Infochain: A Decentralized System for Truthful Information Elicitation
Year: 2019
Type: preprint
Abstract: Incentive mechanisms play a pivotal role in collecting correct and reliable information from self-interested agents. Peer-prediction mechanisms are game-theoretic mechanisms that incentivize agents for reporting the information truthfully, even when the information is unverifiable in nature. Traditionally, a trusted third party implements these mechanisms. We built Infochain, a decentralized system for information elicitation. Infochain ensures transparent, trustless and cost-efficient collection of information from self-interested agents without compromising the game-theoretical guarantees of the peer-prediction mechanisms. In this paper, we address various non-trivial challenges in implementing these mechanisms in Ethereum and provide experimental analysis.
Source: arXiv (Cornell University)
Authors: Cyril van Schreven, Naman Goel, Boi Faltings
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2970735658