Generative Agent-Based Models for Complex Systems Research: a review
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866913470973214720 |
|---|---|
| author | Lu, Yikang Aleta, Alberto Du, Chunpeng Shi, Lei Moreno, Yamir |
| author_facet | Lu, Yikang Aleta, Alberto Du, Chunpeng Shi, Lei Moreno, Yamir |
| contents | The advent of Large Language Models (LLMs) has significantly transformed the fields of natural and social sciences. Generative Agent-Based Models (GABMs), which utilize large language models in place of real subjects, are gaining increasing public attention. Far from aiming for comprehensiveness, this paper aims to offer readers an opportunity to understand how large language models are disrupting complex systems research and behavioral sciences. In particular, we evaluate recent advancements in various domains within complex systems, encompassing network science, evolutionary game theory, social dynamics, and epidemic propagation. Additionally, we propose possible directions for future research to further advance these fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09175 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative Agent-Based Models for Complex Systems Research: a review Lu, Yikang Aleta, Alberto Du, Chunpeng Shi, Lei Moreno, Yamir Physics and Society The advent of Large Language Models (LLMs) has significantly transformed the fields of natural and social sciences. Generative Agent-Based Models (GABMs), which utilize large language models in place of real subjects, are gaining increasing public attention. Far from aiming for comprehensiveness, this paper aims to offer readers an opportunity to understand how large language models are disrupting complex systems research and behavioral sciences. In particular, we evaluate recent advancements in various domains within complex systems, encompassing network science, evolutionary game theory, social dynamics, and epidemic propagation. Additionally, we propose possible directions for future research to further advance these fields. |
| title | Generative Agent-Based Models for Complex Systems Research: a review |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2408.09175 |