Using LLMs to simulate mass user behavior

One of the great problems of mass-user products is predicting emerging behaviors—both good and bad.

A recent study by researchers at Stanford University provides a solution that takes advantage of large language models (LLM) to predict how large populations can behave and affect each other.

The study, titled “Generative Agents: Interactive Simulacra of Human Behavior,” explores the potential of generative models in creating an AI agent architecture that remembers its interactions, reflects on the information it receives, and plans long- and short-term goals based on an ever-expanding memory stream.

By interacting with each other, the agents can emulate the more intricate social behaviors that emerge from the interactions of a large population.

Key findings:

• The study uses agents in SmallVille, a sandbox game environment composed of various objects such as buffets, schools, bars, and more

• Each agent is powered by an LLM (like GPT-4) and has a prompt that contains its character descriptions, memories, and goals

• The agents can interact with their environment and each other and affect each other’s memories and goals

• The researchers designed an intricate system that enables agents to store and retrieve relevant memories to include in the LLM prompt for each interaction

• The agents also have a layered planning function that allows them to plan for both the long- and short-term.

• Their experiments show that the AI agents learn to coordinate among themselves without being explicitly instructed to do so

• The researchers believe their work has far-reaching, practical applications, such as prototyping the dynamics in mass-user products such as social networks

• The agents can help predict the effects of spreading misinformation or experiment with different counterfactual events

Read the full article on VentureBeat

For more on LLM research:

• New AI technique uses language to learn world models

• What you need to know about multimodal language models

• To understand language models, we must separate “language” from “thought”