Reasoning and the Trusting Behavior of DeepSeek and GPT: An Experiment Revealing Hidden Fault Lines in Large Language Models
On the map: LLM reasoning → Reasoning for agents, planning and code
Abstract (arXiv)
When encountering increasingly frequent performance improvements or cost reductions from a new large language model (LLM), developers of applications leveraging LLMs must decide whether to take advantage of these improvements or stay with older tried-and-tested models. Low perceived switching frictions can lead to choices that do not consider more subtle behavior changes that the transition may induce. Our experiments use a popular game-theoretic behavioral economics model of trust to show stark differences in the trusting behavior of OpenAI's and DeepSeek's models. We highlight a collapse in the economic trust behavior of the o1-mini and o3-mini models as they reconcile profit-maximizing and risk-seeking with future returns from trust, and contrast it with DeepSeek's more sophisticated and profitable trusting behavior that stems from an ability to incorporate deeper concepts like forward planning and theory-of-mind. As LLMs form the basis for high-stakes commercial systems, our results highlight the perils of relying on LLM performance benchmarks that are too narrowly defined and suggest that careful analysis of their hidden fault lines should be part of any organization's AI strategy.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 13 of its 34 references that are in the map. First posted on arXiv 18 Feb 2025.
Builds on
13 of 34 references.
Closest ancestors · 1
Same direction, newest first
- 2024GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations19 Feb
Other references · 12
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
- 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,718
- 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,151
- 2023Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Studycited by 203
- 2022Thinking Fast and Slow in Large Language Modelscited by 136
- 2023Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysiscited by 91
- 2024Can Large Language Model Agents Simulate Human Trust Behavior?cited by 62
- 2023The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspectivecited by 57
- 2024Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLMcited by 38
- 2024Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machinacited by 29
- 2023Psychological Metrics for Dialog System Evaluationcited by 4
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Citing papers per year: 2025: 2.
2 papers.
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- 2026Language Models Exhibit Inconsistent Biases Towards Algorithmic Agents and Human Experts
- 2025A closer look at how large language models trust humans: patterns and biases
- 2025Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents
- 2025Social preferences with unstable interactive reasoning: Large language models in economic trust games
- 2025SciTrust 2.0: A Comprehensive Framework for Evaluating Trustworthiness of Large Language Models in Scientific Applications
- 2026A Unified Evaluation Framework for Trustworthy Large Language Models, Agentic AI, and Multimodal Systems