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..
Revenue Maximization Envy-Free Pricing for Homogeneous Resources
Authors: Gianpiero Monaco, Piotr Sankowski, Qiang Zhang
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide upper and lower bounds for item and bundle pricing for the two notions of envy-freeness. The results are summarized in Table 1. Although, the problems studied here are rather basic, some of them are quite challenging. The envy-freeness requires that, given an uniform price per item, each buyer gets the number of items that maximizes his utility. The main result is to solve the EFIP-MUL problem optimally via a dynamic programming for general valuations. |
| Researcher Affiliation | Academia | Gianpiero Monaco University of L Aquila, Italy EMAIL Piotr Sankowski University of Warsaw, Poland EMAIL Qiang Zhang University of Warsaw, Poland EMAIL |
| Pseudocode | Yes | Algorithm 1: A logarithmic approximation algorithm for general valuations in PEFIP-MUL. Algorithm 2: A O(log n) approximation algorithm for non-decreasing valuations. |
| Open Source Code | No | The information is insufficient. The paper does not provide any specific links or statements regarding the availability of its source code. |
| Open Datasets | No | The information is insufficient. The paper is theoretical and does not describe experiments involving datasets for training. |
| Dataset Splits | No | The information is insufficient. The paper is theoretical and does not describe experiments involving datasets or their splits. |
| Hardware Specification | No | The information is insufficient. The paper is theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The information is insufficient. The paper is theoretical and does not provide specific software dependencies or version numbers for experimental replication. |
| Experiment Setup | No | The information is insufficient. The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |