Market Algorithms for Autobidding
summary
Autobidding is framed as an LP: items with known prices, a budget and TCPA constraint, and a practical algorithm solve dual on a sample that yields two dual prices (alpha, beta) used to decide per-item bids; under second‑price auctions bidding the threshold buys the right set. When all bidders use autobidders equilibria exist under continuity and small‑item assumptions and welfare loss is bounded (price of anarchy = 2 for VCG). For value‑maximizers, surprising effects appear: reserve raise can increase welfare and multi‑channel competition flips whether first‑ or second‑price is optimal.
AI-written summary · about
chapters (6)
- 0:00Introduction
- 0:40Outline
- 3:15Problem
- 9:31Auction Design
- 11:44Reserve Prices
- 13:56MultiChannel Bidding
related talks
43mLearning about Auctions
01h 07mMulti-Dimensional and Non-Linear Mechanism Design and Approximation I
40mOnline Ad Allocation: Robust Optimization for Repeated Auctions
48mOutposts Between Worst- and Average Case Analysis: A Case Study in Auction Design
44mOffer Optimization, a Dual Approach
32mLearning Reserve Prices in Second-Price Auctions
48mRobust Inference for Games via Theoretical Guarantees
09mMechanism Design Via Machine Learning: Overfitting, Incentives, and Privacy