Plant-and-Steal: Truthful Fair Allocations via Predictions
Authors: Ilan Cohen, Alon Eden, Talya Eden, Arsen Vasilyan
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we give experiments which illustrate the role of different components of our framework for two players under various noise levels of the predictions. |
| Researcher Affiliation | Academia | Ilan Reuven Cohen Bar-Ilan University ilan-reuven.cohen@biu.ac.il Alon Eden The Hebrew University alon.eden@mail.huji.ac.il Talya Eden Bar-Ilan University talyaa01@gmail.com Arsen Vasilyan UC Berkeley arsen@berkeley.edu |
| Pseudocode | Yes | MECHANISM 1: Two agent Plant-and-Steal Framework |
| Open Source Code | Yes | The experiments, reproducible via Matlab (2022b) at https://tinyurl.com/Plant Steal Experiments, were performed on a standard PC (Intel i9, 32GB RAM) in about 30 minutes. |
| Open Datasets | No | We consider two-player scenarios with m = 100 items. For each distance measure, we generate 1000 valuation profiles. For each pair of valuation profiles and corresponding Kendall tau distance, we generate 100 predictions based on the distance. To generate interesting valuations for the players, we use a multi-step function to generate item values... |
| Dataset Splits | No | The paper describes synthetic data generation and evaluating the performance of algorithms, but does not explicitly mention train/validation/test splits in the context of model training. |
| Hardware Specification | Yes | The experiments, reproducible via Matlab (2022b) at https://tinyurl.com/Plant Steal Experiments, were performed on a standard PC (Intel i9, 32GB RAM) in about 30 minutes. |
| Software Dependencies | Yes | The experiments, reproducible via Matlab (2022b) at https://tinyurl.com/Plant Steal Experiments, were performed on a standard PC (Intel i9, 32GB RAM) in about 30 minutes. |
| Experiment Setup | Yes | We consider two-player scenarios with m = 100 items. For each distance measure, we generate 1000 valuation profiles. For each pair of valuation profiles and corresponding Kendall tau distance, we generate 100 predictions based on the distance. To generate interesting valuations for the players, we use a multi-step function to generate item values, since if the values are close together, any balanced partition obtains good MMS guarantees, without considering reports and predictions. Specifically, we consider a four-step (High/Med/Low/Extra-Low) random valuation function, where an item has a High valuation with probability 8/m, a Medium valuation with probability 1/4, a Low valuation with probability 1/2 and an Extra-Low valuation with the remaining probability. A High valuation is drawn from U[1000, 2000], a Medium valuation is drawn from U[400, 800], a Low valuations is drawn from U[100, 200] and an Extra-Low valuation is sampled from U[1, 2]. |