Undoing Optimization
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Undoing Optimization
Author: Alison B. Powell
language: en
Publisher: Yale University Press
Release Date: 2021-04-13
A unique examination of the civic use, regulation, and politics of communication and data technologies City life has been reconfigured by our use--and our expectations--of communication, data, and sensing technologies. This book examines the civic use, regulation, and politics of these technologies, looking at how governments, planners, citizens, and activists expect them to enhance life in the city. Alison Powell argues that the de facto forms of citizenship that emerge in relation to these technologies represent sites of contention over how governance and civic power should operate. These become more significant in an increasingly urbanized and polarized world facing new struggles over local participation and engagement. The author moves past the usual discussion of top-down versus bottom-up civic action and instead explains how citizenship shifts in response to technological change and particularly in response to issues related to pervasive sensing, big data, and surveillance in "smart cities."
Datapolis
Author: Paul Cournet
language: en
Publisher: TU Delft OPEN Publishing
Release Date: 2024-02-05
DATAPOLIS looks into the materiality of data, its inherent ethical and political contradictions as well as cultural and environmental footprints, by following two main trajectories: the first one attempts to define what ‘the cloud’ is and how it operates. From the systems and infrastructures behind the Internet to the apparatus, gizmos and buildings that can transcend scales and temporal dimensions. The second one explores how data penetrates our existence, not only by affecting the ways we live and work, or design and make cities, but by offering distinct ways of life and organization that otherwise would not have been possible. Through various visual and textual materials, this book speculates on the ways in which architecture can engage with data and digital technology beyond its mere instrumental use in making (smart) cities. DATAPOLIS is edited by Paul Cournet and Negar Sanaan Bensi. With contributions by Kees Kaan, Kate Crawford, Shannon Mattern, Ruha Benjamin, Marina Otero Verzier and Joost Grootens a.o. The most complete version of this work was published in 2023 by nai010.
Introduction to Optimization-Based Decision-Making
The large and complex challenges the world is facing, the growing prevalence of huge data sets, and the new and developing ways for addressing them (artificial intelligence, data science, machine learning, etc.), means it is increasingly vital that academics and professionals from across disciplines have a basic understanding of the mathematical underpinnings of effective, optimized decision-making. Without it, decision makers risk being overtaken by those who better understand the models and methods, that can best inform strategic and tactical decisions. Introduction to Optimization-Based Decision-Making provides an elementary and self-contained introduction to the basic concepts involved in making decisions in an optimization-based environment. The mathematical level of the text is directed to the post-secondary reader, or university students in the initial years. The prerequisites are therefore minimal, and necessary mathematical tools are provided as needed. This lean approach is complemented with a problem-based orientation and a methodology of generalization/reduction. In this way, the book can be useful for students from STEM fields, economics and enterprise sciences, social sciences and humanities, as well as for the general reader interested in multi/trans-disciplinary approaches. Features Collects and discusses the ideas underpinning decision-making through optimization tools in a simple and straightforward manner Suitable for an undergraduate course in optimization-based decision-making, or as a supplementary resource for courses in operations research and management science Self-contained coverage of traditional and more modern optimization models, while not requiring a previous background in decision theory