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

with should consists of below 4 modules. Chapter is attached.

• CHAPTER SUMMARY: Summarize chapter presented during the week. Identify the main point (as in “What’s your point?”), thesis, or conclusion of the key ideas presented in the chapter.
• SUPPORT: Do research outside of the book and demonstrate that you have in a very obvious way. This refers to research beyond the material presented in the textbook. Show something you have discovered from your own research. Be sure this is obvious and adds value beyond what is contained in the chapter itself. 
• EVALUATION: Apply the concepts from the appropriate chapter. Hint: Be sure to use specific terms and models directly from the textbook in analyzing the material presented and include the page in the citation.
• SOURCES: Include citations with your sources. Use APA style citations and references.

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