When Suboptimal Rules
Authors: Avshalom Elmalech, David Sarne, Avi Rosenfeld, Eden Erez
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we empirically show that agents that dispense optimal advice will not necessary facilitate the best improvement in people s strategies. Instead, we claim that agents should at times suboptimally advise. We provide results demonstrating the effectiveness of a suboptimal advising approach in extensive experiments in two canonical mixed agent-human advice-giving domains. |
| Researcher Affiliation | Academia | Avshalom Elmalech Bar-Ilan University, Israel elmalea@cs.biu.ac.il David Sarne Bar-Ilan University, Israel sarned@cs.biu.ac.il Avi Rosenfeld Jerusalem College of Technology, Israel rosenfa@jct.ac.il Eden Shalom Erez Independent Researcher, Israel edenerez@gmail.com |
| Pseudocode | No | The paper describes the advice generation using mathematical formulas and textual descriptions, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link or a statement about code availability in supplementary materials. |
| Open Datasets | No | The paper describes using two canonical games ("Company Valuation Game" and "Birthday Game") and references their original descriptions by Samuelson and Bazerman (1985) and Ball (1914), respectively. However, it does not provide concrete access information (link, DOI, specific repository, or citation to a public dataset resource) for the experimental data collected. |
| Dataset Splits | No | The paper describes the experimental design for human participants, including treatments and number of games played by each participant (e.g., "Each participant took part in one out of three treatments", "Participants were asked to play 10 times the buyer side of the game"). However, it does not specify dataset splits like train/validation/test sets as would be typical for machine learning model evaluation. |
| Hardware Specification | No | The paper mentions using "Amazon Mechanical Turk(AMT)" for participant recruitment but provides no specific details about the hardware (e.g., CPU, GPU models, memory, cloud instance types) used to conduct or analyze the experiments. |
| Software Dependencies | No | The paper mentions using ANOVA for statistical analysis but does not provide any specific software names with version numbers or other ancillary software dependencies required to replicate the experimental environment. |
| Experiment Setup | Yes | The paper provides specific details about the experimental setup, such as the values of 'x' used in the Company Valuation game ("{1, 1.2, 1.4, 1.6, 1.8, 2.2, 2.4, 2.6, 2.8, 3}") and 'N' in the Birthday game ("{17, 25, 38, 51, 70}"), as well as the functions used to generate suboptimal advice (e.g., "Obuyer = 50 (x 1)3 for 1 < x < 2", "p(N)t = p(N) 6 N dp(N)/d N"). |