Efficiency of Ad Auctions with Price Displaying
Authors: Matteo Castiglioni, Diodato Ferraioli, Nicola Gatti, Alberto Marchesi, Giulia Romano4933-4940
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that, in such settings, social welfare maximization can be achieved by means of a direct-revelation mechanism that jointly optimizes, in polynomial time, the ads allocation and the advertisers prices to be displayed with them. However, in practice it is unlikely that advertisers allow the mechanism to choose prices on their behalf. Indeed, in commonly-adopted mechanisms, ads allocation and price optimization are decoupled, so that the advertisers optimize prices and bids, while the mechanism does so for the allocation, once prices and bids are given. We investigate how this decoupling affects the efficiency of mechanisms. In particular, we study the Price of Anarchy (Po A) and the Price of Stability (Po S) of indirect-revelation mechanisms with both VCG and GSP payments, showing that the Po S for the revenue may be unbounded even with two slots, and the Po A for the social welfare may be as large as the number of slots. Nevertheless, we show that, under some assumptions, simple modifications to the indirect-revelation mechanism with VCG payments achieve a Po S of 1 for the revenue. |
| Researcher Affiliation | Academia | Matteo Castiglioni1, Diodato Ferraioli2, Nicola Gatti1, Alberto Marchesi1, Giulia Romano1* 1Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, Italy 2Universit a degli Studi di Salerno, Via Giovanni Paolo II, Fisciano, Italy |
| Pseudocode | No | The paper describes algorithms and mechanisms in prose but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing open-source code for its methodology. |
| Open Datasets | No | The paper is theoretical and does not use or describe any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not use or describe any datasets, thus no validation split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on computational experiments, so no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe software implementations or dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |