Procedural Complexity and Economic Behavior

经济学家传统上认为，人类能够使用非常复杂的决策规则在市场和组织中做出最优选择。经济学家还传统地认为，人类有能力遵守政府和官僚机构制定的非常复杂的规则，比如复杂的税法和拍卖规则。几十年的受控实验室实验和现场数据研究对这些传统假设提出了质疑。经济学家明白，人类能够完成的任务的复杂性存在界限，但事实证明，要正式定义和衡量复杂性，并将其用于预测和制定政策，是很困难的。该提案使用了经济学、心理学和计算机科学的概念和经验工具，仔细地实验测量了规则的哪些特征使它们在难度和人类执行的个人负担方面变得复杂。这些数据将提高我们在面对复杂性时预测人类行为的能力，并使我们能够制定有效、简化的政策，切实考虑到人类应对复杂性的能力。内部决策过程和外部规则都可以建模为算法，由人类而不是机器实现。例如，重复博弈中的策略，以及许多决策问题中的过程，都可以被描述为理论计算机科学算法的简单形式模型。本研究包括一系列实验，旨在评估对程序和规则的算法进行分类的各种方法，这些算法对人类来说或多或少是复杂的，并了解哪些复杂性的衡量标准准确地预测和描述了人类的错误和主观成本。该提案考虑了对算法复杂性建模的不同方法，并检查了它们如何很好地预测受试者在实施决策规则时的困难。这项研究的目标是发展一种基于经验的人类程序复杂性决定因素的特征，这将有助于建立人类行为的预测模型，并设计更有效的政策。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

AbstractEconomists traditionally assume that humans are capable of using very complex decision rules to make optimal choices in markets and organizations. Economists also traditionally assume that humans are capable of complying with very complex rules set by governments and organizational bureaucracies, such as complex tax codes and auction rules. Several decades of research both in controlled laboratory experiments and using field data call these traditional assumptions into question. Economists understand that there exist bounds on the complexity of tasks humans are able to perform, but it has proved difficult to formally define and measure complexity and put the notion to use in making predictions and setting policy. This proposal uses conceptual and empirical tools from economics, psychology and computer science to carefully experimentally measure what features of rules make them complex in terms of difficulty and personal burden for humans to perform. This corpus of data will improve our ability to predict human behavior when faced with complexity, and allow us to develop effective, streamlined policies that realistically account for human capacities to cope with complexity.Both internal decision procedures and external rules can be modeled as algorithms, implemented not by machines but by humans. For instance, strategies in repeated games, and procedures in many decision problems, can be described as simple formal models of algorithms from theoretical computer science. This research consists of a series of experiments designed to evaluate various ways of classifying the algorithms underlying procedures and rules as more or less complex for humans, and to understand which measures of complexity accurately predict and describe human mistakes and subjective costs. The proposal considers distinct ways of modeling the complexity of algorithms and examines how well they predict subjects’ difficulty with implementing decision rules. The goal of the research is to develop an empirically grounded characterization of the determinants of procedural complexity for humans, which will be useful for building predictive models of human behavior, and for designing more effective policies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.