Assessing Policy Effectiveness using AI and Language Models

Applications for Economic and Social Sustainability
2024
ISBN: 978-3-031-56096-5
Verlag: Springer Nature Switzerland

Buch, Englisch, 467 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 898 g

Reihe: International Series in Operations Research & Management Science

This volume uses advanced machine learning techniques to analyze government communication to evaluate policy effectiveness. The book develops policy effectiveness foundation models by cohorting historical budget policies with statistical models which are built on well reputed data sources including economic events, macroeconomic trends, and ratings and commerce terms from international institutions. By signal mining policies to the economic outcome patterns, the book aims to create a rich source of successful policy insights in terms of their effectiveness in bringing development to the poor and underserved communities to ensure the spread of wealth, social wellbeing, and standard of living to the common denomination of society rather than a selected quotient. Enabling academics and practitioners across disciplines to develop applications for effective policy interventions, this volume will be of interest to a wide audience including software engineers, data scientists, social scientists, economists, and agriculture practitioners.

Zielgruppe

Research

Autoren/Hrsg.

Vuppalapati, Chandrasekar

Fachgebiete

• Wirtschaftswissenschaften Volkswirtschaftslehre Wirtschaftspolitik, politische Ökonomie
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Chapter 1: Introduction.- Chapter 2 : Natural Language Models.- Chapter 3: Large Language Models.- Chapter 4 : Macroeconomic Indicators, Aggregates, and Framework.- Chapter 5 : Economic Sustainability.- Chapter 6 : Social Sustainability.- Chapter 7: Conclusion.

Über Autor(innen)

Chandrasekar Vuppalapati is a seasoned Software IT Executive with diverse experience in software technologies, enterprise software architectures, cloud computing, big data business analytics, internet of things (IoT), and software product and program management. He has held engineering and product leadership positions at Microsoft, GE Healthcare, Cisco Systems, St. Jude Medical, and Lucent Technologies. Chandrasekar has an MS in software engineering from San Jose State University (USA) and an MBA from Santa Clara

University (USA) and currently teaches software engineering, large-scale analytics, data science, mobile computing, cloud technologies, and web and data mining at San Jose State

University (USA).