Paper short abstract:

Drawing upon science and technology studies and China studies, my paper explains how China’s specific political and economic circumstances allowed the application of optimal logic to algorithmic management of labor in a way that was not, and arguably could not be, replicated in Western countries.

Paper long abstract:

Optimization—a set of mathematical techniques to find the best solution from all possibilities—has been deemed as core technical and commercial logic undergirding thriving digital platforms. In general, these platforms leverage the logic to engineer available human and non-human resources—such as labor, time, space, and digital affordances—to achieve several desired outcomes. The existing literature in media studies and anthropology has explained how optimization is not only technical but also economic and political simultaneously. It creates an economic form of life where pursuing quantification and efficiency are emphasized, it achieves so by overtly prioritizing some groups’ interests while sacrificing others.

While technical elements that constitute optimization might be generic, which typically includes optimizing objectives, input variables, and constraint variables, their configuration in specific local contexts is always unique. This paper focuses on China’s digital labor platforms, specifically food delivery and ride-hailing platforms. Drawing upon science and technology studies and China studies, I explain how China’s specific political and economic circumstances allowed the application of optimal logic to labor management in a way that was not, and arguably could not be, replicated in Western countries. I draw an analogy between the state’s framework of migrant labor as disposable resources and digital platforms’ optimal framework that treats workers as malleable robots that exist in the mechanical world. Ultimately, I show how deep socio-political logic at work in managing populations and labor shapes and is manifested in the technical designs of digital platforms.