Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Simultaneous 2nd Price Item Auctions with No-Underbidding
Authors: Michal Feldman, Galia Shabtai5391-5398
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the price of anarchy (Po A) of simultaneous 2nd price auctions (S2PA) under a new natural condition of no underbidding, meaning that agents never bid on items less than their marginal values. We establish improved (mostly tight) bounds on the Po A of S2PA under no underbidding for different valuation classes (including unit-demand, submodular, XOS, subadditive, and general monotone valuations), in both full-information and incomplete information settings. To derive our results, we introduce a new parameterized property of auctions, termed (γ, δ)-revenue guaranteed, which implies a Po A of at least γ/(1+δ). Via extension theorems, this guarantee extends to coarse correlated equilibria (CCE) in full information settings, and to Bayesian Po A (BPo A) in settings with incomplete information and arbitrary (correlated) distributions. We then show that S2PA are (1, 1)-revenue guaranteed with respect to bids satisfying no underbidding. This implies a Po A of at least 1/2 for general monotone valuation, which extends to BPOA with arbitrary correlated distributions. Moreover, we show that (λ, µ)-smoothness combined with (γ, δ)-revenue guaranteed guarantees a Po A of at least (γ + λ)/(1 + δ + µ). This implies a host of results, such as a tight Po A of 2/3 for S2PA with submodular (or XOS) valuations, under no overbidding and no underbidding. |
| Researcher Affiliation | Academia | Michal Feldman and Galia Shabtai Tel Aviv University EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: "*For the full version, see https://arxiv.org/abs/2003.11857 (Feldman and Shabtai 2020)." This link refers to the full version of the paper itself, not source code for the methodology. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe data splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe running experiments, so no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe running experiments, so no software dependencies are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup or training configurations. |