Public Signaling in Bayesian Ad Auctions
Authors: Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi, Giulia Romano, Nicola Gatti
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study signaling in Bayesian ad auctions, in which bidders valuations depend on a random, unknown state of nature... We study the problem of computing how the mechanism should send signals to bidders in order to maximize revenue... we start with a negative result, showing that, in general, the problem does not admit a PTAS unless P = NP... The rest of the paper is devoted to settings in which such negative result can be circumvented. First, we prove that, with known valuations, the problem can indeed be solved in polynomial time... Moreover, in the same setting, we provide an FPTAS... In this case, we show that the problem admits an FPTAS, a PTAS, and a QPTAS... |
| Researcher Affiliation | Academia | Francesco Bacchiocchi , Matteo Castiglioni , Alberto Marchesi , Giulia Romano and Nicola Gatti Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133, Milan, Italy |
| Pseudocode | No | The paper describes algorithms such as dynamic programming and the ellipsoid method, but it does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. Footnote 1 refers to a companion technical report, not a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on datasets. Therefore, no information regarding publicly available datasets for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation. Therefore, no information regarding dataset splits for validation is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments, thus no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no specific experimental setup details like hyperparameters or training configurations are provided. |