Signaling in Posted Price Auctions
Authors: Matteo Castiglioni, Giulia Romano, Alberto Marchesi, Nicola Gatti4941-4948
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As a first step, we prove that, in both settings, the problem of maximizing the seller s revenue does not admit an FPTAS unless P = NP, even for basic instances with a single buyer. As a result, in the rest of the paper, we focus on designing PTASs. In order to do so, we first introduce a unifying framework encompassing both public and private signaling, whose core result is a decomposition lemma that allows focusing on a finite set of possible buyers posteriors. This forms the basis on which our PTASs are developed. In particular, in the public signaling setting, our PTAS employs some ad hoc techniques based on linear programming, while our PTAS for the private setting relies on the ellipsoid method to solve an exponentially-sized LP in polynomial time. |
| Researcher Affiliation | Academia | Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133, Milan, Italy {matteo.castiglioni, giulia.romano, alberto.marchesi, nicola.gatti}@polimi.it |
| Pseudocode | Yes | Algorithm 1: FIND-APX-PRICES |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper does not specify the use of a public or open dataset for training, nor does it provide concrete access information for any dataset. It's a theoretical paper. |
| Dataset Splits | No | The paper does not provide specific details about training/validation/test dataset splits. This is a theoretical paper. |
| Hardware Specification | No | The paper does not describe any specific hardware used for running experiments. This is a theoretical paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameters or system-level training settings. This is a theoretical paper focused on algorithm design and proofs. |