Working Paper: NBER ID: w31872
Authors: Daron Acemoglu; Ali Makhdoumi; Azarakhsh Malekian; Asuman Ozdaglar
Abstract: We build a model of online behavioral manipulation driven by AI advances. A platform dynamically offers one of n products to a user who slowly learns product quality. User learning depends on a product’s “glossiness,’ which captures attributes that make products appear more attractive than they are. AI tools enable platforms to learn glossiness and engage in behavioral manipulation. We establish that AI benefits consumers when glossiness is short-lived. In contrast, when glossiness is long-lived, users suffer because of behavioral manipulation. Finally, as the number of products increases, the platform can intensify behavioral manipulation by presenting more low-quality, glossy products.
Keywords: Behavioral Manipulation; AI Advances; Consumer Welfare; Product Quality
JEL Codes: D83; D90; D91; L86
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| short-lived glossiness (Y60) | higher expected user utility (D11) |
| short-lived glossiness (Y60) | higher consumer welfare (D69) |
| long-lived glossiness (L15) | lower expected user utility (D11) |
| long-lived glossiness (L15) | lower consumer welfare (D69) |
| increased product variety (L15) | enhanced behavioral manipulation (E71) |
| enhanced behavioral manipulation (E71) | lower consumer welfare (D69) |
| manipulation effect dominates when glossiness is long-lived (L15) | negative impact on user welfare (D69) |