Modelos cognitivos para la toma de decisiones en contextos de baja certidumbre

Despite considerable advances in the application of computational intelligence tools in decision making processes, decision makers continue to struggle to keep pace with complex and uncertain phenomena such as pandemics, climate change and geopolitics. In recent years, the sharp increase and availability of modern computational intelligence tools such as automated data collection methods, satellite imagery processing, machine learning, artificial intelligence and highly disaggregated simulation models has attracted the attention of decision scientists as these tools present a potential opportunity to generate, collect and analyze new data that can enhance decision makers’ capacities to respond to rapidly evolving decision environments. From this point of view, computational intelligence tools (CITs) could be used as cognitive prosthetics that expand decision makers’ cognitive bandwidth, such that they can use more efficiently their cognitive resources in addressing uncertainty and complexity in decision situations.

This study merges behavioral experimentation and neuroscientific methods to develop an integrated cognitive model of decision-making under uncertainty and complexity. We argue this interdisciplinary approach can contribute to: i) objectively illustrate decision makers’ models of beliefs and values, (ii) support Data Science interventions in crisis situations, and (iii) contribute to the development of modern decision sciences. Ultimately, this integrative approach can result in formal cognitive models of decision making under uncertainty and complexity that will grant the scientific community a deeper understanding of mechanisms by which data science technologies and decision makers interact under rapidly evolving environments.