| Abstract: |
This paper theoretically analyzes fake reviews on a platform market using
models where a seller creates fake reviews through incentivized transactions,
and its sales depend on its rating based on a review history. The platform can
control the incentive for fake reviews by changing the parameters of the
rating system, such as weights placed on old and new reviews and its filtering
policy. At equilibrium, the number of fake reviews increases as quality
increases but decreases as reputation improves. Since fake reviews have a
positive relationship with a product’s underlying quality, rational consumers
find a rating more informative when fake reviews exist, while credulous
consumers suffer from a bias caused by boosted reputation. A stringent
filtering policy can decrease the expected amount of fake reviews and the bias
of credulous consumers, but at the same time, it can decrease the
informativeness of a rating system for rational consumers. In terms of the
weight placed on the review history, rational consumers benefit from higher
weights on past reviews than from optimal weights without fake reviews. |