Pricing Problems with Buyer Preselection
Authors: Vittorio Bilò, Michele Flammini, Gianpiero Monaco, Luca Moscardelli
JAIR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | When insisting on market envy-freeness, we prove that the problem cannot be approximated within n1 ε (with n being the number of buyers) for any ε > 0, under both objective functions; we also provide approximation algorithms with an approximation ratio tight up to subpolynomial multiplicative factors for social welfare and the seller s revenue. The negative result, in particular, holds even for markets with single-minded buyers. We also prove that maximizing the seller s revenue is NP-hard even for a single buyer, thus closing a previous open question. |
| Researcher Affiliation | Academia | Vittorio Bil o EMAIL University of Salento, Italy. Michele Flammini EMAIL GSSI, L Aquila, Italy. Gianpiero Monaco EMAIL University of L Aquila, Italy. Luca Moscardelli EMAIL University of Chieti-Pescara, Italy. |
| Pseudocode | No | The paper describes algorithms in prose and through mathematical notation, such as in the proof of Lemma 7: 'Consider algorithm Best which, given Γ and k, calls Ak on all O(nk) possible subsets of k buyers and returns the solution, let us call it o Best, with the largest objective value.' However, there are no explicitly labeled pseudocode or algorithm blocks with structured steps. |
| Open Source Code | No | The paper does not contain any statements about releasing source code, nor does it provide links to any code repositories. |
| Open Datasets | No | The paper presents theoretical work on pricing problems and does not use or mention any specific datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe any experimental setup involving dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical analysis and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or version numbers. |
| Experiment Setup | No | The paper is theoretical and does not contain any details about experimental setup, hyperparameters, or training configurations. |