Digital crowdsourcing (CS) is a modern approach to perform certain large
projects using small contributions of a large crowd. In CS, a taskmaster
typically breaks down the project into small batches of tasks and assigns them
to so-called workers with imperfect skill levels. The crowdsourcer then
collects and analyzes the results for inference and serving the purpose of the
project. In this work, the CS problem, as a human-in-the-loop computation
problem, is modeled and analyzed in an information theoretic rate-distortion
framework. The purpose is to identify the ultimate fidelity that one can
achieve by any form of query from the crowd and any decoding (inference)
algorithm with a given budget. The results are established by a joint source
channel (de)coding scheme, which represent the query scheme and inference, over
parallel noisy channels, which model workers with imperfect skill levels. We
also present and analyze a query scheme dubbed $k$-ary incidence coding and
study optimized query pricing in this setting.

通过信息论速率失真框架对人在循环计算问题进行建模和分析，以确定通过任何形式的查询和任何解码算法的结果和给定预算所能达到的最终保真度。此外，该论文还介绍并分析了一种查询方案，称为 $k$ -ary incidence coding，并研究了该场景下的查询定价。