# Do Large Language Models Show Decision Heuristics Similar to Humans? A Case Study Using GPT-3.5 arXiv: https://arxiv.org/abs/2305.04400 ยท HTML: https://mlsanity.com/paper/2305.04400/ ## Key facts - Cited by 42 papers in the map (data through 14 Oct 2026). - Builds on 4 of its 37 references that are in the map. - First posted on arXiv 8 May 2023. ## Builds on (most cited first, 4 of 4) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019) - [A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models](https://mlsanity.com/paper/2303.10420/) (2023) - [Emergent Analogical Reasoning in Large Language Models](https://mlsanity.com/paper/2212.09196/) (2022) ## Built on by (most cited first, 30 of 42) - [LooGLE: Can Long-Context Language Models Understand Long Contexts?](https://mlsanity.com/paper/2311.04939/) (2023) - [Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models](https://mlsanity.com/paper/2401.01301/) (2024) - [AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways](https://mlsanity.com/paper/2406.02630/) (2024) - [Black-Box Access is Insufficient for Rigorous AI Audits](https://mlsanity.com/paper/2401.14446/) (2024) - [A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners](https://mlsanity.com/paper/2406.11050/) (2024) - [Prompting Techniques for Reducing Social Bias in LLMs through System 1 and System 2 Cognitive Processes](https://mlsanity.com/paper/2404.17218/) (2024) - [Cognitive Biases in Large Language Models: A Survey and Mitigation Experiments](https://mlsanity.com/paper/2412.00323/) (2024) - [A Philosophical Introduction to Language Models - Part II: The Way Forward](https://mlsanity.com/paper/2405.03207/) (2024) - [Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Review](https://mlsanity.com/paper/2401.01519/) (2024) - [Towards Rationality in Language and Multimodal Agents: A Survey](https://mlsanity.com/paper/2406.00252/) (2024) - [When Your AI Agent Succumbs to Peer-Pressure: Studying Opinion-Change Dynamics of LLMs](https://mlsanity.com/paper/2510.19107/) (2025) - [Will the Real Linda Please Stand up...to Large Language Models? Examining the Representativeness Heuristic in LLMs](https://mlsanity.com/paper/2404.01461/) (2024) - [Using Artificial Populations to Study Psychological Phenomena in Neural Models](https://mlsanity.com/paper/2308.08032/) (2023) - [A Theory of Response Sampling in LLMs: Part Descriptive and Part Prescriptive](https://mlsanity.com/paper/2402.11005/) (2024) - [Do Large Language Models Learn Human-Like Strategic Preferences?](https://mlsanity.com/paper/2404.08710/) (2024) - [Large Language Model Recall Uncertainty is Modulated by the Fan Effect](https://mlsanity.com/paper/2407.06349/) (2024) - [How Does Cognitive Bias Affect Large Language Models? A Case Study on the Anchoring Effect in Price Negotiation Simulations](https://mlsanity.com/paper/2508.21137/) (2025) - [Can Large Language Models Replace Human Subjects? A Large-Scale Replication of Scenario-Based Experiments in Psychology and Management](https://mlsanity.com/paper/2409.00128/) (2024) - [The Other Mind: How Language Models Exhibit Human Temporal Cognition](https://mlsanity.com/paper/2507.15851/) (2025) - [Is There Any Social Principle for LLM-Based Agents?](https://mlsanity.com/paper/2308.11136/) (2023) - [Generative AI-in-the-loop: Integrating LLMs and GPTs into the Next Generation Networks](https://mlsanity.com/paper/2406.04276/) (2024) - [Balancing Rigor and Utility: Mitigating Cognitive Biases in Large Language Models for Multiple-Choice Questions](https://mlsanity.com/paper/2406.10999/) (2024) - [Humanlike Cognitive Patterns as Emergent Phenomena in Large Language Models](https://mlsanity.com/paper/2412.15501/) (2024) - [The Fragility of Strategic Thinking in Large Language Models](https://mlsanity.com/paper/2510.10813/) (2025) - [From Bytes to Biases: Investigating the Cultural Self-Perception of Large Language Models](https://mlsanity.com/paper/2312.17256/) (2023) - [Meaningless is better: hashing bias-inducing words in LLM prompts improves performance in logical reasoning and statistical learning](https://mlsanity.com/paper/2411.17304/) (2024) - [Iconicity in Large Language Models](https://mlsanity.com/paper/2501.05643/) (2025) - [DeFrame: Debiasing Large Language Models Against Framing Effects](https://mlsanity.com/paper/2602.04306/) (2026) - [The Model Says Walk: How Surface Heuristics Override Implicit Constraints in LLM Reasoning](https://mlsanity.com/paper/2603.29025/) (2026) - [One Size Fits None: Heuristic Collapse in LLM Investment Advice](https://mlsanity.com/paper/2604.23837/) (2026) ## Abstract (arXiv) A Large Language Model (LLM) is an artificial intelligence system that has been trained on vast amounts of natural language data, enabling it to generate human-like responses to written or spoken language input. GPT-3.5 is an example of an LLM that supports a conversational agent called ChatGPT. In this work, we used a series of novel prompts to determine whether ChatGPT shows heuristics, biases, and other decision effects. We also tested the same prompts on human participants. Across four studies, we found that ChatGPT was influenced by random anchors in making estimates (Anchoring Heuristic, Study 1); it judged the likelihood of two events occurring together to be higher than the likelihood of either event occurring alone, and it was erroneously influenced by salient anecdotal information (Representativeness and Availability Heuristic, Study 2); it found an item to be more efficacious when its features were presented positively rather than negatively - even though both presentations contained identical information (Framing Effect, Study 3); and it valued an owned item more than a newly found item even though the two items were identical (Endowment Effect, Study 4). In each study, human participants showed similar effects. Heuristics and related decision effects in humans are thought to be driven by cognitive and affective processes such as loss aversion and effort reduction. The fact that an LLM - which lacks these processes - also shows such effects invites consideration of the possibility that language may play a role in generating these effects in humans.