Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].

Fair Information Sharing for Treasure Hunting

Authors: Yiling Chen, Kobbi Nissim, Bo Waggoner

AAAI 2015 | Venue PDF | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We design contract-based mechanisms for information sharing without money. ... We construct a one-shot contract-based mechanism and show that in this mechanism, to maximize winning probability, each agent should report her private information truthfully if all other agents report truthfully. Then, we prove the fairness and welfare properties of the mechanism. We also show that the mechanism satisfies ϵ-voluntary participation for ϵ 0 as information sets grow large.
Researcher Affiliation Academia Yiling Chen Harvard SEAS EMAIL Kobbi Nissim Ben-Gurion University and Harvard CRCS EMAIL Bo Waggoner Harvard SEAS EMAIL
Pseudocode Yes Mechanism 1: One-Shot Mechanism Input: Si for each agent i. Output: A partition of SN = i NSi, with Πi assigned to agent i. set SN = i Si; foreach agent i do compute i s winning probability pi; end initialize each Πi = ; foreach location s SN do let i be a random agent chosen with probability pi; add s to Πi; end output the sets Πi for each i;
Open Source Code No The paper does not provide concrete access to source code for the methodology described.
Open Datasets No The paper is theoretical and does not use or reference any datasets for training or evaluation.
Dataset Splits No The paper is theoretical and does not describe or use data splits for validation.
Hardware Specification No The paper is theoretical and does not describe computational experiments or specific hardware used.
Software Dependencies No The paper is theoretical and does not list specific software dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not provide details about an experimental setup, hyperparameters, or training settings.