title: Generative agent-based modeling with actions grounded in physical,
    social, or digital space using Concordia
summary: 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.
paper_authors._id: 657127baba19285f32dacba5
paper_authors.name: Alexander Sasha Vezhnevets
paper_authors._id: 657127baba19285f32dacba6
paper_authors.name: John P. Agapiou
paper_authors._id: 657127baba19285f32dacba7
paper_authors.name: Avia Aharon
paper_authors._id: 657127baba19285f32dacba8
paper_authors.name: Ron Ziv
paper_authors._id: 657127baba19285f32dacba9
paper_authors.name: Jayd Matyas
paper_authors._id: 657127baba19285f32dacbaa
paper_authors.name: Edgar A. Duéñez-Guzmán
paper_authors._id: 657127baba19285f32dacbab
paper_authors.name: William A. Cunningham
paper_authors._id: 657127baba19285f32dacbac
paper_authors.name: Simon Osindero
paper_authors._id: 657127baba19285f32dacbad
paper_authors.name: Danny Karmon
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-07T12:22:29.862Z
paper_authors._id: 5e048538cbfd036a99df0db4
paper_authors.avatarUrl: /avatars/7afa83361d4727b56f84961d5bfc31f8.svg
paper_authors.fullname: Danny Karmon
paper_authors.type: user
paper_authors.user: dakarmon
paper_authors._id: 657127baba19285f32dacbae
paper_authors.name: Joel Z. Leibo
paper_title: Generative agent-based modeling with actions grounded in physical,
    social, or digital space using Concordia
paper_summary: 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.
paper_ai_keywords: Large Language Models (LLM) Generative Agent-Based Models (GABM) Agent-Based Models (ABM) associative memory retrieval Game Master (GM) API calls tabletop role-playing games general AI assistants synthetic data