16 articles tagged with #behavioral-modeling. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce MobilityGen, a diffusion-based generative model that simulates detailed human mobility patterns across days to weeks at large spatial scales. The framework reproduces real-world mobility behaviors including location visit scaling laws, activity time allocation, and travel mode choices, enabling new analyses of urban accessibility and social segregation dynamics.
Researchers introduce MASS (Memory-Augmented Social Simulation), a framework that enhances LLM-based research agents by integrating realistic social simulations rather than relying solely on literature retrieval. The system combines dynamic goal-path planning, multi-disciplinary behavior datasets, and an Ebbinghaus-inspired forgetting mechanism to improve research creativity and empirical grounding, achieving 6.81% quality improvement and 17.19% insight gains over baseline LLMs.
Researchers developed an agent-based simulation framework using large language models to model individual decision-making during infectious disease outbreaks, integrating LLM-generated behavioral choices into spatially-grounded synthetic populations across real cities. The study found that income and education are the primary factors determining disease reporting rates, with geography and message framing playing secondary roles in shaping public health responses.
Researchers demonstrate that large language models can effectively create detailed digital twins of individual consumers using existing socio-economic panel data, achieving 78.8% accuracy on held-out questions. The study maps construction decisions across model types, information depths, and embedding methods, showing that market research scalability is now limited by data volume and model selection rather than data collection design.
Researchers introduce S-MARC, a streaming framework for modeling conversational behavior in full-duplex dialogue systems that predicts communicative functions and interaction behaviors while capturing their causal relationships. The system generates interpretable reasoning chains and establishes benchmarks for conversational AI reasoning, advancing natural human-computer interaction capabilities.
Researchers developed SMamba-DDPG, a deep reinforcement learning framework that models how pedestrians behave differently when interacting with autonomous vehicles versus human-driven vehicles. The study found that pedestrians react faster to AVs and adopt lower crossing speeds, with AV interactions showing lower conflict rates than HDV scenarios.
Researchers systematically analyze the design space of LLM-based social simulations, examining how different architectural choices—particularly base model selection and network topology—affect simulated agent behavior and opinion formation. The study reveals non-trivial interactions between parameters and identifies the choice of underlying LLM as the most critical factor determining simulation outcomes.
A comprehensive survey examines AI methodologies for simulating mixed autonomous and human-driven traffic, addressing critical gaps in current simulation tools. The research proposes a unified taxonomy of AI methods spanning agent-level behavior models, environment-level simulations, and physics-informed approaches to improve autonomous vehicle testing and validation.
Researchers propose a method for training open-source language models to simulate how programming students learn and debug code, using authentic student data serialized into conversational formats. This approach addresses privacy and cost concerns with proprietary models while demonstrating improved performance in replicating student problem-solving behavior compared to existing baselines.
Researchers introduce BehaviorLM, a progressive fine-tuning approach that enables large language models to predict both common and rare user behaviors more effectively. The method uses a two-stage process that balances learning frequent anchor behaviors with improving predictions for uncommon tail behaviors, demonstrating improved performance on real-world datasets.
Research study reveals that Large Language Models can reproduce behavioral patterns but fail to accurately predict intervention effects. The study tested three LLMs on climate psychology interventions across 59,508 participants from 62 countries, finding that descriptive accuracy doesn't translate to causal prediction accuracy.
Researchers argue that LLM-based AI agents are not yet effective for social simulation, despite growing optimism in the field. The paper identifies systematic mismatches between what current agent systems produce and what scientific simulation requires, calling for more rigorous validation frameworks.
Researchers introduced Conditioned Comment Prediction (CCP) to evaluate how well Large Language Models can simulate social media user behavior by predicting user comments. The study found that supervised fine-tuning improves text structure but degrades semantic accuracy, and that behavioral histories are more effective than descriptive personas for user simulation.
Researchers developed CMA-ES-IG, a new algorithm that helps robots learn user preferences more effectively by incorporating user experience considerations. The algorithm suggests perceptually distinct and informative robot behaviors for users to rank, showing improved scalability, computational efficiency, and user satisfaction compared to existing methods.
Researchers present a framework for social planners to strategically reveal positive and negative role models to influence agent behavior in social networks. The study addresses optimization challenges when disclosure budgets are limited and proposes algorithms to maximize social welfare while maintaining fairness across different groups.
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.
