arXiv AI July 7, 2026 By Stephen Pilli, Vivek Nallur

Predicting Biased Human Decision-Making with Large Language Models in Conversational Settings

arXiv:2601. 11049v2 Announce Type: replace-cross Abstract: We examine whether large language models (LLMs) can predict biased decision-making in conversational settings, and whether their predictions capture not only human cognitive biases but also how those effects change under cognitive load.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 7

Conditional Cognitive Biases in LLMs: How Biased User Turns Modulate In-Context Reasoning

arXiv:2608. 05166v1 Announce Type: cross Abstract: We present an evaluation of cognitive bias expression in state-of-the-art instruction-tuned LLMs under realistic multi-turn interaction settings.

By Sachini Weerasekara, Sagar Kamarthi, Jacqueline Isaacs

llms benchmarks safety

Hugging Face Trending Papers

Jul 13

Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling

Human choice behavior, including route choice, exhibits systematic behavioral biases that deviate from the assumptions of full rationality. Cumulative prospect theory (CPT) has been widely recognized as an effective framework for characterizing such behavioral patterns.

llms agents

arXiv AI

Jul 14

Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling

arXiv:2607. 11632v1 Announce Type: new Abstract: Human choice behavior, including route choice, exhibits systematic behavioral biases that deviate from the assumptions of full rationality.

By Jiangtao Han, Shoufeng Ma, Shuxian Xu, Geng Li, Shuai Ling, Ning Jia, Zhengbing He

llms agents

arXiv AI

Aug 14

Unmasking Conversational Bias in AI Multiagent Systems

arXiv:2501. 14844v3 Announce Type: replace-cross Abstract: Detecting biases in the outputs produced by generative models is essential to reduce the potential risks associated with their application in critical settings.

By Erica Coppolillo, Giuseppe Manco, Luca Maria Aiello

llms agents benchmarks safety

arXiv AI

Aug 28

Assessing mentalization in humans and large language models

The study evaluates mentalization—the capacity to infer others’ beliefs and intentions—in large language models (LLMs) using two economic games and cognitive computational modeling. Researchers tested 2,099 LLM agents from four model families (DeepSeek, GPT‑4.1, GPT‑5, Gemini 2.0 Flash) against opponents of varying sophistication, comparing their performance to 251 human participants. Results show that LLMs exhibit distinct mentalizing behaviors that vary by model provider and size, with strategic prompting generally enhancing performance; notably, GPT‑5 agents adapt their recursive reasoning depth to match opponent sophistication, outperforming humans in one task.

By Aamir Sohail, Xintong Zhong, Arkady Konovalov, Patricia L. Lockwood, Lei Zhang

llms agents

arXiv Computation and Language

Sep 22

Pretrained Persona Mixture Models and Tandem Models for Human Simulation

arXiv:2609.22607v1 Announce Type: new Abstract: We argue here that the current dominant practice in LLM human simulation: prompting instruction-tuned assistant language models to role-play personas,...

By Minwoo Kang, T\'ea Wright, Seun Eisape, Ayush Raj, Suhong Moon, Joseph Suh, Alane Suhr, David M. Chan, John Canny

llms

One email a morning, machine-written

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Conditional Cognitive Biases in LLMs: How Biased User Turns Modulate In-Context Reasoning

Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling

Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling

Unmasking Conversational Bias in AI Multiagent Systems

Assessing mentalization in humans and large language models

Pretrained Persona Mixture Models and Tandem Models for Human Simulation