On a Competitive Secretary Problem

Authors: Anna Karlin, Eric Lei

AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We present numerical results from simulations of these strategies.
Researcher Affiliation Academia Anna Karlin and Eric Lei karlin@cs.washington.edu elei@andrew.cmu.edu
Pseudocode No The paper describes algorithms (e.g., dynamic programming steps) in prose and mathematical notation but does not provide structured pseudocode blocks or sections labeled 'Algorithm'.
Open Source Code No No statement or link providing concrete access to open-source code for the described methodology was found.
Open Datasets No The paper describes a theoretical game theory problem with simulations, not empirical studies on a specific publicly available dataset. Thus, the concept of a 'dataset' for training, validation, or testing in the typical machine learning sense is not applicable, and no specific dataset information or access is provided.
Dataset Splits No The paper describes a theoretical game theory problem with simulations, not empirical studies on a specific publicly available dataset. Thus, the concept of a 'dataset' for training, validation, or testing in the typical machine learning sense is not applicable, and no specific dataset split information is provided.
Hardware Specification No No specific hardware details (e.g., CPU/GPU models, memory) used for running the simulations are mentioned.
Software Dependencies No No specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks) are mentioned for the simulations.
Experiment Setup No The paper describes the parameters of the game (e.g., 'n applicants', 'k employers') and the derived optimal thresholds, but it does not specify an 'experimental setup' in terms of hyperparameters for a machine learning model, training configurations, or system-level settings typically found in empirical studies.