REVIEW 2 cited by
Exploring the Intersection of Large Language Models and Agent-Based Modeling via Prompt Engineering
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The final frontier for simulation is the accurate representation of complex, real-world social systems. While agent-based modeling (ABM) seeks to study the behavior and interactions of agents within a larger system, it is unable to faithfully capture the full complexity of human-driven behavior. Large language models (LLMs), like ChatGPT, have emerged as a potential solution to this bottleneck by enabling researchers to explore human-driven interactions in previously unimaginable ways. Our research investigates simulations of human interactions using LLMs. Through prompt engineering, inspired by Park et al. (2023), we present two simulations of believable proxies of human behavior: a two-agent negotiation and a six-agent murder mystery game.
Forward citations
Cited by 2 Pith papers
-
Validating Generative Agent-Based Models for Logistics and Supply Chain Management Research
Six LLMs show an equivalence-versus-process paradox: some match human surface behaviors but few replicate human decision pathways, so GABMs need dual-level validation before use in logistics research.
-
Reinforce LLM Reasoning through Multi-Agent Reflection
DPSDP trains an actor-critic LLM pair with DPO-style preference learning on self-generated trajectories, improving iterative refinement accuracy on math benchmarks.
Discussion (0). Continue with ORCID to comment.