Nurturing Group-Beneficial Information-Gathering Behaviors Through Above-Threshold Criteria Setting
Authors: Igor Rochlin, David Sarne, Maytal Bremer, Ben Grynhaus
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The analysis results in a closed form solution for the strategies to be used in equilibrium and facilitates the numerical investigation of different model properties as well as a comparison to the dual mechanism according to only an agent whose contribution is below the specified threshold gets to benefit from the contributions of others. One important contribution enabled through the analysis provided is in showing that, counter-intuitively, for some settings the use of the above-threshold criteria is outperformed by the use of the below-threshold criteria as far as collective and individual performance is concerned. |
| Researcher Affiliation | Academia | Igor Rochlin igor.rochlin@gmail.com School of Computer Science College of Management Rishon Le Zion, Israel David Sarne sarned@cs.biu.ac.il Department of Computer Science Bar-Ilan University Ramat-Gan, Israel Maytal Bremer bmaytalb@gmail.com School of Computer Science College of Management Rishon Le Zion, Israel Ben Grynhaus ben.grynhaus@gmail.com School of Computer Science College of Management Rishon Le Zion, Israel |
| Pseudocode | No | The paper describes mathematical models, equations, and theoretical analyses, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about making its source code openly available or provide a link to a code repository. |
| Open Datasets | No | The paper describes the probability distribution function f(y) as "f(y) = 1 for any 0 y 1 and f(y) = 0 otherwise", which is a uniform distribution, and a "fall-back value v0 = 0". This specifies the *model* of the data-generating process, but not a named or publicly accessible dataset in the traditional sense, nor does it provide a link or citation for one. |
| Dataset Splits | No | The paper performs numerical illustrations based on equilibrium analysis but does not describe typical machine learning-style training, validation, and test dataset splits. |
| Hardware Specification | No | The paper describes theoretical analysis and numerical illustrations but does not provide any specific details about the hardware used to perform these computations (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper describes mathematical models and numerical illustrations but does not specify any software names with version numbers that were used for the implementation or analysis. |
| Experiment Setup | Yes | The other setting parameters used are: n = 5 and k = 5 (for graph (a)); n = 5 and c = 0.2 (for graph (b)); and k = 3 and c = 0.2 (for graph (c)). The threshold used with each method is the one that maximizes the expected profit of the agents. |