https://macropaperwarehouse.com/authors/nicholas-wu/Nicholas WuHugo -- gohugo.ioen-ushttps://macropaperwarehouse.com/papers/from-doubt-to-devotion-trials-and-learning-based-pricing/https://macropaperwarehouse.com/papers/from-doubt-to-devotion-trials-and-learning-based-pricing/<p>This paper studies a dynamic mechanism design problem in which an informed seller sells an experience good to a skeptical buyer who learns about the product through consumption. The central question is: how does a seller leverage proprietary data about product-buyer match quality together with the buyer&rsquo;s ability to learn, and what are the welfare implications in equilibrium?</p> <p>The model features a seller who privately observes a binary match quality (theta in {H, L}) between their service and the buyer. The buyer does not observe match quality and has an initially unknown private value v for the good, drawn from a Myerson-regular distribution F with support [v_low, v_high] and normalized mean E[v] = 1. If the match is high, the buyer receives instantaneous utility rewards according to a Poisson process with flow rate lambda*I, where I in [0,1] is the seller-controlled access level. Upon receiving the first reward, the buyer perfectly learns both match quality theta and their own value v. The seller commits to a dynamic mechanism over time horizon T = [0, T] specifying access and prices conditional on reported histories. Both parties are risk-neutral and there is no discounting in the baseline.</p>