# Webinar: AI Agent Simulation of Human Behavior with Michael Bernstein **URL:** \ **Category:** Content Creators\ **Tags:** stanford-online, agents, machine-learning, behavioural-science\ **Created:** [29 September 2026 22:07 UTC](https://www.artofsm.art/t/webinar-ai-agent-simulation-of-human-behavior-with-michael-bernstein/24615 "2026-09-29T22:07:44Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![artesia](https://www.artofsm.art/user_avatar/www.artofsm.art/artesia/32/36_2.png) [@artesia](https://www.artofsm.art/u/artesia)\ **Post date:** [29 September 2026 22:07 UTC](https://www.artofsm.art/t/webinar-ai-agent-simulation-of-human-behavior-with-michael-bernstein/24615/1 "2026-09-29T22:07:44Z")
[![](https://www.artofsm.art/uploads/default/original/3X/9/4/943bc269f284ef5cc0b3416a6ca231d2bb8f76b5.jpeg "Webinar: AI Agent Simulation of Human Behavior with Michael Bernstein") ](https://www.youtube.com/watch?v=6EIkeKruJaI) Professor Michael Bernstein presented how AI-driven generative agents, powered by large language models, can simulate realistic human behaviors and social interactions to improve organizational decision-making and reduce biases compared to traditional models. He emphasized the importance of careful validation, ethical considerations, and practical applications such as training and platform design, highlighting AI’s transformative potential in understanding and predicting human behavior. ---
**Author:** ![artesia](https://www.artofsm.art/user_avatar/www.artofsm.art/artesia/32/36_2.png) [@artesia](https://www.artofsm.art/u/artesia)\ **Post date:** [29 September 2026 22:31 UTC](https://www.artofsm.art/t/webinar-ai-agent-simulation-of-human-behavior-with-michael-bernstein/24615/3 "2026-09-29T22:31:33Z")
Professor Michael Bernstein from Stanford University presented a webinar on AI agent simulation of human behavior, exploring how AI can be used to create realistic simulations of people to improve decision-making in organizations. He began by discussing the challenges leaders face when making decisions based on incomplete information about human reactions, highlighting the potential of AI-driven “what-if machines” to simulate possible outcomes before launching policies, products, or strategies. Bernstein traced the history of simulation models, noting their limitations due to oversimplification or rigid scripting, and introduced the recent advances enabled by large language models (LLMs) like ChatGPT, which can simulate diverse human behaviors more richly. Bernstein described his team’s creation of “generative agents,” AI-driven simulated people with distinct personas, memories, and goals, demonstrated in a virtual town called Smallville. These agents autonomously interact, remember events, reflect on experiences, and plan their daily activities, resulting in believable social dynamics such as information diffusion about a Valentine’s Day party. The simulation allows for interventions and real-time interactions, providing a powerful tool to explore how people might respond to changes in their environment or social context. This approach moves beyond traditional agent-based models by leveraging LLMs’ ability to process complex, nuanced human behavior. To evaluate the accuracy of these AI agents, Bernstein’s team conducted extensive research involving 1,000 real individuals and their digital twins created from detailed interviews. By comparing the agents’ responses to surveys and behavioral experiments with those of the actual people, they found that agents built from rich qualitative data could replicate human attitudes and behaviors with high fidelity—up to 85% as accurately as people replicate themselves over time. This method also reduces stereotyping and bias compared to simpler demographic-based models. However, Bernstein cautioned that some groups, such as far-right conservatives, are harder to model accurately due to inherent biases in the underlying AI models. Bernstein emphasized the importance of understanding the limitations and risks of AI simulations. While qualitative and exploratory uses of these agents are promising and relatively safe, quantitative predictions and large-scale multi-agent simulations require careful validation and should be supplemented with real-world testing. He advised organizations to use simulations to narrow down options and identify potential risks before conducting actual experiments or deployments. Mitigating risks involves ensuring agents have relevant, domain-specific data and validating key outcomes on smaller samples to avoid misleading conclusions. Finally, Bernstein highlighted emerging applications and future directions for AI agent simulations, including their use in training soft skills like conflict negotiation and improving online platform design by anticipating problematic behaviors before launch. He also discussed the broader implications of AI agents in customer service and the creation of entirely artificial characters, noting ethical considerations and the need for specialized AI models fine-tuned to replicate human behavior accurately. The webinar concluded with a Q&A session addressing practical questions about AI’s capabilities, biases, and how these simulations compare to traditional scenario analysis, underscoring the transformative potential of AI in understanding and predicting human behavior. ## Useful Links - [Stanford University Human-Computer Interaction Research](https://profiles.stanford.edu/michael-bernstein) — High - [Generative Agents Project by Michael Bernstein et al.](https://arxiv.org/abs/2304.03442) — High - [Large Language Models (LLMs) such as ChatGPT, Claude, LLaMA](https://business.adobe.com/blog/llm-comparison) — High - [Agent-Based Modeling and Simulation - Thomas Schelling’s Work](https://nifty.stanford.edu/2014/mccown-schelling-model-segregation/) — High - [American Voices Project by David Grusky](https://september.stanford.edu/soco-2026/american-voices-project) — High