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..
A Strategic Analysis of Prepayments in Financial Credit Networks
Authors: Hao Zhou, Yongzhao Wang, Konstantinos Varsos, Nicholas Bishop, Rahul Savani, Anisoara Calinescu, Michael Wooldridge
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This study investigates prepayments from both theoretical and empirical perspectives. We use a simulation-based approach, known as empirical game-theoretic analysis (EGTA). |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Oxford, UK 2The Alan Turing Institute, UK 3Department of Computer Science, University of Liverpool, UK |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block is provided in the paper. |
| Open Source Code | No | No explicit statement or link providing concrete access to open-source code for the methodology is found in the paper. |
| Open Datasets | No | The paper mentions 'synthetic networks' and a 'network generator', with details to be provided in a 'full version', but does not provide concrete access information (link, DOI, formal citation) to a publicly available or open dataset in this paper. |
| Dataset Splits | No | The paper describes generating 'synthetic networks' but does not specify any train/validation/test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or specific computing environments) used for running the experiments are provided. |
| Software Dependencies | No | No specific ancillary software details, such as library names with version numbers, are provided. |
| Experiment Setup | No | The paper describes heuristic strategies and simulation parameters like 'external assets ranging from 40 to 70' and 'α = 0.5', but does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, epochs) typically associated with training models. |