Learning Strong Substitutes Demand via Queries

Abstract

This paper addresses the computational challenges of learning strong substitutes demand when given access to a demand (or valuation) oracle. Strong substitutes demand generalises the well-studied gross substitutes demand to a multi-unit setting. Recent work by Baldwin and Klemperer shows that any such demand can be expressed in a natural way as a finite collection of weighted bid vectors. A simplified version of this bidding language has been used by the Bank of England.Assuming access to a demand oracle, we provide an algorithm that computes the unique list of bids corresponding to a bidder's demand preferences. In the special case where their demand can be expressed using positive bids only, we have an efficient algorithm that learns the bids in linear time. We also show super-polynomial lower bounds on the query complexity of computing the unique list of bids in the general case where bids may be positive and negative. Our algorithms constitute the first systematic approach for bidders to construct a bid list corresponding to non-trivial demand, allowing them to participate in 'product-mix' auctions.

Optimally Deceiving a Learning Leader in Stackelberg Games

“…Finally, learning via incentive queries in games is directly related to the theory of preference elicitation, where the goal is to mine information about the private parameters of the agents by interacting with them [Blum et al, 2004, Lahaie and Parkes, 2004, Zinkevich et al, 2003, Goldberg et al, 2020.…”