Human decision-making underlies all economic behavior. For the past four decades, human decision-making under uncertainty has continued to be explained by theoretical models based on prospect theory, a framework that was awarded the Nobel Prize in Economic Sciences. However, theoretical models of this kind have developed slowly, and robust, high-precision predictive models of human decisions remain a challenge. While machine learning is a natural candidate for solving these problems, it is currently unclear to what extent it can improve predictions obtained by current theories. We argue that this is mainly due to data scarcity, since noisy human behavior requires massive sample sizes to be accurately captured by off-the-shelf machine learning methods. To solve this problem, what is needed are machine learning models with appropriate inductive biases for capturing human behavior, and larger datasets. We offer two contributions towards this end: first, we construct "cognitive model priors" by pretraining neural networks with synthetic data generated by cognitive models (i.e., theoretical models developed by cognitive psychologists). We find that fine-tuning these networks on small datasets of real human decisions results in unprecedented state-of-the-art improvements on two benchmark datasets. Second, we present the first large-scale dataset for human decision-making, containing over 240,000 human judgments across over 13,000 decision problems. This dataset reveals the circumstances where cognitive model priors are useful, and provides a new standard for benchmarking prediction of human decisions under uncertainty.
Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being
University of Notre Dame（圣母大学）；University of Colorado, Boulder（科罗拉多大学博尔德分校）；University of California, Irvine（加州大学欧文分校）；Dartmouth College（达特茅斯学院）；Georgia Institute of Technology（佐治亚理工学院）；University of Washington（华盛顿大学）
Peri-AIIMS: Perioperative Artificial Intelligence Driven Integrated Modeling of Surgeries using Anesthetic, Physical and Cognitive Statuses for Predicting Hospital Outcomes
University of Florida（佛罗里达大学）；UF Health（佛罗里达大学健康医疗中心）；Rowan University（罗文大学）
Predicting Mild Cognitive Impairment Using Naturalistic Driving and Trip Destination Modeling
Department of Mechanical Engineering, Iowa State University（爱荷华州立大学机械工程系）；Institute for Transportation, Iowa State University（爱荷华州立大学交通研究所）；Department of Neurological Sciences, University of Nebraska Medical Center（内布拉斯加医学院神经科学系）
Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling
Goldsmiths, University of London（伦敦大学金史密斯学院）；School of Health Sciences, The University of Manchester（曼彻斯特大学健康科学学院）；Faculty of Computer Science and Engineering, Frankfurt University of Applied Sciences（法兰克福应用科学大学计算机科学与工程学院）；Institute of Psychiatry, Psychology and Neuroscience King’s College London（伦敦国王学院精神病学、心理学与神经科学研究所）
Cognitive Models as Simulators: The Case of Moral Decision-Making
McGill University（麦吉尔大学）；Université de Montréal（蒙特利尔大学）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）
为了实现理想的性能，当前的AI系统通常需要大量的训练数据。这在收集数据既昂贵又耗时的领域中尤其成问题，例如，人工智能系统需要与人类进行多次互动，收集他们的反馈。在这项工作中，我们证实了$\textit{cognitive models as simulators}$的想法，即让人工智能系统与认知模型而不是人类进行交互并从其收集反馈，从而使其训练过程成本更低，速度更快。在这里，我们在道德决策的背景下利用这个想法，通过让强化学习（RL）代理通过与最后通牒游戏（UG）的认知模型交互来学习公平性，这是行为和脑科学中研究公平性的一个典型任务。有趣的是，这些RL代理学会根据模拟UG响应者的情绪状态合理地调整他们的行为。我们的工作表明，使用认知模型作为人类的模拟器是训练AI系统的有效方法，为计算认知科学为AI做出贡献提供了重要途径。
Predicting cognitive load in immersive driving scenarios with a hybrid CNN-RNN model
Institute for Intelligent Systems Research and Innovation（智能系统研究与创新研究所）；Faculty of Computing and Information Technology (FoCIT)（计算与信息科技学院）；Swinburne University of Technology（斯威丁大学）
Flipping Minds by Flipping Bits: Decision-Level Cognitive Hijacking of Large Language Models
Information Engineering University（信息工程大学）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所信息安全国家重点实验室）
Predicting Court Decisions for Alimony: Avoiding Extra-legal Factors in Decision made by Judges and Not Understandable AI Models
Université de Lyon（里昂大学）；UJM-Saint-Etienne（圣埃蒂安让·莫内大学）；CNRS（法国国家科学研究中心）；Maison des Sciences de l’Homme, Lyon St-Étienne（里昂-圣埃蒂安人文之家）；Université Lumière Lyon 2（里昂第二大学）；Centre Max Weber（马克斯·韦伯中心）
OPTIMUS: Predicting Multivariate Outcomes in Alzheimer's Disease Using Multi-modal Data amidst Missing Values
University Lausanne Hospital (CHUV)（洛桑大学医院）；University of Lausanne (UNIL)（洛桑大学）；Zhejiang University（浙江大学）；Zhejiang University School of Medicine（浙江大学医学院）
