Cary Coglianese
University of Pennsylvania
189 Papers
1.3K Citations
Cary Coglianese is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Government & Administrative law. The author has an hindex of 33, co-authored 176 publications. Previous affiliations of Cary Coglianese include Stanford University & Harvard University.
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Papers
Democracy and Its Critics
TL;DR: The course is focused on historical texts, most of them philosophical as discussed by the authors, and context for understanding the texts and the course of democratic development will be provided in lecture and discussions, and by some background readings (Dunn).
Management-Based Regulation: Prescribing Private Management to Achieve Public Goals
Cary Coglianese,David Lazer +1 more
TL;DR: In this paper, the authors develop a framework for assessing conditions for using management-based regulation as opposed to the more traditional technology-based or performance-based regulations, and they conclude that managementbased regulation requires a far more complex intertwining of the public and private sectors than is typical of other forms of regulation, owing to regulators' need to intervene at multiple stages of the production process as well as to the degree of ambiguity over what constitutes good management.
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Assessing Consensus: The Promise and Performance of Negotiated Rulemaking
TL;DR: This article examined the impact of negotiated rulemaking on its two major purposes: (1) reducing rulemaking time; and (2) decreasing the amount of litigation over agency rules, concluding that formal negotiation can actually expand the range of potential conflicts in the regulatory process rather than reduce them.
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Regulating by Robot: Administrative Decision Making in the Machine-Learning Era
Cary Coglianese,David Lehr +1 more
TL;DR: This question is examined by considering whether the use of robotic decision tools by government agencies can pass muster under core, time-honored doctrines of administrative and constitutional law, and concludes that when machine-learning technology is properly understood, it can comfortably fit within these conventional legal parameters.
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