Understanding Over Participation in Simple Contests
Authors: Priel Levy, David Sarne
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental methodology we use compares contestants participation decisions in eight contest settings differing in the nature of the contest used, the number of contestants used and the theoretical participation predictions to those obtained (whenever applicable) by subjects facing equivalent non-competitive decision situations in the form of a lottery. |
| Researcher Affiliation | Academia | Priel Levy, David Sarne Department of Computer Science Bar Ilan University, Israel priel.levy@live.biu.ac.il, sarned@cs.biu.ac.il |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code related to the described methodology. |
| Open Datasets | No | Subjects were recruited and interacted through AMT which has proven to be a well established method for data collection in tasks which require human intelligence (Paolacci, Chandler, and Ipeirotis 2010). Overall, we had 4000 participants taking part in our experiments: 2000 competing in contests, 1200 choosing whether or not to participate in lotteries replicating decisionsituations similar to those experienced in contests and 800 competing as the third or fifth contestants in contests (for augmenting the space of contest-related decisions that can be emulated by lotteries). The paper collects data via experiments, but does not provide access to a public dataset in the conventional sense of machine learning datasets, nor does it refer to a named, pre-existing public dataset with access details. |
| Dataset Splits | No | The paper describes its experimental design and data collection from human subjects, but does not specify training, validation, or test dataset splits in the context of machine learning model development. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running its experiments or simulations. |
| Software Dependencies | No | The paper mentions developing a 'java-script web-based game' but does not specify any software dependencies with version numbers (e.g., libraries, frameworks, or solvers) needed to replicate the experiment. |
| Experiment Setup | Yes | We used eight experimental treatments, each corresponding to a different combination of the contest type (sequential and parallel), the number of contestants (3 and 5), and the theoretical predictions for the participation probability (high and low participation thresholds in the sequential contest and high and low participation probability in the parallel contest). The prize M and cost c values for each treatment are given in table 1. |