Complexity-based Thinking in Systems Intelligence for Systems Resilience

摘要

We posit that our models and approaches in systems resilience persistently demonstrate fragmented and dispersed knowledge because we fail to fully perceive the complexities of our systems and the situations that daunt them. We argue for a systems intelligence that has complexity-based thinking at its foundation. Complexity-based thinking involves methodological pluralism, law of requisite knowledge, and complexity absorption. The system integrates knowledge from heterogeneous sources, namely, massive information data points, expert and experiential knowledge, and perceptions of human sensors. As new facts are continuously derived with incoming evidence, the intelligent system self-improves its knowledge. With the synergism of heterogeneous knowledge, the emergence of new intelligence is possible. The integrated knowledge may expose unstated assumptions, reconcile inconsistencies and conflicts, and elucidate ambiguities in complex system behavior. The integrated knowledge is also aimed to influence the course of system vulnerabilities, destructive perturbations, and critical systemic changes.