Public Service Innovation: Data-driven Management, Productive ecosystems, Driving Sectors and the Optimizer

Main Article Content

Osvaldo Alvarado
Jhon Fonseca

Abstract

This article aims to understand the requirements of productive ecosystems and identify key elements for the emergence and success of companies or sectors in specific regions, seeking an efficient use of each area by contrasting it with the characteristics of regions and communities, identifying areas conducive to sectoral development. It also addresses the implementation of public policies, prioritizing the "rescue of talent" as a catalyst for broader positive effects, including innovation and sustainable development. The methodology used includes an exhaustive literature review to understand previous treatments of productive ecosystems. The Optimizer of Productive Ecosystems (Opt-EP), a Monte Carlo-based heuristic algorithm that identifies productive activities with high potential and impact in a region, is presented.  The results present Opt-EP as an advanced artificial intelligence tool specialized in analyzing the "Productive DNA" of regions. Using heuristic algorithms, Opt-EP delves into socio-economic, cultural, environmental, and economic dynamics, decomposing measurements to obtain a more detailed view. The integration of the Productive DNA concept provides a solid basis for designing public policies tailored to the unique characteristics of each region, underscoring the importance of identifying and nurturing the intrinsic capabilities of each area to achieve authentic and sustainable economic development.

Article Details

How to Cite
Public Service Innovation: Data-driven Management, Productive ecosystems, Driving Sectors and the Optimizer. (2024). Yulök Revista De Innovación Académica, 8(1), 10-30. https://doi.org/10.47633/2yvhfy42
Section
Artículo científico

How to Cite

Public Service Innovation: Data-driven Management, Productive ecosystems, Driving Sectors and the Optimizer. (2024). Yulök Revista De Innovación Académica, 8(1), 10-30. https://doi.org/10.47633/2yvhfy42

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