SE4030 Modeling and Analysis of Emergent System Behaviors

This course covers the construction of and reasoning about models of system behaviors, including any combination of software, hardware, humans, organizations, and surrounding environment.  Students will study concepts of emergence and work with selected Monterey Phoenix models to develop cognitive skills for detecting, predicting, classifying, and controlling emergent system behaviors.   Students will then deploy their new knowledge and skills to create their own system behavior models and assess them for the presence or absence of expected and unexpected system behaviors.  The concepts and skills taught in this course will help learners think critically and systematically about a system under design, probing automated output for tacit assumptions and overlooked expectations.  This course makes use of example system models across a wide range of application domains and invites students to apply their own personal and professional experiences with systems.

Prerequisite

None.

Lecture Hours

3

Lab Hours

2

Course Learning Outcomes

Upon successful completion of this course, students will be able to: 

• Explain the concept of system behavior and why it is a major design concern. 
• Describe the purpose of modeling system behaviors. 
• Apply scope to control the iteration of activities or objects. 
• Distinguish implementation mistakes (e.g., code bugs in the model) from cognitive biases (e.g., tacit assumptions about the modeled problem).   
• Predict and document emergent behaviors through inductive reasoning guided by subject matter knowledge and experience. 
• Apply feedback to improve teamwork efforts using team member peers and self-assessment. 
• Control emergent behaviors through changes in model logic and/or application of constraints.  
• Define probabilities for events in a given selection space. 
• Compute resource utilization using event attributes such as time, cost, weight, and power.
• Assign risk factors to events and query event traces for highest risk, lowest risk, and risk factor above or below a certain number. 
• Synthesize learning into a presentation of models, results, findings, and recommendations.