The study investigates how large language models (LLMs) perform in a double auction market, a common economic mechanism. By replacing human participants with LLM agents, the authors find that markets with LLMs converge more slowly or not at all, leading to less efficient resource allocations. Analysis of trading decisions reveals significant variation across model families and roles, and a lexical study of Chain-of-Thought traces links trade execution to a shift from strategic thinking to urgency.
By Pawel Struski, Jakub Swistak, Inez Okulska, Przemyslaw Biecek
The study investigates how Large Language Models (LLMs) acting as surrogate consumers are influenced by marketing pricing cues such as just‑below pricing and promotional framing. Using a tool called "Tool‑Lab" to trace information acquisition, the researchers found that when no cost is imposed, pricing cues rarely mislead LLMs, but when acquisition costs are introduced under a vague goal prompt, LLMs tend to omit important diagnostic attributes and make suboptimal choices similar to human heuristics. The findings suggest that marketing heuristics in AI‑driven shopping are shaped more by storefront information architecture than by inherent LLM limitations.
By Davood Wadi, Yu Ma
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.
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
The paper investigates whether large language models (LLMs) make decisions in ways that mirror human cognition. Using a new 140,000-trial product choice benchmark, the authors test 12 open‑source and commercial LLMs to see if their context sensitivity aligns with a cognitive economic theory that relies on problem categorization and attention allocation. While context prompts human‑like shifts in choice and problem categorization, it does not consistently reweight attention between features such as price and quality, and neither scaling nor chain‑of‑thought reasoning produces human‑like behavior. The findings indicate that LLM decision mechanisms differ from those of humans.
By Johnathan Sun, Andrei Shleifer, Yonatan Belinkov
The paper evaluates how Large Language Models generate preference distributions for air travel, restaurants, and consumer products. It finds that while each model produces self-coherent outcomes that stabilize quickly, there is significant disagreement across different model families and scales, with little consensus even on the most probable preferences. These discrepancies persist across various decoding strategies, temperature settings, and prompt variations, indicating that the model choice itself has a larger impact than prompt wording.
By Fan Huang, Minsuk Kim, C. Tyler Diggans, Filippo Radicchi
arXiv:2604. 08525v2 Announce Type: replace Abstract: Large language models (LLMs) are trained to align with user preferences through methods like reinforcement learning.
By Addison J. Wu, Ryan Liu, Shuyue Stella Li, Yulia Tsvetkov, Thomas L. Griffiths
The paper introduces TruthMarketTwin, a simulation framework that uses agent-based modeling to study large language model (LLM) agents in e‑commerce markets characterized by asymmetric information. It models bilateral trade where sellers and buyers make strategic decisions about listings, purchases, ratings, and recourse to maximize profit and utility. The study finds that LLM agents can autonomously exploit weaknesses in reputation‑based governance, but that warrant enforcement can reduce deception and alter strategic behavior.
By Shijun Lei, Quang Nguyen, Swapneel S Mehta, Zeping Li, Huichuan Fu, Xiaolong Zheng, Siki Chen, Yunji Liang, Philip Torr, Zhenfei Yin
arXiv:2505. 17648v5 Announce Type: replace-cross Abstract: We introduce a framework for simulating macroeconomic expectations in survey experiments using LLM-based economic agents (LLM Agents).
By Jianhao Lin, Lexuan Sun, Yixin Yan
arXiv:2602. 09802v2 Announce Type: replace Abstract: As Large Language Models (LLMs) are increasingly deployed in applications such as travel assistance and purchasing support, they are often required to make subjective choices on behalf of users in settings where no objectively correct answer exists.
By Manon Reusens, Sofie Goethals, Toon Calders, David Martens
The paper introduces Market Signal Injection (MSI), an attack that alters how market data is formatted or described—without changing its numerical values—to influence large language model (LLM) pricing agents. Experiments on nine open‑weight and three proprietary models in simulated duopoly and triopoly markets show that sentiment‑based formatting changes cause significant shifts in firm behavior, profits, and consumer surplus. The study also demonstrates that model susceptibility varies across families, that larger models are not always more robust, and that techniques such as input canonicalization and decision boundary anchoring can partially mitigate these attacks.
By Dohun Lee, Hyunwoo Park
arXiv:2608. 11215v1 Announce Type: new Abstract: Simulating societies of many large language model (LLM) agents is expensive, yet the questions asked of such simulations are usually macroscopic: phase behaviour, stylised facts, and scaling with the number of agents $N$, not the cognition of any single agent.
By Igor Itkin