Spring 2009 One Page Flyer

SySc 510: Models in the Natural & Social Sciences

This interdisciplinary course focuses on the concept of models in science. What is a model? How do models fit into the scientific method? What is common to all scientific models, regardless of discipline. What are the strengths and weaknesses of models as tools for understanding scientific problems? What makes one model better than another? What types of modeling techniques are available and which are best for investigating different types of scientific questions. These are the types of questions students will address by studying established models and by developing their own models in an area of their interest. The goal is for students to achieve both a better conceptual understanding of how models are used in science, as well as enhanced practical skills in using modeling methods and tools.

SySc 513: Systems Approach

Provides a practitioner-oriented introduction to systems, including:

  • observer dependencies & context
  • meta-systems & subsystems
  • value systems and associated optimization/sub-optimization
  • life-cycle project management
  • inquiring systems
  • learning organizations
  • multiple perspectives.

Also explores qualitative aspects systems analysis tools such as graphs, structural modeling, and dynamic modeling.

For more information: Systems Approach Course Description

SySc 527/627: Discrete System Simulation

The mathematical basis for discrete system simulation (DSS) is probability theory and queuing theory. It is used extensively in the fields of operations research, civil engineering, industrial engineering, systems analysis, etc. Students learn how to use DSS to model systems of interest.


SySc 553/653: Manufacturing System Simulation

The course focuses on using the ProModel discrete event simulation software to model manufacturing systems. Concepts include: a) overview of discrete system simulation and manufacturing simulation, b) data collection and prob. distributions, c) modeling material handling systems, d) job shop and production planning applications, and e) experimental design and output analysis. Relevant aspects of ProModel are also covered: locations, entities, processing logic, arrivals, path networks, resources, etc.

The course is designed to be of interest to students in Business, Engineering Management, Systems Science, Systems Engineering, and other programs; and to professionals in manufacturing, manufacturing engineering, and industrial engineering.

SySc 557/657 Artificial Life

"Artificial Life" (ALife) is a name given to theoretical, mathematical, and computationally "empirical" studies of phenomena commonly associated with "life," such as replication, metabolism, morphogenesis, learning, adaptation, and evolution. It focuses on the materiality-independent, i.e., abstract, bases of such phenomena. As such, it overlaps extensively with "theoretical biology" and, less extensively, with certain areas of physics and chemistry and the social sciences. It also raises important philosophical questions. It is part of a larger research program into "complex adaptive systems," one stream of contemporary systems theory.

In its intersection with computer science, ALife is the newest example of "the sciences of the artificial" (Herbert Simon). ALife is to life what AI is to intelligence. Christopher Langton writes that "Artificial Life ... complements the traditional biological sciences ... by attempting to synthesize life-like behaviors within computers and other artificial media." The purpose is twofold: to understand these phenomena better and to develop new computational technologies.

The course will sample the research literature in this field, and will be organized in a seminar format. Topics to be emphasized are: (1) discrete dynamics: cellular automata and random networks, (2) ecological & evolutionary dynamics, (3) genetic algorithm optimization and adaptation, (4) agent-based simulation. Other topics will include: artificial and real chemistry (metabolism, reproduction, & origin of life), "complex adaptive systems," autonomous agents, and philosophical issues.

SySc 576: AI: Neural Networks II

MW 4:00-5:50, Room TBA
George Lendaris, 725-4988 lendaris@sysc.pdx.edu

Focuses on applications. Topics in fuzzy set theory, control theory, and pattern recognition are studied and incorporated in considering neural networks. A Design project (using NN simulator) in selected application area is done by each student.

More Information: Neural Networks II, Spring 2008