Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Fair Rent Division on a Budget
Authors: Ariel Procaccia, Rodrigo Velez, Dingli Yu
AAAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 18,294 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,972 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | By contrast, we design a polynomial-time algorithm that takes budget constraints as part of its input; it determines whether there exist envy-free allocations that satisfy the budget constraints, and, if so, computes one that optimizes an additional criterion of justice.In Section 3, we construct a polynomial-time (LP-based) algorithm that computes an optimal allocation with respect to a given (linear) criterion of justice, subject to the envy-freeness constraints and the given budget constraints, when a feasible allocation exists. |
| Researcher Affiliation | Academia | Ariel D. Procaccia Computer Science Department Carnegie Mellon University Rodrigo A. Velez Department of Economics Texas A&M University Dingli Yu Institute for Interdisciplinary Information Sciences Tsinghua University |
| Pseudocode | Yes | Algorithm 1: Maximum-rent envy-free allocation in a fully connected economy.Algorithm 2: Optimal envy-free allocation subject to budget constraints. |
| Open Source Code | No | The paper does not provide a link to the source code for the methodology described in this paper. It mentions Spliddit.org as an application but not its own code. |
| Open Datasets | No | This paper is theoretical and does not use datasets for training or evaluation. |
| Dataset Splits | No | This paper is theoretical and does not involve dataset splits for validation. |
| Hardware Specification | No | This paper is theoretical and does not describe experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not specify software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |