From Building a Model to Adaptive Robust Decision Making Using Systems Modeling

ITS 832 Chapter 5

From Building a Model to Adaptive Robust Decision Making Using Systems Modeling

InformationTechnology in a Global Economy

Professor Miguel Buleje

Introduction • Modeling & Simulation

• Fields that develops and applies computational methods to address complex system

• Addresses problems related to complex issues

• Focus on decision making abilities

• Opportunities to leverage interdisciplinary approach, and learn across fields to understand complex systems.

• Legacy System Dynamics (SD) modeling and others methods are presented

• Recent innovations

• What the future holds

• Examples

Systems Modeling

• Dynamic complexity • Behavior evolves over time

• Modeling Methods • System Dynamics (CD) • Discrete Event Simulation (DES) • Multi-actor Systems Modeling (MAS) • Agent-based Modeling (ABM) • Complex Adaptive Systems Modeling (CAS)

• Enhanced computing supports model based decision making • Modeling and simulation has become interdisciplinary

• Operation research, policy analysis, data analytics, machine learning, computer science

Legacy System Dynamics Modeling

• 1950’s – Jay W. Forrester

• Primary characteristics • Method to model complex systems or issues

• Feedback effects – dependent on their own past

• Accumulation effects – building up intangibles/ mental or other states for a complete model.

• Behavior of a system is explained • Casual theory – model generates dynamic behavior

• Works well when: • Complex system responds to feedback and accumulation

Recent Innovations

• Detailed list of individual innovations

• Deep uncertainty • Analysts do not know or cannot agree on

• Model

• Probability distributions of key features

• Value of alternative outcomes

• Two primary evolutions: • Smarter methods (Data Science)

• Usability/accessibility advances

What the Future Holds

• Better models, as a result of technology innovation

• More data (“Big Data”)

• Social media

• Advanced capabilities for: • Hybrid Modeling: mixing and matching models.

• Simultaneous Modeling

• Modeling multiple models or uncertainty

• Adopting all recent innovations and opportunities would bring the future state in Modeling and Simulation, as presented in Fig. 5.1

Modeling and Simulation

Examples

• Assessing the Risk, and Monitoring, of New Infectious Diseases

• Simple systems model with deep uncertainty

• Integrated Risk-Capability Analysis Under Deep Uncertainty

• System-of-systems approach

• Policing Under Deep Uncertainty • Smart model-based decision support system

Summary

• Modeling has long been used with complex systems and issues / simulation.

• Recent evolutions have advanced modeling • Increase computing power

• Social media and Big data

• Sophisticated analytics

• Multi-method and hybrid approaches are now feasible

• Continued move into interdisciplinary study • Advanced modeling for complex systems • Operation research, policy analysis, data analytics, machine

learning, computer science