2021/12/20 by Samhita Kanaparthy, Kanaparthy, Samhita, Sankarshan Damle +3
Computer Science · Decision Sciences · Social Sciences · #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #Experimental Behavioral Economics Studies #FOS: Computer and information sciences #Mobile Crowdsensing and Crowdsourcing
paper · pdf · doi:10.48550/arxiv.2112.10659
openalex publication_date 2021/12/20 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
Crowdsourcing is an effective method to collect data by employing distributed human population. Researchers introduce appropriate reward mechanisms to incentivize agents to report accurately. In particular, this paper focuses on Peer-Based Mechanisms (PBMs). We observe that with PBMs, crowdsourcing systems may not be fair, i.e., agents may not receive the deserved rewards despite investing efforts and reporting truthfully. Unfair rewards for the agents may discourage participation. This paper aims to build a general framework that assures fairness for PBMs in temporal settings, i.e., settings that prefer early reports. Towards this, we introduce two general notions of fairness for PBMs, namely gamma-fairness and qualitative fairness. To satisfy these notions, our framework provides trustworthy agents with additional chances of pairing. We introduce Temporal Reputation Model (TERM) to quantify agents' trustworthiness across tasks. With TERM as the key constituent, we present our iterative framework, REFORM, that can adopt the reward scheme of any existing PBM. We demonstrate REFORM's significance by deploying the framework with RPTSC's reward scheme. Specifically, we prove that REFORM with RPTSC considerably improves fairness; while incentivizing truthful and early reports. We conduct synthetic simulations and show that our framework provides improved fairness over RPTSC.