| Summary: | The future is to be built – is multiple and uncertain. Within the social sciences, scenarios can be defined as a description of a future situation and a course of events that allow move from a primary position toward this future situation. Currently, there is a multiplicity of methods and tools available for building scenarios, including methods of an essentially rationalist approach, as Michel Godet’s method. The purpose of this work is to use the hypothetical-deductive method to reduce, starting from Michel Godet’s Scenario Method and its tools, the complexity of the scenario-building process while maintaining the robustness of the findings. For this purpose, it is proposed two different approaches: (1) to integrate, in one step, the structural analysis and the cross-impact matrix so the first one derives automatically while filling the last one; (2) to use the concept of Bayesian networks as a method to integrate the cross-impact matrix and the morphological analysis. Both approaches aim to reduce the amount of information needed to feed the tools and improve the feedback criteria, resulting in greater flexibility during the process and better holistic view of the system. Scientifically, these approaches open a new field of studies in scenario planning as it appropriates the concept of Bayesian networks, widely used in other areas of knowledge (artificial intelligence, geological studies, medical diagnostics, pattern classification, etc.), and bring it to the field of social sciences.
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