Artificial Intelligence (AI) can change the way of doing policy modelling

Mario Arturo Ruiz Estrada, Donghyun Park and Marcin Staniewski

Journal of Policy Modeling, 2023, vol. 45, issue 6, 1099-1112

Abstract: This paper seeks to assess the transformative potential of Artificial Intelligence (AI) in policy modeling. Rapid advancements in AI, encompassing algorithms, advanced programming software, robotics, metadata, sophisticated mathematical models, neural networks, and graphical models are ushering in innovative new research methods for analysing and resolving intricate socio-economic issues. Our focus lies in a comparative evaluation of Artificial Intelligence Response (AIR) versus Human Intelligence Response (HIR) in generating swift and potent solutions to various socio-economic challenges. To achieve this, we propose a fundamental model for appraising the effectiveness of policy modeling, known as the "Policy Modeling Response Evaluator (PMR-Evaluator)." Furthermore, we conducted an experiment to gauge the responsiveness and effectiveness of both AIR and HIR. This experiment revolved around addressing a specific socio-economic problem, namely controlling inflation. Initially, we scrutinized responses from an extensive database of papers published in the Journal of Policy Modeling (JPM) by Elsevier over the past forty-five years (1978–2023) to ascertain HIR's capacity to analyze and resolve inflation-related issues. Concurrently, we utilized ChatGPT, a powerful artificial intelligence application (AI-APP), to explore potential solutions for controlling inflation. Ultimately, we analyzed whether HIR or AIR proved more effective and precise.

Keywords: Policy modelling; Multidimensional graphical modeling; Learning machine; Artificial intelligence; APD-Maker; Multidimensional coordinate spaces; Quantitative mega-data visualization (search for similar items in EconPapers)
JEL-codes: Z28 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jpolmo:v:45:y:2023:i:6:p:1099-1112

DOI: 10.1016/j.jpolmod.2023.11.005

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