Humans are not homo economicus (i.e., rational economic beings). As humans, we exhibit systematic behavioral biases such as loss aversion, anchoring, framing, etc., which lead us to make suboptimal economic decisions. Insofar as such biases may be embedded in text data on which large language models (LLMs) are trained, to what extent are LLMs prone to the same behavioral biases? Understanding these biases in LLMs is crucial for deploying LLMs to support human decision-making. We propose utility theory-a paradigm at the core of modern economic theory-as an approach to evaluate the economic biases of LLMs. Utility theory enables the quantification and comparison of economic behavior against benchmarks such as perfect rationality or human behavior. To demonstrate our approach, we quantify and compare the economic behavior of a variety of open- and closed-source LLMs. We find that the economic behavior of current LLMs is neither entirely human-like nor entirely economicus-like. We also find that most current LLMs struggle to maintain consistent economic behavior across settings. Finally, we illustrate how our approach can measure the effect of interventions such as prompting on economic biases.
Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling
Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University, China（复杂管理系统的计算与分析实验室，天津大学，中国）；College of Management and Economics, Tianjin University, China（管理与经济学院，天津大学，中国）；Faculty of Science and Engineering, University of Nottingham Ningbo China（科学与工程学院，诺丁汉大学宁波校区，中国）
Faculty of Data and Decision Sciences, Technion（以色列理工学院数据与决策科学学院）；The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Google Research（谷歌研究院）；Hebrew University Business School（希伯来大学商学院）；Federmann Center for the Study of Rationality, Hebrew University（希伯来大学费德曼理性研究中心）；Department of Cognitive and Brain Sciences, Hebrew University（希伯来大学认知与脑科学系）
How Does Alignment Tuning Shape Representations of Sycophancy and Related Cue-Induced Biases in LLMs?
University of Michigan（密歇根大学）；Jinesis Lab, University of Toronto & Vector Institute（多伦多大学Jinesis实验室和向量研究所）；Max-Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；ELLIS Institute Tübingen（图宾根ELLIS研究所）
A closer look at how large language models trust humans: patterns and biases
The Hebrew University Business School（希伯来大学商学院）；The Federmann Center for the study of Rationality（费德曼理性研究中心）；The Hebrew University of Jerusalem（特拉维夫希伯来大学）
UGID: Unified Graph Isomorphism for Debiasing Large Language Models
University of Electronic Science and Technology of China（电子科技大学）；Mohamed bin Zayed University of Artificial Intelligence（Mohamed bin Zayed人工智能大学）；South China University of Technology（华南理工大学）
Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection
The University of Manchester（曼彻斯特大学）；Stevens Institute of Technology（史蒂文斯理工学院）；The Fin AI（Fin AI）；Columbia University（哥伦比亚大学）；Islamic University of Technology（伊斯兰科技大学）；Athens University of Economics and Business（雅典经济与商业大学）；Archimedes, Athena Research Center（阿基米德，亚特兰蒂斯研究中心）；MBZUAI；McGill University（麦吉尔大学）；Mila - Quebec AI Institute（魁北克人工智能研究所）；Dubai Police（迪拜警察）；University of Melbourne（墨尔本大学）；Halmstad University（哈姆斯塔德大学）；University of Florida（佛罗里达大学）；ELLIS Manchester（曼彻斯特ELLIS）
