Understanding Complex Systems Through Computational Models
From disease transmission and transportation networks to energy systems and supply chains, many of society's most pressing challenges emerge from the interactions of millions of individuals, organizations, and infrastructures. Understanding these systems requires more than isolated data points—it requires models that capture how components interact and how those interactions give rise to large-scale behavior.
Researchers at the Biocomplexity Institute develop computational models that represent biological, social, technological, and engineered systems across multiple scales. These models provide a framework for exploring relationships, identifying vulnerabilities, and generating insights that would be difficult or impossible to obtain through observation alone.
Our work spans agent-based models, network models, graph dynamical systems, statistical models, and hybrid approaches that integrate diverse data sources and scientific disciplines. By combining domain expertise with mathematics, computer science, and data science, we create models that help decision makers better understand complex systems and prepare for an uncertain future.
Applications
- Infectious disease transmission
- Supply chains and critical infrastructure
- Energy systems and communities
- Transportation and mobility
- Workforce and economic systems
- Food systems and agriculture
- Information diffusion and social networks
Representative Projects
Explore the projects below to see how BI researchers use computational modeling to address challenges in health, national security, infrastructure, and societal resilience.
