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].
Analytically Tractable Models for Decision Making under Present Bias
Authors: Yasunori Akagi, Naoki Marumo, Takeshi Kurashima
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We demonstrate that for a specific class of c, the trajectory of states taken by an agent can be described analytically. This tractability is a significant advantage not found in existing models and allows us to perform further theoretical analyses, including finding optimal interventions. Based on such analytical descriptions, we analyze three problems related to agents under present bias: task abandonment, optimal goal setting, and optimal reward scheduling. |
| Researcher Affiliation | Industry | 1NTT Human Informatics Labratories, NTT Corporation, 2NTT Communication Science Laboratories, NTT Corporation |
| Pseudocode | No | The paper mentions algorithms (e.g., "efficient algorithm") and formulas but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | This paper is theoretical and does not use or reference any publicly available datasets for training or evaluation. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical validation or dataset splits for training, validation, or testing. |
| Hardware Specification | No | This paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not include details about an experimental setup, such as hyperparameters or system-level training settings. |