A Continuous-time Tractable Model for Present-biased Agents
Authors: Yasunori Akagi, Hideaki Kim, Takeshi Kurashima
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This study introduces a novel continuous-time mathematical model for agents influenced by present bias. Using the variational principle, we model human behavior, where individuals repeatedly act according to a sequence of states that minimize their perceived cost. Our model not only retains analytical tractability but also accommodates various discount functions. Using this model, we consider intervention optimization problems under exponential and hyperbolic discounting and theoretically derive optimal intervention strategies, offering new insights into managing present-biased behavior. ... As a result, it is currently challenging to compare past experimental data with the theoretical results obtained in this study. We consider this to be a significant limitation of this paper. As a future direction, we aim to conduct experiments with human subjects to collect data and examine the consistency with our theoretical findings. |
| Researcher Affiliation | Industry | NTT Human Informatics Labratories, NTT Corporation, 2NTT Communication Science Laboratories, NTT Corporation EMAIL |
| Pseudocode | No | The paper focuses on mathematical modeling, theorems, and derivations, such as Theorem 1 and its proof, without presenting any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository. It cites an ArXiv preprint but this is not a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on developing a mathematical model. It describes hypothetical scenarios for progress-based tasks (e.g., "completing 20 hours of exercise within a week") but does not use or provide access to any specific datasets for empirical evaluation. Section 7.2 mentions future empirical validation. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on datasets, therefore no dataset split information is provided. |
| Hardware Specification | No | The paper describes a theoretical mathematical model and its derivations. It does not involve any experimental setup that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical modeling and analysis. It does not mention any software dependencies or specific versions used for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical, presenting a mathematical model and derivations. It does not describe any empirical experiments, and thus no experimental setup details like hyperparameters or training settings are provided. |