An Institutional Theory Framework for Leveraging Large Language Models for Policy Analysis and Intervention Design

J. de Curtò (), I. de Zarzà, Leandro Sebastián Fervier, Victoria Sanagustín-Fons and Carlos T. Calafate
Additional contact information
J. de Curtò: Department of Computer Applications in Science & Engineering, BARCELONA Supercomputing Center, 08034 Barcelona, Spain
I. de Zarzà: Estudis d’Informàtica, Multimèdia i Telecomunicació, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
Leandro Sebastián Fervier: Departamento de Psicología y Sociología, Universidad de Zaragoza, 50009 Zaragoza, Spain
Victoria Sanagustín-Fons: Departamento de Psicología y Sociología, Universidad de Zaragoza, 50009 Zaragoza, Spain
Carlos T. Calafate: Departamento de Informática de Sistemas y Computadores, Universitat Politècnica de València, 46022 València, Spain

Future Internet, 2025, vol. 17, issue 3, 1-28

Abstract: This study proposes a comprehensive framework for integrating data-driven approaches into policy analysis and intervention strategies. The methodology is structured around five critical components: data collection, historical analysis, policy impact assessment, predictive modeling, and intervention design. Leveraging data-driven approaches capabilities, the line of work enables advanced multilingual data processing, advanced statistics in population trends, evaluation of policy outcomes, and the development of evidence-based interventions. A key focus is on the theoretical integration of social order mechanisms, including communication modes as institutional structures, token optimization as an efficiency mechanism, and institutional memory adaptation. A mixed methods approach was used that included sophisticated visualization techniques and use cases in the hospitality sector, in global food security, and in educational development. The framework demonstrates its capacity to inform government and industry policies by leveraging statistics, visualization, and AI-driven decision support. We introduce the concept of “institutional intelligence”—the synergistic integration of human expertise, AI capabilities, and institutional theory—to create adaptive yet stable policy-making systems. This research highlights the transformative potential of data-driven approaches combined with large language models in supporting sustainable and inclusive policy-making processes.

Keywords: data-driven policy analysis; institutional theory; decision support systems; visual analytics; AI; large language models; predictive modeling; intervention design; graph neural networks (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/17/3/96/pdf (application/pdf)
https://www.mdpi.com/1999-5903/17/3/96/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:17:y:2025:i:3:p:96-:d:1595506

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().