Abstract of the Paper: This paper builds a model of online behavioral manipulation. Our approach is motivated by advances in AI that are massively expanding the amount of information platforms have about users. In our model, platforms dynamically offer one of n products and an associated price to a user who is uncertain about and can slowly learn the quality of the products. The signals about product quality, however, also depend on extraneous factors, such as the appearance of the good or other attributes that make it appear more attractive than it is, at least in the short run — what we refer to as “glossiness”. AI tools enable platforms to better estimate the glossiness of products and enable them to engage in behavioral manipulation. Formally, we study a continuous-time experimentation problem in which the platform has different beliefs and guides the experimentation choices of the user. Our analysis provides three main results. First, when glossiness is absent or short-lived, the superior information of the platform benefits consumers, thus validating the default position in the literature that AI can help users and consumers. Second, in sharp contrast, when glossiness is long-lived, superior information of the platform makes users worse off because it enables behavioral manipulation — the platform systematically distorts user choices towards glossy products. Third, as the number of products increases, behavioral manipulation intensifies, and the user is more likely to be confronted with low-quality, glossy products.
https://doi.org/10.3386/w31872
E (cid:2)postAI({i, i}n E (cid:2)U postAI({i, i}n E (cid:2)W postAI({i, i}n i=1)(cid:3) > E (cid:2)preAI({i}n i=1)(cid:3) > E (cid:2)U preAI({i}n i=1)(cid:3) > E (cid:2)W preAI({i}n i=1)(cid:3) i=1)(cid:3) i=1)(cid:3) . 16 The informational advantage of the platform always increases its own prots, as it enables the platform to modify the users behavior. Theorem 3 establishes that this informational advantage also increases the expected users utility and welfare for large enough . This theorem conrms what might be viewed as the conventional wisdom in the literature: more data enables better allocation of products and therefore benets the users and society as a whole. This theorem follows from the fact that for large enough , the helpfulness effect, characterized in Proposition 2, dominates the manipulation effect of Proposition 1. In the next subsection, we will see that this intuition does not hold in general and the platforms information advantage may harm users.
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6.2 When Behavioral Manipulation Harms Users Here, we show that in the post-AI environment, the users utility decreases because of behavioral manipulation since the manipulation effect dominates the helpfulness effect. Theorem 4. Suppose the initial beliefs {i}n such that for l we have i=1 are i.i.d. and uniform over [0, 1]. For any r, , , there exists l E (cid:2)postAI({i, i}n E (cid:2)U postAI({i, i}n E (cid:2)W postAI({i, i}n i=1)(cid:3) > E (cid:2)preAI({i}n i=1)(cid:3) < E (cid:2)U preAI({i}n i=1)(cid:3) < E (cid:2)W preAI({i}n i=1)(cid:3) i=1)(cid:3) i=1)(cid:3) . In this case with low , the platforms informational advantage enables it to engage in behavioral manipulation: the user is pushed towards products with = 1 (or more accurately, towards products that have initial glossiness state i,0 = 1). Because these products do not generate bad news in the short run, the users belief will become more positive for a while, and this will enable the platform to charge
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However, because glossy products are low quality, this behavioral manipulation is bad for user utility and utilitarian welfare. That is small here is important. As we saw, when is large, the platform expects the glossiness of the production to wear off quickly, and thus it is not worthwhile to push the user towards glossy products. But from a welfare point of view, it is more costly to have users consume glossy products when is small, because they will not discover for quite a while that the product is actually not high-quality. It is this feature of behavioral manipulation that reduces user utility and utilitarian welfare.
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6.3 Big Data Double Whammy: More Products Negatively Impact User Welfare The availability of big data provides platforms with valuable insights into predictable patterns of user behavior, which can be leveraged for behavioral manipulation, as we have established thus far. Moreover, the same advances in AI also enable digital platforms to expand the range of products and services they offer. Next, we demonstrate that this combination of greater choice and more platform information 17 may be particularly pernicious as the number of products increases, the potential for behavioral manipulation increase as well. This result highlights that multiple aspects of the new capabilities of digital platforms closely interact in affecting user welfare. Theorem 5. Suppose the initial beliefs {i}n+1 exist and l such that for , l, and n (cid:100)1 i=1 are i.i.d. and uniform over [ 1 2 , 1 2 + ]. For any r, , , there log(2)/(1) (cid:101) in the post-AI environment we have:
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