The Nudge Wars: A Glimpse into the Modern Socialist Calculation Debate

Abstract

We investigate the claims of behavioral paternalism in the more realistic framework of complex choice. In particular, we analyze the claims made by behavioral paternalists that predictive analytics over large amounts of data will make it possible to target and successfully implement purportedly welfare-enhancing nudges deemed to make nudged agents better off “as judged by themselves” (AJBT). We draw parallels between the socialist calculation debate and nudge theoretical arguments, particularly the libertarian socialism of H. D. Dickinson and the libertarian paternalism of Cass Sunstein and Richard Thaler. We find that if actual idealized behavior is a more complicated process of recursive feedback using a knowledge classification method, behavioral paternalists engaging in an automatized process of notice-and-comment rulemaking using Big Data methods still encounter epistemological problems and the problems associated with radical uncertainty unearthed during the socialist calculation debate and afterwards. We contend that when behavioral paternalists refer to the advancements in cost-benefit analysis made possible by Big Data and machine learning, they are implicitly referring to unlocking the advantages of a self-regulatory feedback mechanism indistinguishable in structure from a cybernetic system. That is, they are hoping that Big Data and machine learning will revive the dream of a practicable control theory in economics that died in the 1970s along with the hope of practicable mechanism design in economics.