Human Decision-Making under Limited Time

Authors: Pedro A. Ortega, Alan A. Stocker

NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Here we experimentally tested the predictions made by a formalization of bounded rationality based on ideas from statistical mechanics and information-theory. We systematically tested human subjects in their ability to solve combinatorial puzzles under different time limitations.
Researcher Affiliation Academia Pedro A. Ortega Department of Psychology University of Pennsylvania Philadelphia, PA 19104 ope@seas.upenn.edu Alan A. Stocker Department of Psychology University of Pennsylvania Philadelphia, PA 19014 astocker@sas.upenn.edu
Pseudocode No The paper does not contain any pseudocode or clearly labeled algorithm blocks.
Open Source Code No The paper does not provide any concrete access to source code for the methodology described.
Open Datasets No The paper describes a custom experimental setup using self-created combinatorial puzzles and does not provide concrete access information for a publicly available or open dataset.
Dataset Splits No The paper mentions '90 training and 285 test trials' but does not specify a separate validation dataset split.
Hardware Specification No The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments.
Software Dependencies No The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment.
Experiment Setup Yes Training trials lasted 10s each, while test trials had durations of 1.25, 2.5, and 5s. For the learning rate ηt > 0, we choose a simple schedule that satisfied the Robbins-Monro conditions.