Finding the Path Toward Design of Synergistic Human-Centric Complex Systems

Modern decision support systems are becoming increasingly sophisticated due to the unprecedented volume of data that must be processed through their underlying information architectures. As advances are made in artificial intelligence and machine learning (AI/ML), a natural expectation would be to assume that the complexity and sophistication of these systems will become daunting in terms of comprehending their design complexity, effective operations, and managing total lifecycle costs. Considering the fact that such systems operate holistically with humans, the interdependencies created between the information architectures, AI/ML processes and humans begs that a fundamental question be asked –“how do we design complex systems such as to yield and exploit effective and efficient human-machine interdependencies and synergies?” A simple example of these interdependencies may include the effects of human actions changing the behavior of algorithms and vice-versa. The algorithms may serve in the extraction and fusion of heterogeneous data, employ a variety of AI/ML algorithms that range from hand crafted, supervised and unsupervised approaches coupled with federated models and simulations to reason and infer about future outcomes.