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.

算法评分系统提供了新颖的方法来对较大数量的借款人，消费者，雇员和受益人进行分类。这种分类算法可以在传统生产场所之外更有效地获取价值（以租金的形式）。本文考虑了如何根据算法和数字平台对分配式索赔做出的改进，从而更有利地区分市场参与者并提取信息租金。更具体地说，本文审视了一种新兴的集体分配政治形式，我称之为连续人群的道德经济。。在这一系列人群中，算法和数字自我护理（例如信用建立和监控，社交媒体档案管理，自我跟踪等）的个人行为被想象为“扩大规模”，并共同构成“自我”的集体行为“保护”免受掠夺性经济参与者和道德（重新）秩序市场的侵害。为了理解为什么这种社会主张的制定方式在当前形势下具有显着性，本文分析了（i）纠正不平等和歧视的运动，而这些运动似乎在分布上是中立的，并且（ii）人群对问题的重塑是由精英管理，成为“明智的”可利用的市场问题解决者。然后，本文讨论了当代的例子，在这些例子中，一系列的人群与从Go Fund Me运动到抗债务的各种道德经济秩序相关，