A HYBRID FRAMEWORK INTEGRATING COMPUTATIONAL THINKING INTO STRATEGIC FORESIGHT

Abstract

In the era of accelerated technological dislocation and complicating global interdependencies, organizations must anticipate rather than merely react to change. Strategic foresight has had a long time offering the conceptual and methodological foundations for projecting alternative futures; however, its qualitative orientation regularly shortens analytical perception and systemic validation. This article proposes a novel hybrid method that integrates computational thinking (CT) into strategic foresight methodology. Relying on the core CT concepts of decomposition, abstraction, algorithmic design, and iterative testing, the new methodology enhances traditional foresight methodologies by adding formal modeling and simulation-based investigations. This enables better mapping of interdependencies among systems, sensitivity analysis of main variables, and analysis of emergent behavior in future systems.

To illustrate the conceptual validity of this approach, an exploratory case on the global energy transition is presented. This case demonstrates how CT enables the operationalization of esoteric foresight challenges through the facilitation of systemic mapping, computer simulation, and iterative refinement of strategic alternatives. The proposed framework is intended to augment the credibility and transferability of foresight outcomes while maximizing participatory engagement through model transparency.

The paper contributes to the new research on computational foresight in mediating qualitative study and quantitative simulation, offering both conceptual foundation and methodological stream for developing more scientific and robust foresight systems.