Foundations of Peer-to-Peer Reputation

Abstract

Successful classification of good or bad behavior in the digital domain is limited to central governance, as can be seen with trading platforms, search engines and news feeds. We explore and consolidate existing work on decentralized reputation systems to form a common denominator for what makes a reputation system successful when applied without a centralized reputation authority, formalized in 7 axioms and 3 postulates. Reputation must start from nothing and always reward performed work, respectively lowering and increasing as work is consumed and performed. However, it is impossible for nodes to perform work in a purely synchronous attack-proof work model and real systems must necessarily employ relaxations to such a work model. We show how the relaxations of performing parallel work, allowing unconsumed work and seeding well-known identities with work satisfy our model. Our formalizations allow constraint driven design of decentralized reputation mechanisms.