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Software has become a key component of contemporary life and algorithmic techniques that rank, classify, or recommend anything that fits into digital form are everywhere. This book approaches the field of information ordering conceptually as well as historically. Building on the philosophy of Gilbert Simondon and the cultural techniques tradition, it first examines the constructive and cumulative character of software and shows how software-making constantly draws on large reservoirs of existing knowledge and techniques. It then reconstructs the historical trajectories of a series of algorithmic techniques that have indeed become the building blocks for contemporary practices of ordering. Developed in opposition to centuries of library tradition, coordinate indexing, text processing, machine learning, and network algorithms instantiate dynamic, perspectivist, and interested forms of arranging information, ideas, or people. Embedded in technical infrastructures and economic logics, these techniques have become engines of order that transform the spaces they act upon.
Bernhard Rieder is Associate Professor of New Media and Digital Culture at the University of Amsterdam and a collaborator with the Digital Methods Initiative.
"How were computers given the task of searching and classifying information? Engines of Order offers an exciting archaeology of the algorithmic techniques that made these feats possible. It is a unique book that mixes a high level of theorization with a detailed examination of modes of expression in the form of technique." - Dominique Cardon, Professor at Sciences Po Paris and Director, médialab "This book is essential reading for everybody who wants to better understand our algorithmic society. For the first time, a number of key data techniques such as data-driven classification methods are given proper theoretical and historical attention. I highly recommend this clear and smart guide to the world in which we are all living." - Lev Manovich, Professor at City University of New York and Director, Cultural Analytics Lab
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