This methodology uses neural networks (e.g. GANs) and large language models (LLMs) to generate synthetic audiences, simulating consumer behavior, preferences and responses without human intervention. It is based on deep learning and social simulation theory, training algorithms on historical Big Data (social networks, transactions) to replicate demographic, psychographic and cultural patterns. Academically, it aligns itself with computational epistemology and data ethics, questioning biases inherent in traditional models.

It aims to predict reactions to hypothetical scenarios (e.g. disruptive launches) or to test strategies in controlled environments (e.g. synthetic image crisis). Demands include scalable speed (testing in hours), reducing logistical costs and mitigating ethical risks (e.g. avoiding the collection of sensitive data). Key applications: simulating responses to controversial campaigns, stress testing brand positioning or generating personas for non-existent niches (e.g. post-climatic futures markets).

It is indicated in exploratory R&D stages, scenarios of high uncertainty (e.g. pandemics) or to validate hypotheses in global markets without investment in fieldwork. Advantages:

1. Agility (unlimited iterations);

2. Statistical Control (precise manipulation of variables);

3. Elimination of Declaratory Bias (answers not filtered by social desirability).

Methods such as agent-based modeling make it possible to simulate the dynamics of mass adoption or network effects.

Companies report a 70% reduction in concept validation time and a 40% increase in predictive accuracy compared to traditional methods. Examples: brands that have adjusted slogans globally after tests with multicultural synthetic avatars. Critical studies warn of the risks of cultural overfitting, requiring governance frameworks.

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