探索对话智能体作为测量决策中认知偏差的有效工具
Exploring Conversational Agents as an Effective Tool for Measuring Cognitive Biases in Decision-Making
AI总结：
本文研究利用对话智能体自动测量决策中的认知偏差，提出融合现有实验设计的偏差测量机制，并通过初步实验验证其在测量框架效应和损失厌恶偏差上的有效性。
摘要：
启发式与认知偏差是人类决策的组成部分。自动检测特定认知偏差可使智能工具提供更好的决策支持。目前检测认知偏差的存在需要手工设计的实验和人工解释。我们的研究旨在探索对话智能体作为在不同领域测量各种认知偏差的有效工具。我们提出的对话智能体包含一个偏差测量机制，该机制借鉴了现有实验设计和文献中确定的各种实验任务。我们用于测量框架效应和损失厌恶偏差的初步实验表明，对话智能体可以被有效地用于测量这些偏差。
英文摘要：
Heuristics and cognitive biases are an integral part of human decision-making. Automatically detecting a particular cognitive bias could enable intelligent tools to provide better decision-support. Detecting the presence of a cognitive bias currently requires a hand-crafted experiment and human interpretation. Our research aims to explore conversational agents as an effective tool to measure various cognitive biases in different domains. Our proposed conversational agent incorporates a bias measurement mechanism that is informed by the existing experimental designs and various experimental tasks identified in the literature. Our initial experiments to measure framing and loss-aversion biases indicate that the conversational agents can be effectively used to measure the biases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
