People's decisions often deviate from classical notions of rationality, incurring costs to themselves and society. One way to reduce the costs of poor decisions is to redesign the decision problems people face to encourage better choices. While often subtle, these nudges can have dramatic effects on behavior and are increasingly popular in public policy, health care, and marketing. Although nudges are often designed with psychological theories in mind, they are typically not formalized in computational terms and their effects can be hard to predict. As a result, designing nudges can be difficult and time-consuming. To address this challenge, we propose a computational framework for understanding and predicting the effects of nudges. Our approach builds on recent work modeling human decision making as adaptive use of limited cognitive resources, an approach called resource-rational analysis. In our framework, nudges change the metalevel problem the agent faces-that is, the problem of how to make a decision. This changes the optimal sequence of cognitive operations an agent should execute, which in turn influences their behavior. We show that models based on this framework can account for known effects of nudges based on default options, suggested alternatives, and information highlighting. In each case, we validate the model's predictions in an experimental process-tracing paradigm. We then show how the framework can be used to automatically construct optimal nudges, and demonstrate that these nudges improve people's decisions more than intuitive heuristic approaches. Overall, our results show that resource-rational analysis is a promising framework for formally characterizing and constructing nudges. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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认知受限代理的最佳助推：用于建模、预测和控制选择架构效果的框架。


人们的决定常常偏离经典的理性概念，给自己和社会带来成本。减少错误决策成本的一种方法是重新设计人们面临的决策问题，以鼓励更好的选择。虽然这些推动往往很微妙，但它们可以对行为产生巨大影响，并且在公共政策、医疗保健和营销中越来越受欢迎。尽管助推通常是根据心理学理论设计的，但它们通常没有用计算术语形式化，而且它们的效果很难预测。因此，设计推动可能既困难又耗时。为了应对这一挑战，我们提出了一个计算框架来理解和预测助推的影响。我们的方法建立在最近的工作基础上，将人类决策建模为有限认知资源的适应性使用，这种方法称为资源理性分析。在我们的框架中，助推改变了代理面临的元级问题，即如何做出决策的问题。这改变了代理应该执行的认知操作的最佳顺序，进而影响他们的行为。我们证明，基于此框架的模型可以解释基于默认选项、建议替代方案和信息突出显示的微调的已知效果。在每种情况下，我们都在实验过程跟踪范例中验证模型的预测。然后，我们展示了如何使用该框架来自动构建最佳助推，并证明这些助推比直观的启发式方法更能改善人们的决策。总的来说，我们的结果表明，资源理性分析是一个有前途的框架，用于正式表征和构建助推。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。