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

*Source* · `src-vezhnevets-2023-concordia` · status: **n/a**

| field | value |
|---|---|
| kind | Preprint |
| venue | arXiv |
| date | 2023-12-06 |
| url | https://arxiv.org/abs/2312.03664 |
| arxiv | 2312.03664 |
| accessed | 2026-10-24 |

## Authors

- Alexander Sasha Vezhnevets
- John P. Agapiou
- Avia Aharon
- Ron Ziv
- Jayd Matyas
- Edgar A. Duéñez-Guzmán
- William A. Cunningham
- Simon Osindero
- Danny Karmon
- Joel Z. Leibo

## Notes

Agents state intended actions in natural language; a Game Master agent, inspired by tabletop RPGs, decides outcomes, checks physical plausibility, and can call digital tools. Behavior comes from components mediating LLM calls and associative memory retrieval.

## Backlinks (computed)

**cited by:** [Many-agent LLM societies](../technique/llm-agent-societies.md), [LLM game master and narrative director](../technique/llm-game-master.md), [LLM-adjudicated state transitions (LLM as game mechanic)](../technique/llm-rule-adjudication.md)

## Citation

Alexander Sasha Vezhnevets, John P. Agapiou, Avia Aharon, Ron Ziv, Jayd Matyas, Edgar A. Duéñez-Guzmán, William A. Cunningham, Simon Osindero, Danny Karmon, Joel Z. Leibo (2023-12-06). *Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia*. arXiv https://arxiv.org/abs/2312.03664

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Provenance: created in run `2026-10-24-techniques-llm-agents`.