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An interdisciplinary concept for analyzing and designing complex systems that explains emergence, nonlinearity, and self-organization in technical and organizational contexts.
Complexity science studies how local interactions among many components produce emergent patterns, self-organization, and nonlinear behavior. It provides conceptual models and methods (e.g. networks, agent-based models, feedback loops) for analyzing, anticipating, and designing complex technical, organizational, and socio-ecological systems. Applications span infrastructure, enterprises, and ecosystems.
Probability that a system fails under defined conditions.
Measure of centrality and dependencies in the system network.
Duration until system indicators return to an acceptable range.
Analysis of service dependencies in a distributed architecture to identify central nodes.
Simulating user interactions to estimate load spikes and emergent usage patterns.
Modeling organizational decision paths to test governance changes for stability.
Identify stakeholders and define modeling objectives.
Integrate data sources and build initial simple models.
Simulate scenarios, validate, and incrementally deploy into practice.