# The Wisdom of Partisan Crowds: Comparing Collective Intelligence in Humans and LLM-based Agents

arXiv: https://arxiv.org/abs/2311.09665 · HTML: https://mlsanity.com/paper/2311.09665/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 11 of 11)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022)
- [On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://mlsanity.com/paper/2212.08061/) (2022)
- [Turning large language models into cognitive models](https://mlsanity.com/paper/2306.03917/) (2023)
- [Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms](https://mlsanity.com/paper/2310.05984/) (2023)
- [Lyfe Agents: Generative agents for low-cost real-time social interactions](https://mlsanity.com/paper/2310.02172/) (2023)
- [Quantifying the Impact of Large Language Models on Collective Opinion Dynamics](https://mlsanity.com/paper/2308.03313/) (2023)
- [Computational Agent-based Models in Opinion Dynamics: A Survey on Social Simulations and Empirical Studies](https://mlsanity.com/paper/2306.03446/) (2023)

## Built on by (most cited first, 30 of 30)

- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [LLM Generated Persona is a Promise with a Catch](https://mlsanity.com/paper/2503.16527/) (2025)
- [LLM Voting: Human Choices and AI Collective Decision Making](https://mlsanity.com/paper/2402.01766/) (2024)
- [AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios](https://mlsanity.com/paper/2410.19346/) (2024)
- [Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks](https://mlsanity.com/paper/2406.17232/) (2024)
- [Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulations](https://mlsanity.com/paper/2504.03274/) (2025)
- [LLM-based multi-agent poetry generation in non-cooperative environments](https://mlsanity.com/paper/2409.03659/) (2024)
- [Does Socialization Emerge in AI Agent Society? A Case Study of Moltbook](https://mlsanity.com/paper/2602.14299/) (2026)
- [The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies](https://mlsanity.com/paper/2509.18052/) (2025)
- [A Survey on Human-Centric LLMs](https://mlsanity.com/paper/2411.14491/) (2024)
- [Bridging the Creativity Understanding Gap: Small-Scale Human Alignment Enables Expert-Level Humor Ranking in LLMs](https://mlsanity.com/paper/2502.20356/) (2025)
- [BookWorld: From Novels to Interactive Agent Societies for Creative Story Generation](https://mlsanity.com/paper/2504.14538/) (2025)
- [PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization](https://mlsanity.com/paper/2603.19649/) (2026)
- [Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits](https://mlsanity.com/paper/2605.18890/) (2026)
- [Wisdom from Diversity: Bias Mitigation Through Hybrid Human-LLM Crowds](https://mlsanity.com/paper/2505.12349/) (2025)
- [AI Agent Behavioral Science](https://mlsanity.com/paper/2506.06366/) (2025)
- [DEBATE: A Large-Scale Benchmark for Evaluating Opinion Dynamics in Role-Playing LLM Agents](https://mlsanity.com/paper/2510.25110/) (2025)
- [Large Language Models can Achieve Social Balance](https://mlsanity.com/paper/2410.04054/) (2024)
- [To Mask or to Mirror: Human-AI Alignment in Collective Reasoning](https://mlsanity.com/paper/2510.01924/) (2025)
- [The Society of HiveMind: Multi-Agent Optimization of Foundation Model Swarms to Unlock the Potential of Collective Intelligence](https://mlsanity.com/paper/2503.05473/) (2025)
- [Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences](https://mlsanity.com/paper/2507.08440/) (2025)
- [AI Agents Alone Are Not (Yet) Sufficient for Social Simulation](https://mlsanity.com/paper/2603.00113/) (2026)
- [Neuro-Symbolic Traders: Assessing the Wisdom of AI Crowds in Markets](https://mlsanity.com/paper/2410.14587/) (2024)
- [Bridging Minds and Machines: Toward an Integration of AI and Cognitive Science](https://mlsanity.com/paper/2508.20674/) (2025)
- [Disentangling Interaction and Bias Effects in Opinion Dynamics of Large Language Models](https://mlsanity.com/paper/2509.06858/) (2025)
- [Collaborative QA using Interacting LLMs. Impact of Network Structure, Node Capability and Distributed Data](https://mlsanity.com/paper/2511.14098/) (2025)
- [An Empirical Study of Collective Behaviors and Social Dynamics in Large Language Model Agents](https://mlsanity.com/paper/2602.03775/) (2026)
- [From Plausible to Causal: Counterfactual Semantics for Policy Evaluation in Simulated Online Communities](https://mlsanity.com/paper/2604.03920/) (2026)
- [Bypassing Safety Guardrails in LLMs Using Humor](https://mlsanity.com/paper/2504.06577/) (2025)
- [CogEvolution: A Human-like Generative Educational Agent to Simulate Student's Cognitive Evolution](https://mlsanity.com/paper/2604.14786/) (2026)

## Abstract (arXiv)

Human groups are able to converge on more accurate beliefs through deliberation, even in the presence of polarization and partisan bias -- a phenomenon known as the "wisdom of partisan crowds." Generated agents powered by Large Language Models (LLMs) are increasingly used to simulate human collective behavior, yet few benchmarks exist for evaluating their dynamics against the behavior of human groups. In this paper, we examine the extent to which the wisdom of partisan crowds emerges in groups of LLM-based agents that are prompted to role-play as partisan personas (e.g., Democrat or Republican). We find that they not only display human-like partisan biases, but also converge to more accurate beliefs through deliberation as humans do. We then identify several factors that interfere with convergence, including the use of chain-of-thought prompt and lack of details in personas. Conversely, fine-tuning on human data appears to enhance convergence. These findings show the potential and limitations of LLM-based agents as a model of human collective intelligence.