.png){kind=icon}
返回
Misattribution prevents learning
.png){kind=icon}
OA
AI
DOI：10.1111/ajae.12466.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In many markets, consumers believe things about products that are not true. We study how incorrect beliefs about product quality can persist even after a consumer has used a product many times. We explore the example of fertilizer in East Africa. Farmers believe much local fertilizer is counterfeit or adulterated; however, multiple studies have established that nearly all fertilizer in the area is good quality. We develop a learning model to explain how these incorrect beliefs persist. We show that when the distributions of outcomes using good and bad quality products overlap, agents can misattribute bad luck or bad management to bad quality. Our learning model and its simulations show that the presence of misattribution inhibits learning about quality and that goods like fertilizer with unobservable quality that are inputs into production processes characterized by stochasticity should be thought of as credence goods, not experience goods. Our results suggest that policy makers should pursue quality assurance programs for products that are vulnerable to misattribution.
Keyword:
beliefs
East Africa
fertilizer
input quality
learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
6.6K
被引数:
8.9K
机构
引用论文
Logic-tree Approach for Probabilistic Tsunami Hazard Analysis and its Applications to the Japanese Coasts概率海啸危险分析的逻辑树方法及其在日本海岸的应用
PREAI
分享收藏Preparation of Nanofibers Mats Derived from Task-Specific Polymeric Ionic Liquid for Sensing and Catalytic Applications.png)
PolymersOAAI
PolymersIF0
OAAI分享收藏Advanced Digital 3D Technology in the Combined Surgery-First Orthognathic and Clear Aligner Orthodontic Therapy for Dentofacial Deformity TreatmentProcesses
OAAI
ProcessesIF0
OAAI分享收藏
查看更多内容