Stanford Global Alumni Webinar | August 2025 | AI Agent Simulation of Human Behavior

The video explores the development and potential of AI agents capable of simulating human behavior, addressing the long-standing challenge of making decisions with incomplete information. Traditional simulation methods, like agent-based models, have been too simplistic or labor-intensive to accurately capture human complexity. However, recent advancements in large language models have opened new avenues, allowing for the creation of more believable and potentially accurate simulations by prompting these models to adopt specific personas and interact within simulated environments.

These advanced AI agents, termed "generative agents," are built upon a foundation of memory, reflection, and planning. Their memory stream records observations, augmented by retrieval mechanisms to prioritize relevant information. Reflection allows them to form higher-level understandings of themselves and their goals, while planning enables them to navigate their environment and adapt to unforeseen events. This approach allows for the simulation of complex social dynamics, such as information diffusion and event planning, offering a "what-if machine" for exploring potential outcomes before real-world implementation.

The accuracy of these simulations is a critical area of research. By comparing agent behavior to actual human responses gathered through extensive interviews and surveys, a significant replication rate has been demonstrated, particularly when agents are equipped with rich qualitative data. While challenges remain, especially with quantitative predictions and multi-agent simulations, the potential applications in areas like market research, policy analysis, and soft skills training are vast, promising more informed and proactive decision-making.

• The inherent difficulty in making decisions due to incomplete information has historically led to poor outcomes.
• Advancements in AI, specifically large language models, enable the creation of "generative agents" that can simulate human behavior with greater believability and accuracy.
• These agents possess memory, reflection, and planning capabilities, allowing them to navigate complex simulated environments and interact realistically.
• Research demonstrates that rich qualitative data, derived from extensive interviews, significantly improves the accuracy of these AI agent simulations.
• The technology offers powerful applications for "look before you launch" strategies, soft skills training, and market research, though careful consideration of risks is necessary.

The Challenge of Decision Making with Incomplete Information [00:36]

• Decisions are often based on incomplete information about how people will react.
• This leads to making "bad bets" or ultimately being wrong due to limited feedback and the inability to know future outcomes in advance.
• This problem spans various sectors including consumer packaged goods, personal finance, product design, policy management, and even academia.
• The difficulty of predicting group behavior dates back to at least 1906, as noted by sociologist Robert Merton, illustrated by the example of everyone trying to escape city crowds only to find vacation spots also overcrowded.

We make these decisions based on the best data we can. But that data is often incomplete.

This section highlights the fundamental human challenge of predicting behavior and its consequences, emphasizing that such difficulties are not new but a persistent issue across many domains.

The Concept of a "What If" Machine [02:40]

• The speaker poses the question: "What if you had a what-if machine?"
• This machine would allow one to imagine in advance what might happen, such as how customers might react to an organizational path, the pathway of failure, or how people might respond to policy changes or new products before deployment.
• The claim is that having such a machine would lead to making better decisions more often.
• This has motivated research into creating simulated AI agents that replicate human behavior.

What if machine? What if you had a whatif machine? What would you use that for?

This section introduces the core idea of a predictive simulation tool that can foresee potential outcomes, thereby improving decision-making processes.

The Evolution of Simulation and Generative Agents [04:01]

• Simulation itself is not a new concept, with roots in agent-based models from 1978, used in fields like pandemic spread analysis.
• Entertainment also utilizes simulations, as seen in games like "The Sims."
• The increasing deployment of AIs interacting with humans necessitates reasoning about human responses, leading to the idea of "AI teammates."

• This creates an opportunity for "look before you launch" tools to help foresee potential issues before deployment.

• However, previous models have been rigid, either oversimplified (e.g., using only a few parameters) or requiring extensive manual scripting, leading to limited impact and minimal accuracy in academic literature.

The basic conclusion is the models have been highly stylized and have had minimal impact.

The evolution from traditional, often rigid, simulation models to the potential of AI-driven simulations is discussed, setting the stage for the emergence of generative agents.

Generative Agents: Simulating Human Behavior with LLMs [07:04]

• Modern AI, specifically large language models (LLMs) like ChatGPT, have been trained on vast amounts of human behavior data, including research and social media.
• This training allows LLMs to be prompted to adopt the perspectives of diverse individuals with different backgrounds, experiences, and traits.
• By prompting an LLM to "be this person" with a description and situation, and multiplexing these descriptions, an entire crowd of different simulated people can be created.
• These simulated crowds can then be placed in various situations to observe potential outcomes.
• This led to the creation of "generative agents," simulated people within a simulated town called "Smallville," which showcased autonomous AIs playing different roles and going about their daily lives.

We could take Chat GBT or another large language model, LLM. We can give it a prompt that roughly equates to, you know, here's be this person, here's a name, a description, and in this situation, how would they react?

This section details how large language models are leveraged to create dynamic and persona-driven AI agents, forming the foundation for sophisticated simulations.

Building and Interacting with Generative Agents [10:13]

• The process begins by creating a persona for each individual agent, defining their role, characteristics, and knowledge about other agents in the simulation.
• These agents then wake up and act autonomously, performing daily activities like brushing teeth, eating breakfast, and conversing without explicit step-by-step instructions.
• The natural language output of these AI agents needs to be grounded into concrete actions within the simulated environment (e.g., rendering emojis).
• Users can directly interact with these agents by posing questions or even intervening in the simulation, such as starting a fire, and observing the agents' reactions.

We create a little persona for each individual. So here's John Lynn. He's a pharmacy shopkeeper. We tell him that he runs the pharmacy in this town.

This part describes the practical construction and interactive capabilities of generative agents, demonstrating how they operate and can be influenced within a simulated world.

Simulating Complex Social Dynamics and Information Spread [15:29]

• A more complex scenario was simulated where an agent, Isabella, running a cafe, was given the intent to plan a Valentine's Day party.
• This event demonstrated information diffusion patterns similar to rumor or whisper networks, as Isabella told others, who then told more people.
• The simulation showed that half the town heard about the party, with a portion attending and others declining for various reasons.
• A further layer of complexity was added by giving another agent, Maria, a memory of having a crush on another agent, Klaus, leading to Maria asking Klaus to the party.
• This illustrates how internal states and relationships can influence simulated social interactions.

Isabella tells people, people tell other people. You can see this graph of who's hearing about this party from whom as the simulation plays out.

This section showcases the emergent behavior within simulations, illustrating how simple intentions can trigger complex social dynamics and information spread among AI agents.

Key Components of Generative Agent Architecture [18:50]

• Memory: Agents have a "memory stream," a record of everything they observe. This is processed using Retrieval Augmented Generation (RAG) to preferentially retrieve recent, important, and relevant memories, preventing LLMs from getting distracted by long contexts.
• Reflection: Agents are prompted to reflect on their memories to produce higher-level insights about their dispositions, interests, and goals. These reflections are reinserted into the memory stream, creating more consistent and goal-oriented behavior.
• Planning: Agents plan their day hour by hour and minute by minute. When encountering new observations, they can replan if necessary, allowing them to adapt to changes in the environment.

The first thing you need to do to create an agent like this is we need to give them the ability to remember.

This explains the core technical components that enable generative agents to function, including memory management, self-reflection, and adaptive planning.

Measuring Simulation Accuracy and Mitigating Bias [25:02]

• Measuring the accuracy of AI agent simulations is crucial, moving beyond mere believability.
• Approaches include "demographic agents" (representing population samples) and "persona agents" (narrative descriptions). However, these can lead to simplified and stereotyped behaviors.
• A more accurate method involves conducting 2-hour interviews with a representative sample of people to construct "digital twins" or generative agents.
• These agents' memories are the interview transcripts, and they are then subjected to the same surveys and experiments as the real individuals to gauge attitude and behavior replication.
• This approach has shown agents can replicate human responses 85% as well as people replicate themselves over two weeks on the General Social Survey.
• Interviews also reduce bias, as richer data allows for more subtle modeling compared to relying solely on demographic variables.

Rich qualitative information is an interesting direction.

This section delves into the methods for validating the accuracy of AI simulations, highlighting the importance of detailed qualitative data and its role in reducing stereotypical outputs.

Risks and Mitigation Strategies for AI Simulations [37:00]

• Not all studies or simulations replicate correctly, and errors can occur, particularly with quantitative outcomes where small percentage differences can be significant.
• The risks are categorized on a "ladder":
  • Possibility: Plausible chains of events leading to an outcome (generally reliable).
  • Qualitative: Estimating individual attitudes (safer, mostly works with rich data).
  • Quantitative: Recreating market research surveys, where errors can lead to wrong decisions (requires caution, best for narrowing down options for A/B testing).
  • Multi-agent simulation: Entire market or town simulations (highest risk, requires trusting individual agents and complex systems reasoning).
• Mitigation strategies include ensuring agents have in-domain data, utilizing the safer possibility and qualitative rungs for rough estimations, and validating important questions on small subsamples.

You need to tread carefully here.

This part addresses the potential pitfalls of AI simulations, outlining a tiered approach to trust based on the type of outcome being predicted and suggesting practical methods to manage these risks.

Frontiers and Applications of AI Agent Simulations [42:13]

• "Look Before You Launch" Tools: These tools help foresee policy backfires or negative interactions in online platforms by allowing iteration in simulation before real-world deployment.
• Training Soft Skills: Generative agents can serve as "sparring partners" for practicing conflict negotiation, salary discussions, or other interpersonal skills in a simulated environment, leading to improved real-world performance and reduced antisocial strategies.
• Business Applications: Significant opportunities exist in market research, user research, and internal data analysis, potentially leading to the development of startups focused on these capabilities.

There's something about this idea that I could try something sort of in simulation that really aids learning.

This final section explores the cutting-edge applications and future potential of AI agent simulations, emphasizing their role in proactive problem-solving, skill development, and business innovation.