发帖
雷达卡
摘要翻译：
本文提出了一种分析人类决策的新方法，包括比较职业棋手的行为相对于认知有限理性的计算基准。该基准是使用现代国际象棋引擎的算法构建的，并允许在单个移动一个一个移动的观察水平上调查行为，因此代表了计算有界优化的自然基准。该分析提供了新颖的见解，通过隔离偏离这一基准的有限理性，以及他们的原因和后果的表现。这些发现证明了行为偏差的几个不同维度的存在，这些维度与损失和收益、时间压力、疲劳和复杂性方面的不对称位置评估有关。结果还表明，偏离基准并不一定会导致业绩恶化。更快的决策与更频繁地偏离基准相关联，但它们也与更好的性能相关联。这些发现与直觉和经验的重要影响是一致的，从而为最近关于认知过程中计算合理性的辩论提供了新的线索。
---
英文标题：
《Decisions and Performance Under Bounded Rationality: A Computational
Benchmarking Approach》
---
作者：
Dainis Zegners, Uwe Sunde, Anthony Strittmatter
---
最新提交年份：
2020
---
分类信息：
一级分类：Economics 经济学
二级分类：General Economics 一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
--
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--
一级分类：Quantitative Finance 数量金融学
二级分类：Economics 经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
--
---
英文摘要：
This paper presents a novel approach to analyze human decision-making that involves comparing the behavior of professional chess players relative to a computational benchmark of cognitively bounded rationality. This benchmark is constructed using algorithms of modern chess engines and allows investigating behavior at the level of individual move-by-move observations, thus representing a natural benchmark for computationally bounded optimization. The analysis delivers novel insights by isolating deviations from this benchmark of bounded rationality as well as their causes and consequences for performance. The findings document the existence of several distinct dimensions of behavioral deviations, which are related to asymmetric positional evaluation in terms of losses and gains, time pressure, fatigue, and complexity. The results also document that deviations from the benchmark do not necessarily entail worse performance. Faster decisions are associated with more frequent deviations from the benchmark, yet they are also associated with better performance. The findings are consistent with an important influence of intuition and experience, thereby shedding new light on the recent debate about computational rationality in cognitive processes.
---
PDF链接：
https://arxiv.org/pdf/2005.12638
京公网安备 11010802022788号
