Research map / Bandits and online learning / Online convex optimization and games
Learning and Collusion in Multi-unit Auctions
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Represent candidate bid vectors as paths in a weighted DAG | Offline bidding optimization · Offline repeated multi-unit auction | computation time: polynomial time | Helped |
| Use multiplicative weights over DAG paths with implicit edge-based weight pushing | Repeated K-th and (K+1)-st price auctions · Full information | regret: O(v_{i,1}\sqrt{TK^3\log T}) | Helped |
| Use an unbiased edge-weight estimator from the selected path | Repeated K-th and (K+1)-st price auctions · Bandit feedback | regret: O(v_{i,1} \cdot \min \{ \sqrt[4]{T^3K^7\log T}, KT\}) | Helped |
| Allow other players to choose the bid sequence adversarially | Any player strategy in either auction variant · Full information and bandit feedback | expected regret lower bound: at least c \cdot v_{i,1} K \sqrt{T} | Hurt |
| Use the (K+1)-st price rather than the K-th price | Uniform-price auction · (K+1)-st price auction, n > K hungry players | price in core-stable profiles without transfers: p(\vec{b}) = 0 | Hurt |
| Use the (K+1)-st price rather than the K-th price | Uniform-price auction · (K+1)-st price auction, n > K hungry players | price in core-stable profiles with transfers: p(\vec{b}) = 0 | Hurt |
| Use the K-th price rather than the (K+1)-st price | Uniform-price auction · K-th price auction | existence of arbitrarily low-price pure Nash equilibrium | Helped |
| Allow transfers among bidders | Core of the (K+1)-st price auction · n > K hungry players | social welfare of core-stable allocations | Helped |
Closest papers by meaning
- Comparing Uniform Price and Discriminatory Multi-Unit Auctions through Regret Minimization (NeurIPS 2025 · similarity 0.89)
- Improved learning rates in multi-unit uniform price auctions (NeurIPS 2024 · similarity 0.89)
- Beyond Monotonicity: On the Convergence of Learning Algorithms in Standard Auction Games (AAAI 2025 · similarity 0.85)
- Optimizing the coalition gain in Online Auctions with Greedy Structured Bandits (NeurIPS 2024 · similarity 0.85)
- Increasing Revenue in Efficient Combinatorial Auctions by Learning to Generate Artificial Competition (AAAI 2025 · similarity 0.85)
- Learning Safe Strategies for Value Maximizing Buyers in Uniform Price Auctions (ICML 2025 · similarity 0.84)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.