AINeutralarXiv – CS AI · Jun 236/10
🧠Researchers decompose financial market dynamics by testing four pluggable mechanisms in an evolutionary agent-based model with 120 heterogeneous agents, finding that selection operators control diversity, price microstructure drives realism, and behavioral bias amplifies fragility—but these levers operate largely independently, offering a framework for understanding which market design choices produce which emergent properties.
AINeutralarXiv – CS AI · Jun 236/10
🧠Researchers investigated whether large language models can generate synthetic survey responses that mimic real population data on health behaviors and vaccination attitudes. While LLMs successfully reproduced demographic distributions and broad vaccination trends across epidemic waves, they failed to capture correlations between factors within individual respondents and remained identifiable as synthetic, suggesting LLM-generated data could support exploratory modeling but requires further validation before replacing human surveys.
AINeutralarXiv – CS AI · Jun 95/10
🧠Researchers tested whether large language models assigned distinct personas could simulate a live concert audience experience through real-time chat during K-pop video playback. While persona-conditioned LLM agents produced more natural and higher-quality chat messages than baseline models, the study found no measurable improvement in user engagement, social connectedness, or emotional response, suggesting that algorithmic personas alone cannot replicate the cultural and social depth of authentic fandom experiences.
AINeutralarXiv – CS AI · Jun 56/10
🧠Researchers present RAINO, a systematic framework addressing how realism is poorly defined and inconsistently operationalized in Agent-Based Models. The framework identifies Reality Anchors (empirical data, theory, expert knowledge) and their application as inputs or outputs, providing a conceptual foundation for evaluating and developing more realistic computational models.
AINeutralarXiv – CS AI · Jun 56/10
🧠Researchers have developed a large-scale benchmark dataset for evaluating causal inference methods in epidemic time-series prediction under dynamic interventions. Using calibrated agent-based models grounded in real-world U.S. county data, the benchmark enables testing of causal inference techniques across static and time-varying treatment scenarios with verifiable counterfactual outcomes.
AINeutralarXiv – CS AI · Jun 45/10
🧠Researchers developed Neetyabhas, an agent-based simulation framework that models pandemic policy decisions under real-world uncertainty, incorporating individual behavioral choices and imperfect data. Using reinforcement learning, the model demonstrates that masks and vaccines effectively reduce outbreak severity when policies account for implementation errors and measurement gaps.
AINeutralarXiv – CS AI · May 296/10
🧠Researchers introduce AgentSchool, an LLM-powered multi-agent simulator that models student learning through state transitions rather than simple role-play, featuring cognitively growable student agents with knowledge graphs and adaptive teachers operating within the Zone of Proximal Development. The system addresses the challenge of validating educational AI interventions in real classrooms by creating a configurable simulation environment that reproduces plausible learning outcomes and social dynamics without requiring institutional constraints or ethical compromises of live trials.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce TruthMarketTwin, a simulation framework that models LLM agent behavior in e-commerce markets with asymmetric information. The study reveals that autonomous LLM agents strategically exploit reputation-based governance weaknesses, but warrant enforcement mechanisms significantly reduce deceptive practices.
AINeutralarXiv – CS AI · May 16/10
🧠Researchers evaluated 17 large language models on their ability to implement agent-based models from standardized specifications, finding that while GPT-4.1 and Claude 3.7 Sonnet produce statistically valid implementations, executability alone doesn't guarantee scientific reliability. The study reveals both significant promise and critical limitations in using LLMs as automated tools for scientific model engineering and replication.
🧠 GPT-4🧠 Claude
AINeutralarXiv – CS AI · Apr 146/10
🧠Researchers introduce SLALOM, a validation framework addressing the credibility crisis of LLM-based social simulations by shifting focus from outcome accuracy to process fidelity. The framework uses Dynamic Time Warping to compare simulated trajectories against empirical data across intermediate checkpoints, enabling quantitative assessment of whether simulations achieve realistic social mechanisms rather than merely correct endpoints.
AINeutralarXiv – CS AI · Apr 136/10
🧠Researchers introduce AgentSociety, a large-scale simulator using LLM-driven agents to study human behavior and social dynamics. The system simulates over 10,000 agents and 5 million interactions to model real-world social phenomena including polarization, policy impacts, and urban sustainability, demonstrating alignment with actual experimental results.
AINeutralarXiv – CS AI · Apr 74/10
🧠Researchers have developed discourse_simulator, an open-source Python framework that combines large language models with agent-based modeling to simulate how public attitudes change over time in response to real-world events. The framework models social media interactions and opinion dynamics through AI agents in social networks, offering a new tool for social science research on attitude polarization and belief evolution.
AINeutralarXiv – CS AI · Mar 54/10
🧠Researchers developed a multi-agent simulation framework using reinforcement learning to model archaeological mobility patterns in complex terrain. The system combines global path planning with local adaptation to simulate human and animal movement in historical landscapes, demonstrated through pursuit scenarios and transport analysis.
AINeutralarXiv – CS AI · Mar 25/107
🧠A research position paper examines the integration of Large Language Models (LLMs) in agent-based social simulations, highlighting both opportunities and limitations. The study proposes Hybrid Constitutional Architectures that combine classical agent-based models with small language models and LLMs to balance expressive flexibility with analytical transparency.
