Simultaneous Cake Cutting
Authors: Eric Balkanski, Simina Brânzei, David Kurokawa, Ariel Procaccia
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The theory of fair division provides formal notions of fairness, and mechanisms for computing outcomes that achieve these notions. ... Cake cutting is largely an algorithmic endeavor, but only recently have computer scientists started to weigh in. ... In this paper, we introduce a novel computational model that, we believe, provides a fundamentally new perspective on cake cutting; we call it the simultaneous model. |
| Researcher Affiliation | Academia | Eric Balkanski Carnegie Mellon University, USA ebalkans@andrew.cmu.edu Simina Brˆanzei Aarhus University, Denmark simina@cs.au.dk David Kurokawa Carnegie Mellon University, USA dkurokaw@cs.cmu.edu Ariel D. Procaccia Carnegie Mellon University, USA arielpro@cs.cmu.edu |
| Pseudocode | No | The paper describes protocols verbally and mathematically (e.g., 'Consider the following simultaneous protocol: Map the valuation function...'), but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code for the described methodology or links to code repositories. |
| Open Datasets | No | The paper is theoretical and does not discuss empirical studies involving datasets, training, or public dataset availability. |
| Dataset Splits | No | The paper is theoretical and does not mention any validation dataset splits or cross-validation methods. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |