Title
Quantifying Agent Strategies under Reputation
Abstract
Our research proposes a simple buyer/seller game that captures the incentives dictating the interaction between peers in resource trading peer-to-peer networks. We prove that for simple reputation-based buyer strategies, a seller驴s decision whether to cheat or not is dependent only on the length of its transaction history, not on the particular actions committed. Given a finite number of transactions, a peer can compute a utility optimal sequence of cooperations and defections. With the limited information provided by many reputation systems, a peer has incentive to defect on a large fraction of its transactions. If temporal information is used, equilibrium is reached when peers predominantly cooperate.
Year
DOI
Venue
2005
10.1109/P2P.2005.29
Peer-to-Peer Computing
Keywords
Field
DocType
reputation system,finite number,temporal information,simple buyer,large fraction,quantifying agent strategies,incentives dictating,simple reputation-based buyer strategy,particular action,limited information,seller game,distributed systems,electronic trading,transaction processing,e commerce
Transaction processing,Incentive,Computer science,Computer network,Peer to peer computing,Electronic trading,Database transaction,E-commerce,Reputation
Conference
ISBN
Citations 
PageRank 
0-7695-2376-5
1
0.36
References 
Authors
10
3
Name
Order
Citations
PageRank
Sergio Marti147428.48
Héctor García-Molina2243595652.13
Garcia-Molina, H.3264158.67