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

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  • Estrada, Mario Arturo Ruiz
  • Park, Donghyun
  • Staniewski, Marcin

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.

Suggested Citation

  • Estrada, Mario Arturo Ruiz & Park, Donghyun & Staniewski, Marcin, 2023. "Artificial Intelligence (AI) can change the way of doing policy modelling," Journal of Policy Modeling, Elsevier, vol. 45(6), pages 1099-1112.
    • Handle: RePEc:eee:jpolmo:v:45:y:2023:i:6:p:1099-1112
    DOI: 10.1016/j.jpolmod.2023.11.005
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    References listed on IDEAS

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    1. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation, and Work," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 197-236, National Bureau of Economic Research, Inc.
    2. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    3. Estrada, Mario Arturo Ruiz & Yap, Su Fei, 2013. "The origins and evolution of policy modeling," Journal of Policy Modeling, Elsevier, vol. 35(1), pages 170-182.
    4. Steve J. Bickley & Ho Fai Chan & Benno Torgler, 2022. "Artificial intelligence in the field of economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2055-2084, April.
    5. Ruiz Estrada, Mario Arturo, 2011. "Policy modeling: Definition, classification and evaluation," Journal of Policy Modeling, Elsevier, vol. 33(4), pages 523-536, July.
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