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 | Conference PDF | Archive PDF | Plain Text | 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. |