Algorithmic scoring systems provide novel ways to sort larger populations of borrowers, consumers, employees and benefits recipients. Such algorithmic regimes of classification enable the more efficient capture of value (in the form of rents) outside of traditional sites of production. This paper considers how distributional claims making has evolved in response to the use of algorithms and digital platforms to more profitably discriminate between market participants and extract information rents. More specifically, the paper interrogates an emerging form of collective distributional politics, which I call the moral economy of the serial crowd. This serial crowd is one in which individual acts of algorithmic and digital selfcare (e.g. credit building and monitoring, social media profile curation, self-tracking, etc.) are imagined to ‘scale up,’ and together constitute a collective act of ‘self-protection’ from predatory economic actors, and morally (re)order markets. To understand why this style of social claims making has assumed salience in the current conjuncture, the paper analyses (i) movements to redress inequality and discrimination while appearing to be distributionally neutral, and (ii) the refiguration of the crowd from a problem to be managed by elites to a ‘wise’ exploitable market problem solver. The paper then discusses contemporary examples where serial crowds are associated with various moral economic orders from Go Fund Me campaigns to debt resistance, and credit building.
- cultural economy
- economy geography
ASJC Scopus subject areas
- Business, Management and Accounting(all)