Pith Number

pith:J4YTC66I

pith:2023:J4YTC66IRWHUPWJY4T7DBURYQN

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S$^3$: Social-network Simulation System with Large Language Model-Empowered Agents

Chen Gao, Depeng Jin, Huandong Wang, Jinghua Piao, Jinzhu Mao, Xiaochong Lan, Yong Li, Zhihong Lu

LLM agents in the S3 system emulate human perception and actions to produce emergent social network phenomena like information and emotion propagation.

arxiv:2307.14984 v3 · 2023-07-27 · cs.SI

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4 Citations open

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Claims

C1strongest claim

By endowing the agent in the system with the ability to perceive the informational environment and emulate human actions, we observe the emergence of population-level phenomena, including the propagation of information, attitudes, and emotions. ... the results demonstrate promising accuracy.

C2weakest assumption

That prompt engineering and prompt tuning suffice to make LLM agents emulate genuine human behavior in social networks closely enough for the observed population-level phenomena to be meaningful.

C3one line summary

S³ uses LLM agents to simulate social networks by modeling emotion, attitude, and interaction, producing emergent propagation phenomena with promising accuracy on real data.

References

46 extracted · 46 resolved · 4 Pith anchors

[1] Using large language models to simulate multiple humans and replicate human subject studies 2023

[2] Advancing the art of simulation in the social sciences 1997

[3] Modeling echo chambers and polarization dynamics in social networks.Physical Review Letters, 124(4):048301, 2020 2020

[4] Emer- gence of polarized ideological opinions in multidimensional topic spaces 2021

[5] A guide to simulation, 1987 1987

Formal links

2 machine-checked theorem links

Cited by

29 papers in Pith

AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society

Large Language Model Agent: A Survey on Methodology, Applications and Challenges

A closer look at how large language models trust humans: patterns and biases

Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research

Mechanism Plausibility in Generative Agent-Based Modeling

Receipt and verification

First computed	2026-05-17T23:38:14.238778Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4f31317bc88d8f47d938e4fe30d238835eab3f0f39968a635b2f58f30744dd12

Aliases

arxiv: 2307.14984 · arxiv_version: 2307.14984v3 · doi: 10.48550/arxiv.2307.14984 · pith_short_12: J4YTC66IRWHU · pith_short_16: J4YTC66IRWHUPWJY · pith_short_8: J4YTC66I

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/J4YTC66IRWHUPWJY4T7DBURYQN \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 4f31317bc88d8f47d938e4fe30d238835eab3f0f39968a635b2f58f30744dd12

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "0cfb4359e7ea15cec682937d15a0d285c38133ea90bcd3a9424ce2685df0911b",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.SI",
    "submitted_at": "2023-07-27T16:24:56Z",
    "title_canon_sha256": "4eeeee15272aa3fdb97785e95b84e247c0975228bce48d3060420ac7ac0d9db7"
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  "source": {
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    "kind": "arxiv",
    "version": 3
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}