Identifying Perverse Incentives in Buyer Profiling on Online Trading Platforms

Karthik N. Kannan, Rajib Saha, Warut Khern-am-nuai
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引用次数: 3

Abstract

Consumer profiling has become one of the most common practices on online trading platforms. Many platforms strive to obtain and implement technological innovations that allow them to understand and identify consumers’ needs, and, thereafter, monetize this capability by charging sellers to present and/ or sell their products or services based on consumers’ interests. However, an interesting and relevant question arises in this context: Does the platform have an incentive to profile its buyers as accurately as possible? This paper develops and analyzes a parsimonious game-theoretic model to answer this research question. We find that, surprisingly, platforms that charge sellers for discoveries have a perverse incentive to deviate from accurate buyer profiling. However, such a perverse incentive does not exist for platforms that charge sellers for transactions. As a result, with such a perverse incentive, social welfare under discovery-based pricing is lower than that under transaction-based pricing.
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识别在线交易平台上买方画像中的不正当动机
消费者分析已经成为在线交易平台上最常见的做法之一。许多平台努力获得和实施技术创新,使他们能够理解和识别消费者的需求,然后,通过向卖家收费,根据消费者的兴趣展示和/或销售他们的产品或服务,从而将这种能力货币化。然而,在这种情况下,一个有趣且相关的问题出现了:该平台是否有动机尽可能准确地描述其买家?本文发展并分析了一个简约的博弈论模型来回答这一研究问题。我们发现,令人惊讶的是,向卖家收取发现费用的平台有一种偏离准确买家描述的反常动机。然而,对于向卖家收取交易费用的平台来说,这种不正当的激励并不存在。因此,在这种反常的激励下,基于发现定价的社会福利低于基于交易定价的社会福利。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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