16/25 ๐๐ป ๐๐ป๐ณ๐ฒ๐ฐ๐๐ถ๐ผ๐๐ ๐๐ถ๐๐ฒ๐ฎ๐๐ฒ ๐ฆ๐ฝ๐ฟ๐ฒ๐ฎ๐ฑ ๐ฆ๐ถ๐บ๐๐น๐ฎ๐๐ถ๐ผ๐ป ๐๐ฎ๐๐ฒ๐ฑ ๐ผ๐ป ๐๐ฎ๐ฟ๐ด๐ฒ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น ๐๐ฒ๐ฐ๐ถ๐๐ถ๐ผ๐ป ๐ ๐ฎ๐ธ๐ถ๐ป๐ด
This paper proposes a spatially grounded, agent-based simulation framework that integrates LLM-generated decisions about self-reported influenza-like illness into a census-based synthetic population. By assigning agents to spatial units in cities using real-world census data, it models geographically diverse behaviour. Simulations in San Francisco and Atlanta reveal income and education as dominant drivers of reporting rate variation, with smaller effects from geography, LLM choice, and message framing, supporting spatial epidemiological modelling and bias-aware behavioural analysis.
#LLMApplications#AgentBasedModeling#Epidemiology#PublicHealth#BehavioralModeling#SpatialModeling
Paper Link: arxiv.org/abs/2606.06360
#JASSS Vol.29 Issue 2 is out!
New research explores online polarization, authoritarian repression, solar PV adoption & financial sentiment spread through the lens of #AgentBasedModeling.
Find out what #ComputationalSocialScience reveals about society ๐ jasss.org
Simulating Society with NetLogo!
We had an engaging hands-on session at the IndabaX Spring School with Professor Frank Dignum and Professor Virginia Dignum.
Participants explored NetLogo and learned how simple rules can be used to simulate complex social behaviors.
The Segregation Model showed how small individual choices can lead to large societal patterns over time, with real-time visualizations bringing these dynamics to life.
It was a powerful reminder of how agent-based modeling can help us better understand and design real-world systems.
@DeepIndaba@AI_HealthLabMak@UNUniversity@ZindiAfrica@MasakhaneNLP@AIR_lab_MUK@Google@TheOfficialACM#IndabaXUganda2026#AgentBasedModeling#NetLogo#SpringSchool#SocialSimulation
๐ฏ What if you could predict the battlefield?
Dive into the cutting-edge world of AI-driven combat simulationsโwhere every decision, every variable, and every strategy is modeled with precision.
๐ง Why it matters:
In a world of evolving threats, intelligent simulation isnโt just theoryโitโs mission-critical. This book bridges military science with AI, machine learning, and optimization techniques to shape the future of modern warfare.
๐ Inside you'll uncover:
โ๏ธ Agent-Based & Hybrid Models
๐ Adaptive Strategy Optimization
๐งฉ Ontology-Driven Design
๐ Real-time Scenario Simulation
๐ Military Use Cases & Future Forecasting
๐ก Perfect for:
โข Military tech professionals
โข Simulation engineers
โข Defense researchers & strategists
โข AI, ML, and cognitive modeling experts
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๐ Code: WSTWTR30
๐ Join the Conversation:
@DefenseTechTalks @MilAI_Research @SimulateWarfare @CombatModeling @AgentBasedAI @OntoDefence @HybridSimLab @DefenseFutures @ML_in_Defense @ThinkTankSim
@CognitiveBattle @AI4Warfare @NextGenDefense @TacticalSims @WarGameTech @SimulationNow @MilitaryAIHub @ModelingScience @AISimStrategies @SmartCombatTech
#CombatSimulation#DefenseAI#WarfareTech#AgentBasedModeling#CognitiveModeling#AIinDefense#HybridSim#MilitaryInnovation#DefenseR&D #Wargaming#OperationalResearch
The Null World: Episode 5 of The Fractals of Finance
We built a market with no feedback. Every statistical signature vanished.
Then we turned feedback on. Every signature returned.
Over four episodes, we documented the fingerprint of real markets: deep memory, fat tails, volatility clustering, universality across 68 contracts. But observation is not proof. So we built a controlled experiment.
An agent-based model. One variable: feedback. Everything else held constant.
The null world, pure Gaussian noise with no agents, produced exactly what traditional finance assumed. Zero memory. Zero fat tails. Zero five-sigma events. A perfect random walk.
The moment we introduced traders who condition their behaviour on price, the flat line erupted into structure. Memory appeared. Tails fattened. Extreme events multiplied. The simulated world became statistically indistinguishable from real markets.
No external shocks required. No news. No central banks. No earnings announcements. Just participants observing price and reacting to it.
Feedback alone is sufficient to reproduce the statistical DNA of real markets. Its absence produces a world that does not exist.
Episode 6 asks: how much feedback does it take to break the random walk?
Less than you think.
Read the full episode here: atstradingsolutions.com/the-โฆ#QuantitativeFinance#AgentBasedModeling#MarketMicrostructure#FractalsOfFinance
Meet our speaker Georgina Curto, PhD, MBA
Georgina will lead multiple sessions on Agent-Based Modelling and Decision Intelligence, sharing practical insights on how agent-based systems can support policy making and complex decision processes.
She is a Senior AI Researcher and Team Lead at the United Nations University Institute in Macau @UNUMACAU, where her work focuses on applying AI to real-world societal challenges.
Her sessions will blend strong foundations with hands-on practice, helping participants understand how agent-based and agentic AI methods inform better decisions.
We are excited to have her at IndabaX & Spring School 2026.
@DeepIndaba@SunbirdAI@AI_HealthLabMak@UNUMACAU@ZindiAfrica@TheAIJournal1@MasakhaneNLP@AIR_lab_MUK@DeepIndabaX_ZA@GoogleDeepMind@TheOfficialACM#IndabaXUganda#SpringSchool2026#AIinAfrica#AgentBasedModeling