报告详情
Information Asymmetry as Computable Risk: An Interpretable Index-Based Early Warning System for Detecting Misrepresentation in E-Commerce Marketplaces
编号：46
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更新：2026-11-10 16:30:32 浏览：7次
Online
报告开始：2026年11月21日 15:15（Asia/Kolkata）
报告时间：15min
所在会场：[S4] Artificial Intelligence and Intelligent Computing [S4-10] Artificial Intelligence and Intelligent Computing
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摘要
Online marketplaces observe information asymmetry through its symptoms, such as fake reviews and complaints, after consumers have been harmed, and no listing-level, computable measure of asymmetry has been evaluated as a leading indicator of harm. This study creates an Information Asymmetry Index (IAI) that combines four pillars of the theory, based on signals that can be seen before a snapshot (disclosure deficit, claim-experience divergence, rating-distribution distortion and authenticity risk) and translates the index into alerts using a threshold that is derived from training. Evaluation in a seeded Monte Carlo marketplace of 6,000 listings per replication, 20 main replications, 10 signal-decoupling controls, seller-disjoint training and test sets, two snapshots and a 90-day outcome horizon. For listings not yet in a complaint surge, the IAI attains an area under the precision–recall curve (AUC-PR) of 0.435 [0.422, 0.448], against 0.187 [0.179, 0.195] for a rating-and-volume logistic baseline, a paired gain of 0.248 (p < 0.001). If alerts are set at the 97th percentile, alerts will be 0.675 accurate at a 0.012 false positive rate. Top-decile alerts overlap by at least 0.874 across equal, principal-component and entropy weights, and the gain drops to 0.001 [−0.006, 0.009] when observable signals are removed from the analysis, suggesting that the gain is due to the asymmetry–signal connection and not to pipeline leakage. The results demonstrate that information asymmetry can be made measurable and auditable in the form of a forward-looking risk score and present a test bed for field validation that can be reproduced.
关键词
Composite index,E-commerce platforms,Early warning system,Information asymmetry,Monte Carlo simulation,Online reviews,Predictive analytics,Product uncertainty
报告人
Kamaludeen P
Professor Christ College of Engineering and Technology； Affiliated to Pondicherry University稿件作者
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重要日期
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会议日期
11月20日
2026
至11月22日
2026
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10月31日 2026
初稿截稿日期
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11月07日 2026
提前注册日期
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11月22日 2026
注册截止日期
主办单位
United Societies of Science
承办单位
Pondicherry University
协办单位
IEEE Section
IEEE Madras Section
IEEE Madras Section
联系方式
- ICon INDIA 2026
- ic******@usssociety.org
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