Learning to bid and bundle: a posterior-sampling agent for online retail

arXiv cs.AINovember 3, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2611.00165v1 Announce Type: new Abstract: An online retailer launching a perishable product bids for sponsored-search clicks, discounts a bundle with an always-stocked staple and displays its remaining stock, which raises willingness to pay. It knows neither how clicks respond to its bid nor how strong and dispersed its market is, and learns both over repeated seasons from data its own decisions censor: a zero bid reveals nothing about paid clicks, and choices follow only clicks. We design a posterior-sampling agent for it. Each period it draws a market from an exact posterior and posts the bid and discount of that market's exactly solved season plan, so every action is one a fully inf

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