An approach for developing a highly trustworthy crowd-sourced workforce

Abstract

In applying crowd-sourcing techniques, one of the most critical challenges is building a crowd workforce that is both capable and trustworthy. Previous studies proposed numerous strategies, methods, and mechanism to motivate individuals; however, although the results improved the effectiveness and efficiency of finishing crowd-sourcing tasks, few studies focused on improving the honesty of crowd-sourced workers and assisting requesters in obtaining the correct quality report. To address this, based on the principal-agent model and signaling game theory, we design a novel mechanism for building a capable and trustworthy crowd-sourced workforce. This mechanism enables information exchange between crowd-sourced workers and requesters, and leverages a random inspection strategy to assign financial incentives/punishments to honest/dishonest behaviors accordingly. To validate our mechanism, we conduct an extensive simulation. The results show this mechanism is effective and efficient to motivate workers to behave in a trustworthily manner and capable of changing the behavior of dishonest workers with minimal extra cost.