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Review of Artificial Intelligence Applied in Decision-Making Processes in Agricultural Public Policy - 2020

Review of Artificial Intelligence Applied in Decision-Making Processes in Agricultural Public Policy

Research Area:  Artificial Intelligence

Abstract:

The objective of this article is to review how Artificial Intelligence (AI) tools have helped the process of formulating agricultural public policies in the world. For this, a search process was carried out in the main scientific repositories finding different publications. The findings have shown that, first, the most commonly used AI tools are agent-based models, cellular automata, and genetic algorithms. Secondly, they have been utilized to determine land and water use, and agricultural production. In the end, the large usefulness that AI tools have in the process of formulating agricultural public policies is concluded. The conceptual framework of this article consists of 3 areas of knowledge that are public policy, Artificial Intelligence (AI) and decision making, specifically framed in the agricultural sector. For a better understanding, the concept of public policy and its decision-making process are initially presented, as is how artificial intelligence has been applied in decision-making processes. In order to understand the concept of public policy, [1] explains that there are the strategies and norms composed and implemented to solve collective problems and that the creation of these policies is based on social interest and the improvement of the quality of life. Therefore, all public policy generation processes basically have three stages: formulation, implementation, and evaluation. The formulation stage determines the best solutions to find the best implementation solution; thus, it is of utmost importance that the decision-making process in public policy is as assertive as possible. For this reason, various authors have elaborated several models on the decision-making process in this area, the main ones are summarized below.

Keywords:  

Author(s) Name:  Juan M. Sánchez, Juan P. Rodríguez, Helbert E. Espitia

Journal name:  Processes

Conferrence name:  

Publisher name:  MDPI

DOI:  https://doi.org/10.3390/pr8111374

Volume Information:  Volume 8(11), Pages 1374