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Fast and Slow Learning from Reviews
September 12 @ 4:30 pm - 5:30 pm
Speaker: Daron Acemoglu (MIT)
Many online platforms present summaries of reviews by previous users. Even though such reviews could be useful, previous users leaving reviews are typically a selected sample of those who have purchased the good in question, and may consequently have a biased assessment. In this paper, we construct a simple model of dynamic Bayesian learning and profit-maximizing behavior of online platforms to investigate whether such review systems can successfully aggregate past information and the incentives of the online platform to choose the relevant features of the review system.
On the consumer side, we assume that each individual cares about the underlying quality of the good in question, but in addition has heterogeneous ex ante and ex post preferences (meaning that she has a different strength of preference for the good in question than other users, and her enjoyment conditional on purchase is also a random variable). After purchasing a good, depending on how much they have enjoyed it, users can decide to leave a positive or a negative review (or leave no review if they do not have strong preferences). New users observe a summary statistic of past reviews (such as fraction of all reviews that are positive or fraction of all users that have left positive review etc.). Our first major result shows that, even though reviews come from a selected sample of users, Bayesian learning ensures that as the number of potential users grows, the assessment of the underlying state converges almost surely to the true quality of the good. More importantly, we provide a tight characterization of the speed of learning (which is a contribution relative to most of the works in this area that focus on whether there is learning or not).
Under the assumption that the online platform receives a constant revenue from every user that purchases (because of commissions from sellers or from advertising revenues), we then show that, in any Bayesian equilibrium, the profits of the online platform are a function of the speed of learning of users. Using this result, we study the design of the review system by the online platform, and show the possibility of both fast and slow learning from reviews.
Authors: Daron Acemoglu, Ali Makhdoumi, Azarakhsh Malekian and Asu Ozdadaglar.
Daron Acemoglu is the Elizabeth and James Killian Professor of Economics at MIT. In 2005 he received the John Bates Clark Medal awarded to economists under forty judged to have made the most significant contribution to economic thought and knowledge. Among many other awards, in 2017 he was given an Honorary Doctorate (Bath University), Great Immigrant List of the Carnegie foundations, BBVA Frontiers of Knowledge Award in Economics and a Carnegie Fellow.