This document discusses systems modeling and its use in decision making. It covers the evolution of modeling from early legacy approaches to recent innovations utilizing increased computing power, big data, and sophisticated analytics. Specifically, it describes how modeling has advanced from focusing on feedback and accumulations to now enabling multi-method and hybrid approaches through interdisciplinary study. These evolutions allow for more robust modeling of complex systems to support adaptive decision making.

1. ITS 832 CHAPTER 5
FROM BUILDING A MODEL TO ADAPTIVE ROBUST
DECISION MAKING USING SYSTEMS
MODELING
INFORMATION TECHNOLOGY IN A GLOBAL ECONOMY
DR. JORDON SHAW
INTRODUCTION
• Systems modeling
• Focus on decision making abilities
• Legacy System Dynamics (SD) modeling
• 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)

2. • 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
• 1950s – Jay W. Forrester
• Primary characteristics
• Feedback effects – dependent on their own past
• Accumulation effects – building up intangibles
• 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

3. • Two primary evolutions
• Smarter methods (Data Science)
• Usability/accessibility advances
WHAT THE FUTURE HOLDS
• Better models
• More data (“Big Data”)
• Social media
• Advanced capabilities for
• Hybrid modeling
• Simultaneous modeling
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

4. SUMMARY
• Modeling has long been used with complex systems
• 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