac.at2 Institute for Advanced Study in Toulouse, France Abstract ‘Noisy’ behavior, belief updating, or decision-making is universally observed, yet typically treated superficially or not even accounted for at all by social simulation modelers. Here, we show how noise can affect model dynamics and outcomes, argue why injecting noise should become a central part of model analyses, and how it can help our understanding of (our mathematical models for) social behavior.

"Make some noise! Why agent-based modelers should embrace the power of randomness" by Peter Steiglechner and Marijn Keijzer on why testing your model with randomness is vital. #RofASSS #ABM #noise #randomness #robustness #socialsimulation #taxonomy #simulation #science #method #agent-based