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 [1].
Time-Constrained Participatory Budgeting Under Uncertain Project Costs
Authors: Dorothea Baumeister, Linus Boes, Christian Laußmann
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition, we experimentally evaluate algorithms for uncertain project costs that sequentially start new projects using the information of the cost of already finished projects and thus optimize the utilization of the budget. Meanwhile, they do their best effort to satisfy as many desirable properties as possible. Furthermore, we analyze different forms of proportionality for uncertain project costs. We test both algorithms using real data from the Participatory Budgeting Library (see [Stolicki et al., 2020]), modified to fit our uncertainty scenario. |
| Researcher Affiliation | Academia | Dorothea Baumeister , Linus Boes and Christian Laußmann Heinrich-Heine University D usseldorf EMAIL |
| Pseudocode | No | The paper describes algorithms such as Best effort exhaustiveness (BEE), Best effort punctuality (BEP), and Rule X for uncertain cost (RX) in text, but it does not include formal pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | We test both algorithms using real data from the Participatory Budgeting Library (see [Stolicki et al., 2020]), modified to fit our uncertainty scenario. |
| Dataset Splits | No | The paper mentions using "real data" and testing algorithms but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU/GPU models or memory. |
| Software Dependencies | No | The paper mentions using "real data from the Participatory Budgeting Library" but does not specify any software names with version numbers for dependencies (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | No | The paper describes the proposed algorithms and their theoretical properties but does not provide specific experimental setup details such as hyperparameter values, optimization settings, or training configurations. |