Advice Provision for Choice Selection Processes with Ranked Options
Authors: Amos Azaria, Ya'akov Gal, Claudia Goldman, Sarit Kraus
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In an empirical evaluation involving human users on AMT, we showed that the social utility approach significantly outperformed the MDP approach. All of our experiments were performed using Amazon s Mechanical Turk service (AMT). Participation in all experiments consisted of a total of 272 subjects from the USA, of which 44.4% were females and 55.6% were males. |
| Researcher Affiliation | Collaboration | 1 Department of Computer Science, Bar-Ilan University, Ramat Gan 52900, Israel 2 Department of Information Systems Engineering, Ben-Gurion University of the Negev, Israel 3 General Motors Advanced Technical Center, Herzliya 46725, Israel 4 Institute for Advanced Computer Studies University of Maryland, MD 20742 |
| Pseudocode | No | No structured pseudocode or algorithm blocks (e.g., sections labeled 'Pseudocode' or 'Algorithm') are present in the paper. |
| Open Source Code | No | The paper does not provide any explicit statement about making the source code available or a link to a code repository. |
| Open Datasets | No | All of our experiments were performed using Amazon s Mechanical Turk service (AMT). Participation in all experiments consisted of a total of 272 subjects from the USA, of which 44.4% were females and 55.6% were males. The paper describes data collected from human users on AMT, which is a custom-collected dataset from their own experiments. No concrete access information (link, DOI, specific citation to a public repository) is provided for this dataset. |
| Dataset Splits | No | The paper mentions dividing subjects into 'treatment groups' for data collection, but does not specify dataset splits (e.g., percentages or sample counts) for training, validation, or testing in a machine learning context. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU/GPU models, memory, or processor types) used for running experiments. |
| Software Dependencies | No | The paper mentions methods like 'ANOVA test' and 'value-iteration' but does not list specific software libraries, frameworks, or solvers with version numbers. |
| Experiment Setup | No | The paper describes the construction of the user model and agents, but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, epochs) or detailed training configurations. |