Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition
Authors: Ahmed M. Alaa, Mihaela van der Schaar
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we present a Bayesian decision-making model in which a decision-maker adaptively decides when to gather (costly) information from an underlying time series in order to accumulate evidence on the occurrence/non-occurrence of an adverse event. We characterize the structure of the optimal decision-making policy that prescribes when should the decision-maker acquire new information, and when should she stop acquiring information and issue a final prediction. |
| Researcher Affiliation | Academia | Ahmed M. Alaa Electrical Engineering Department University of California, Los Angeles Mihaela van der Schaar Electrical Engineering Department University of California, Los Angeles |
| Pseudocode | No | The paper describes mathematical models and theorems, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for open-source code. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a specific dataset, therefore no information about public dataset access for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not report on empirical experiments, thus no information on training, validation, or test dataset splits is provided. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory specifications) used for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, therefore no specific experimental setup details like hyperparameter values or training configurations are provided. |