Identifying Perverse Incentives in Buyer Profiling on Online Trading Platforms
研究在线交易平台是否有激励准确刻画消费者画像，发现按交易收费的平台有此激励，但按发现收费的平台存在偏离准确画像的不正当激励。
With advance machine learning and artificial intelligence models, the capability of online trading platforms to profile consumers to identify and understand their needs has substantially increased. In this study, we use an analytical model to study whether these platforms have an incentive to profile their customers as accurately as possible. We find that “payments-for-transactions” platforms (i.e., platforms that charge for transactions that occur on the platform) indeed have such incentives to accurately profile the customers. However, surprisingly, “payments-for-discoveries” platform (i.e., platforms that charge customers for discoveries) have a perverse incentive to deviate from accurate consumer profiling. Our study provides insights into underlying mechanisms that drive this perverse incentive and discuss circumstances that lead to such a perverse incentive.