Generative agent-based modeling with actions grounded in physical,
  social, or digital space using Concordia
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.
657127baba19285f32dacba5
Alexander Sasha Vezhnevets
657127baba19285f32dacba6
John P. Agapiou
657127baba19285f32dacba7
Avia Aharon
657127baba19285f32dacba8
Ron Ziv
657127baba19285f32dacba9
Jayd Matyas
657127baba19285f32dacbaa
Edgar A. Duéñez-Guzmán
657127baba19285f32dacbab
William A. Cunningham
657127baba19285f32dacbac
Simon Osindero
657127baba19285f32dacbad
Danny Karmon
admin_assigned
2023-12-07T12:22:29.862Z
5e048538cbfd036a99df0db4
/avatars/7afa83361d4727b56f84961d5bfc31f8.svg
Danny Karmon
user
dakarmon
657127baba19285f32dacbae
Joel Z. Leibo
Generative agent-based modeling with actions grounded in physical,
  social, or digital space using Concordia
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.
Concordia is a library that enables the construction of Generative Agent-Based Models (GABMs) using Large Language Models (LLMs) to simulate realistic agent behaviors and interactions in physical or digital environments.
Agent-Based Models Generative Agent-Based Models Large Language Models associative memory retrieval Game Master API calls general AI assistants