Temporal Information Design in Contests

Authors: Priel Levy, David Sarne, Yonatan Aumann

IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We provide a game-theoretic analysis of such information disclosure schemes as they apply to two common models of contests: (a) simple contests, wherein contestants decisions concern only their participation; and (b) Tullock contests, wherein contestants choose the effort levels to expend. For each of these we analyze and characterize the equilibrium strategy, and exhibit the potential benefits of information design.
Researcher Affiliation Academia Priel Levy , David Sarne and Yonatan Aumann Department of Computer Science, Bar Ilan University, Israel priel.levy@live.biu.ac.il, sarned@cs.biu.ac.il, aumann@cs.biu.ac.il
Pseudocode No The paper does not contain structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for source code.
Open Datasets No The paper describes theoretical examples with defined parameters and distributions (e.g., "three homogeneous agents and a prize M = 0.6", "f(x) = 1 for 0 x 1", "c1 = 0.1 with probability 0.12 and c2 = 0.9 with probability 0.88"), but it does not use or provide concrete access information for a publicly available or open dataset.
Dataset Splits No The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) as it deals with theoretical models and not empirical data.
Hardware Specification No The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its analyses, as the work is theoretical.
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 theoretical analysis.
Experiment Setup No The paper discusses parameters for its theoretical models (e.g., prize M, costs c, probability distributions f(x)), but these are not experimental setup details like hyperparameters or system-level training settings for an empirical study.