GEORGE WASHINGTON UNIVERSITY
 SCHOOL OF ENGINEERING AND APPLIED SCIENCE

 OR236 - Systems Thinking and Policy Modeling II
General Information
Course Description and Objectives
Text and Materials
Requirements and Grading
Schedule
Project
 

GENERAL INFORMATION

Instructor:            John H. Saunders, PhD, CKE  Work #: (202) 685-2078
E-mail:                 jsaunders@erols.com or saunders@ndu.edu

COURSE DESCRIPTION and OBJECTIVES

Case studies in dynamic policy analysis. Use of microcomputers in simulation. The class collectively
 models and simulates a social system to explore policy options.
Upon successful completion of this course, the student should be able to:
1. Model, both mathematically and visually, concrete and abstract dynamic structures of a system environment.

2. Provide a foundation for the consequences of feedback, time delay, and non-linear activity in problem analysis and synthesis.

3. Develop and test the influence of endogenous and exogenous policy change variables upon environmental performance.

4. Provide cases where stock and flow analysis has provided unique insight into major issues.

REQUIRED TEXTS AND MATERIALS
1)  ithink Strategy software v 5.1 from High Performing Systems. Manuals packaged with the software include: Getting Started with the ithink software and Technical Documentation, both 1997.
Other current articles may be assigned throughout the semester.
 
 COURSE REQUIREMENTS AND GRADING
1. Group Project (50%): Students will select entrance to a small group to expand their knowledge of general technology approaches for discovering patterns in systems. Each group will select from a list of alternative approaches for solving problems in operations research. The group will then be responsible for educating the class in the particular technology and implementing an example using the stock and flow methodology. The group will contrast traditional approaches with stock & flow technique. See the following detail project description. A joint grade will be assigned to all members of the group. Additionally a peer evaluation will be utilized to measure and evaluate group cooperation. Individual grades may be adjusted accordingly.

2. Class Participation (20%):  A great deal of the graduate learning experience is gained through a discussion of shared experiences and methods for improving quality and processes. In-class exercises and discussion will therefore be evaluated.  The criteria (from most important to least important) for judging a studentís contribution to the discussions are quality (well thought out, relevant to the topic), clarity, and frequency.

3. Final Exam (30%) A take home final will be issued one week before the end of class and will be due two weeks later.

The course grade of "B" represents the benchmark.  It indicates that the student has fulfilled all course requirements and demonstrated competency in the subject matter of the course.  Only those students who fully meet this standard and who demonstrate exceptional comprehension of the course subject matter, merit an "A".  Students who do not meet the benchmark standard of competency will earn the course grade of "C".  In those cases where there is substantial failure, the student will earn an "F". Plus and Minus grades are awarded.
 
SCHEDULE
 
Session/Date General Topic / Links Readings / Assignments Due 
1 Introduction, Basics Review  Technique Investigation Assignment
2 Connections with Chaos  Exercise
3 Break  Work on Presentation
4 Discrete Event Simulation   Exercise
5 Technology Contrast 1 - Genetic Algorithms  
6 Technology Contrast 2 - Petri Nets  
7 Technology Contrast 3 - Neural Networks  
8 Technology Contrast 4 - Cellular Automata  
9 Technology Contrast 5 - Constraint Optimization  
10 Applications/ Software  DC Symposium
11  Break /Final Posted Online  
12 Take Home Final Due  
 
 

Group Model Project

The purpose of the group project is to provide the student with an opportunity to explore the SD methodology in further detail.  The process to be followed is:

1) Students will investigate the operations research and artificial intelligence methods listed below - what types of challenges do they attack? How do the methods work? How is success utilizing the method measured?
2) Each group will then select one of the methods.
3) The group will then study the method in greater depth and the types of challenges it addresses. This would involve selecting simple challenges and utilizing the method to find solutions to those problems.
4) The group will then attempt to build an "engine" which addresses the same issues utilizing the stock and flow capability in ithink or a similar SD package. The group will record the challenges encountered in attempting to build this engine.
5) Providing a successful engine is built,  it should be contrasted to processes and results utilizing software specifically built for implementing the alternative technique. Is there a difference in results or greater difficulty in implementation?
6) The group will deliver a lecture which discusses their experience and where possible provides a hands-on learning experience for the audience.
Deliverables:
a) System Dynamics Model - small but effective model which would provide insight into the power or lack of power to represent and provide a solution to the complex problem area.

b) Paper - 5 - 7 page paper providing technology foundations, representative ability and conclusions. Written in a form to be distributed to other class members.

c) Presentation - 2 - 3 hour in-class. Presenters will review the foundations of the technology under study, contrast the technology  foundations to the precepts of system dynamics and provide a hands-on experience.

Possible Alternative Modeling Technology approaches follow: