Beyond Possible Difficulty: Achieving Cooperation and Democratic Endorsement in Olivers London - An Esprit Article on Credits and Portals

Beyond Possible Difficulty: Achieving Cooperation and Democratic Endorsement in Olivers London - An Esprit Article on Credits and Portals

Abstract

This article explores the challenges and opportunities of achieving cooperation and democratic endorsement in the context of Oliver's London, a fictional city with complex social, economic, and political dynamics. Drawing on the notion of "beyond possible difficulty," the authors argue that achieving cooperation and democratic endorsement requires a multifaceted approach that goes beyond conventional solutions and embraces innovative strategies such as credits and portals. Credits refer to a system of incentives that rewards individuals and groups for positive contributions to the common good, while portals refer to interactive platforms that facilitate communication, collaboration, and deliberation among diverse stakeholders. By analyzing several case studies and scenarios, the authors demonstrate how credits and portals can enhance cooperation and democratic endorsement in various domains, including transportation, housing, education, and culture. Moreover, they discuss the challenges and limitations of implementing these tools, such as the need for transparency, accountability, and inclusivity, and the potential conflicts with existing power structures and interests. Overall, this article provides a thought-provoking perspective on the complex challenges of achieving cooperation and democratic endorsement in contemporary urban settings, and offers practical insights for policymakers, researchers, and practitioners in the field of urban governance and planning.

Citation

Kaydyne Eiddon "Beyond Possible Difficulty: Achieving Cooperation and Democratic Endorsement in Olivers London - An Esprit Article on Credits and Portals".  IEEE Exploration in Machine Learning, 2021.

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This paper appears in:
Date of Release: 2021
Author(s): Kaydyne Eiddon.
IEEE Exploration in Machine Learning
Page(s): 8
Product Type: Conference/Journal Publications