Mechanism design is a form of optimization developed in economic theory. It casts economists as institutional engineers, choosing an outcome and then arranging a set of market rules and conditions to achieve it. The toolkit from mechanism design is widely used in economics, policymaking, and now in building and managing online environments. Mechanism design has become one of the most pervasive yet inconspicuous influences on the digital mediation of social life. Its optimizing schemes structure online advertising markets and other multi-sided (...) platform businesses. Whatever normative rationales mechanism design might draw on in its economic origins, as its influence has grown and its applications have become more computational, we suggest those justifications for using mechanism design to orchestrate and optimize human interaction are losing traction. In this article, we ask what ideological work mechanism design is doing in economics, computer science, and its applications to the governance of digital platforms. Observing mechanism design in action in algorithmic environments, we argue it has become a tool for producing information domination, distributing social costs in ways that benefit designers, and controlling and coordinating participants in multi-sided platforms. (shrink)

This paper focuses on the conception and use of machine-learning algorithms for marketing. In the last years, specialized service providers as well as in-house data scientists have been increasingly using machine learning to predict consumer behavior for large companies. Predictive marketing thus revives the old dream of one-to-one, perfectly adjusted selling techniques, now at an unprecedented scale. How do predictive marketing devices change the way corporations know and model their customers? Drawing from STS and the sociology of quantification, I propose (...) to study the original ambivalence that characterizes the promise of a mass personalization, i.e. algorithmic processes in which the precise adjustment of prediction to unique individuals involves the computation of massive datasets. By studying algorithms in practice, I show how the active embedding of local preexisting consumer knowledge and punctual de-personalization mechanisms are keys to the epistemic and organizational success of predictive marketing. This paper argues for the study of algorithms in their contexts and suggests new perspectives on algorithmic objectivity. (shrink)

How corporations surveil and influence consumers using big data tools is a major area of research and public debate. However, few studies explore it in relation to physicians in the USA, even though they have been surveilled and targeted by the pharmaceutical industry since at least the 1950s. Indeed, in 2010, concerns about the pharmaceutical industry's undue influence led to the passing of the Physician Sunshine Act, a unique piece of transparency legislation that requires companies to report their financial ties (...) to physicians and teaching hospitals in a public database. This article argues that while the Sunshine Act has clearly helped expose important commercial influences on both prescribing and the scale of industry involvement with physicians, it has also, paradoxically, fuelled further commercial surveillance and marketing. The article casts new light on innovative pharmaceutical marketing approaches and the key role of data brokers and analytics companies in the identification, targeting, managing, and surveillance of physicians. We place this analysis within the political economies of the pharmaceutical industry, surveillance-based marketing, and transparency, and argue that policies to promote increased transparency must be tightly tied to policies that impede the commodification and use of transparency data for surveillance and marketing purposes. (shrink)