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Harvard and MIT researchers built 8.3 billion AI personas to simulate the world's population, achieving high trait adherence, with potential applications in campaign design and product validation.
This paper proposes a method for simulating large LLM-agent societies on a laptop by fitting low-parameter surrogate models from a few hundred queries, using a statistical-physics-based taxonomy to predict when this approximation holds. The approach is validated on EconAgent and several other simulations using DeepSeek-elicited agent behaviors.
This paper introduces MatrAIx, a population-scale simulated-user evaluation infrastructure using 8.3 billion persona records to test AI systems and digital products. It provides a quality-filtered coreset of ~1 million personas, multiple evaluation environments, and validation studies showing high persona adherence.
This paper proposes a systems blueprint and six-level capability ladder for building Economic World Models (EWMs), where heterogeneous agents interact with markets and institutions to generate emergent economic dynamics. It surveys existing work and emphasizes gaps in self-evolving agents, endogenous institutions, and empirical alignment.
Presents Eco3S, a socio-economic system simulation framework that uses LLM-based agents with co-evolving environments, structural causal simulation, and a self-corrective refinement paradigm to replicate and analyze economic phenomena.
This paper introduces HALE, a scalable hybrid agent-based and language-driven epidemic modeling framework that leverages LLMs to predict human decision-making in ABM simulations, demonstrating improved accuracy in modeling COVID-19 spread in Salt Lake County.
AgoraSim is a hybrid agent-based modeling framework that combines LLM agents with classical ABM for social reaction analysis. It supports multimodal inputs and structured decision outputs for scenario-oriented simulation.
This paper presents an extended evacuation framework integrating cognitive, emotional, social, and personality mechanisms for agent-based simulations of human behavior under uncertainty. It models dynamic event awareness, memory, fear, and OCEAN-based personality, demonstrating impacts on evacuation efficiency and realistic crowd phenomena.
Built an MCP server allowing Claude to control NetLogo for agent-based modeling, including headless sweeps and model loading from CoMSES Net.
This paper introduces HELM, a human-agent framework that automates finite element modeling of concrete bridge barriers, increasing success rates from 20% to 75% using commercial FE software.
Neetyabhas is a framework for uncertainty-aware public policy optimization using hierarchical reinforcement learning agents in agent-based epidemic simulations. It models individual behaviors (mask-wearing, vaccination, shopping) and policymaker interventions under uncertainty, demonstrating effective COVID-19 outbreak management.
The paper presents a hybrid Discrete Event Simulation and Agent-Based Model framework for emergency departments, validated against real-world data, and integrates a multi-agent system for autonomous resource allocation optimization.