Subsidy Allocations in the Presence of Income Shocks
Authors: Rediet Abebe, Jon Kleinberg, S. Matthew Weinberg7032-7039
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present optimal and near-optimal algorithms for various general settings. We develop a stylized model for the state of an agent (representing a family potentially in need of assistance) as they experience shocks over time. We then use this model to formulate the problem of allocating subsidies to agents... |
| Researcher Affiliation | Academia | Rediet Abebe,1,2 Jon Kleinberg,2 S. Matthew Weinberg3 1Harvard University 2Cornell University 3Princeton University |
| Pseudocode | No | The paper describes algorithms like the 'priority algorithm', 'binary search', and 'Dynamic Program' verbally, explaining their steps and properties, but it does not present them in a structured pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper primarily presents a theoretical model and algorithmic analysis rather than conducting empirical studies on a specific dataset. It references external programs like 'Poverty Tracker' for context but does not use them as a dataset for its own experiments. |
| Dataset Splits | No | The paper describes theoretical algorithms and models, and therefore does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper describes theoretical models and algorithms and does not specify any hardware used for experiments or computations. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical models and algorithms, thus it does not list specific software dependencies with version numbers required for replication. |
| Experiment Setup | No | The paper focuses on theoretical models and algorithmic design, and therefore does not provide details on experimental setup such as hyperparameters or training configurations. |