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..
Subsidy Allocations in the Presence of Income Shocks
Authors: Rediet Abebe, Jon Kleinberg, S. Matthew Weinberg7032-7039
AAAI 2020 | Venue PDF | 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. |