Agent-based modeling has been around for decades, and applied widely across
the social and natural sciences. The scope of this research method is now
poised to grow dramatically as it absorbs the new affordances provided by Large
Language Models (LLM)s. Generative Agent-Based Models (GABM) are not just
classic Agent-Based Models (ABM)s where the agents talk to one another. Rather,
GABMs are constructed using an LLM to apply common sense to situations, act
"reasonably", recall common semantic knowledge, produce API calls to control
digital technologies like apps, and communicate both within the simulation and
to researchers viewing it from the outside. Here we present Concordia, a
library to facilitate constructing and working with GABMs. Concordia makes it
easy to construct language-mediated simulations of physically- or
digitally-grounded environments. Concordia agents produce their behavior using
a flexible component system which mediates between two fundamental operations:
LLM calls and associative memory retrieval. A special agent called the Game
Master (GM), which was inspired by tabletop role-playing games, is responsible
for simulating the environment where the agents interact. Agents take actions
by describing what they want to do in natural language. The GM then translates
their actions into appropriate implementations. In a simulated physical world,
the GM checks the physical plausibility of agent actions and describes their
effects. In digital environments simulating technologies such as apps and
services, the GM may handle API calls to integrate with external tools such as
general AI assistants (e.g., Bard, ChatGPT), and digital apps (e.g., Calendar,
Email, Search, etc.). Concordia was designed to support a wide array of
applications both in scientific research and for evaluating performance of real
digital services by simulating users and/or generating synthetic data.

Agent-based modeling has evolved with the integration of Large Language Models (LLMs) to create Generative Agent-Based Models (GABMs) facilitated by Concordia, enabling language-mediated simulations in physically or digitally grounded environments.

基于 Concordia 的将行动植根于物理、社交或数字空间的生成式基于代理的建模

Generative agent-based modeling with actions grounded in physical,  social, or digital space using Concordia

We discuss the emerging new opportunity for building feedback-rich
computational models of social systems using generative artificial
intelligence. Referred to as Generative Agent-Based Models (GABMs), such
individual-level models utilize large language models such as ChatGPT to
represent human decision-making in social settings. We provide a GABM case in
which human behavior can be incorporated in simulation models by coupling a
mechanistic model of human interactions with a pre-trained large language
model. This is achieved by introducing a simple GABM of social norm diffusion
in an organization. For educational purposes, the model is intentionally kept
simple. We examine a wide range of scenarios and the sensitivity of the results
to several changes in the prompt. We hope the article and the model serve as a
guide for building useful diffusion models that include realistic human
reasoning and decision-making.

利用生成式人工智能建立反馈丰富的社会系统计算模型，通过生成代理基于模型（GABMs），将人类决策过程在社会环境中进行模拟，并探讨了社会规范扩散的一种简单 GABM 模型，以及该模型在不同情境下的敏感性和重要性。