观察学习
代表性启发
启发式
心理学
心理干预
借记
社会学习
建议(编程)
认知心理学
社会心理学
人工智能
观察研究
应用心理学
机器学习
计算机科学
数学教育
体验式学习
教育学
统计
程序设计语言
数学
精神科
操作系统
作者
Haewon Yoon,Irene Scopelliti,Carey K. Morewedge
标识
DOI:10.1016/j.obhdp.2020.10.011
摘要
Abstract Observational learning can debias judgment and decision making. One-shot observational learning-based training interventions (akin to “hot seating”) can produce reductions in cognitive biases in the laboratory (i.e., anchoring, representativeness, and social projection), and successfully teach a decision rule that increases advice taking in a weight on advice paradigm (i.e., the averaging principle). These interventions improve judgment, rule learning, and advice taking more than practice. We find observational learning-based interventions can be as effective as information-based interventions. Their effects are additive for advice taking, and for accuracy when advice is algorithmically optimized. As found in the organizational learning literature, explicit knowledge transferred through information appears to reduce the stickiness of tacit knowledge transferred through observational learning. Moreover, observational learning appears to be a unique debiasing training strategy, an addition to the four proposed by Fischhoff (1982). We also report new scales measuring individual differences in anchoring, representativeness heuristics, and social projection.
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