Courses One Page Flyer Fall 2010

SYSC 346U  (also CS 346U) Exploring Complexity in Science and Technology

Freedom Privacy and Technology Cluster



Much of our scientific knowledge is based on two seemingly reasonable assumptions: 1) if we understand the parts of a system, we will understand the whole, and 2) small changes to a systems will have small effects, and big changes will have big effects. These assumptions turn out to be inadequate for many of the complex systems we interact with everyday (e.g. the weather, the economy, our biological environment, and the many social networks to which we belong). The goal of this course is to explore some of the most interesting and useful concepts behind the behavior of complex systems (without getting too bogged down in the scientific and mathematical details).



SYSC 399U, Models in Science

Universitiy Studies Cluster Course for Science in the Liberal Arts 



This interdisciplinary course focuses on the role of models in scientific inquiry. Students explore how scientists from a variety of disciplines use different types of models, including physical (scale), mathematical (analytic and numeric), agent-based, and animal. To facilitate this exploration, the course is divided into three main sections.

  1. Definition: We compare different definitions of “science,” “the scientific method,” and “model.” Here we also look briefly at what philosophers of science have said about how models fit into scientific inquiry.
  2. Deconstruction: Students critically analyze a variety of models used in the research literature from different disciplines. Key questions include: What are the strengths and weaknesses of models as tools for posing and answering scientific questions? Which types of modeling techniques are best for investigating different types of scientific questions? What common issues apply to the use of all scientific models? How should one evaluate models in terms of the tension between fidelity to the “real world” and their necessary (and desirable) simplifications? What validation procedures are necessary to increase confidence in model generated results?
  3. Construction: Students identify a scientific question of interest and propose a hypothesis that a model could help to test. They then design, construct, and use their model to address their question. This construction phase defines the main course project which is staged over the term.

The course provides both a conceptual understanding of how models are used in science and “hands on” experience conducting scientific inquiry using models as tools.



SySc 512: Quantitative Methods of Systems Science

This class presents a unified introduction to quantitative concepts for all students of Systems Science. It will be offered in a dual track format, allowing students to self select the type of assignments to be completed based on their mathematical background. The theories of dynamic systems, stochastic processes, and optimization will be surveyed in the context of modeling, control and classification problems. Students will gain perspective on issues such as discrete versus continuous systems, types of system representations and linear versus non-linear systems. Topics will include concepts such as controllability, observability, system response, random variables and techniques for gradient and nongradient optimization.

For more information: Quantitative Methods of Systems Science Course Description


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


Courses: SYSC 521/621: Systems Philosophy

This seminar will consider some philosophical issues central to the systems field. Fundamental to these issues is Bunge's conception of systems science as a research program aimed at the construction of an exact and scientific metaphysics, that is, a set of concepts, models, and theories of broad generality and philosophical import, which are applicable to the sciences, and which are cast (or capable ultimately of being cast) in the exact language of mathematics.

The course will present a broad range of systems ideas (from information theory, game theory, thermodynamics, non-linear dynamics, decision theory, and many other areas) and attempt to integrate these ideas into a coherent framework. These ideas will be organized around the theme of fundamental "problems," that is, difficulties (imperfections, modes of failure) encountered by many systems of widely differing types. While most of these ideas are mathematically-based, they will be approached in this course primarily at a conceptual level (with mathematical details provided as requested). Many of these systems ideas derive from the natural sciences and engineering, but they apply as well to the social sciences and to fields of professional practice (business, the helping professions, etc.). It is primarily their relevance to the human domain--to individuals, groups, organizations, and societies ¿ and to technology which motivates this theoretical/philosophical inquiry. Certain of these ideas pertain also to the arts and humanities.

This course draws from the literature of general systems theory and cybernetics, which launched the systems research program, and from the literature of chaos, complexity, and complex adaptive systems which continues this program today. While the contemporary renaissance of systems theory has brought major advances, the older "classical" tradition of GST/cybernetics articulated the systems project in a deeper way. Seminal writings of both classical and contemporary systems scientists (e.g., Boulding, Deutsch, Emery & Trist, Jantsch, Laszlo, Bateson, Wiener, Holland, Gell-Mann, Crutchfield, Arthur) will be discussed.

Readings will be from (1) the manuscript of a book (working title: Elements and Relations) being written by the instructor, which attempts the integration spoken of above, (2) a collection of xeroxed articles and selections from books, and (3) a Scientific American Reader in Systems Theory & Complex Systems, all obtainable at SmartCopy, 1915 SW 6th (227-6137).

Course work: term paper (25 dbl.-sp. pages [non-mathematical papers]+ bibl.); class participation; supplementary short writing assignments

Prerequisites: graduate status in Systems Science or permission of instructor. This is a seminar course with limited enrollment, so SySc students have first priority.

Information on Systems Philosophy research


 Fall 2006 Syllabus

SySc 525/625: Agent Based Simulation

This course focuses on the technical and theoretical aspects of agent-based programming. During this class students will learn how to use StarLogo to create agent-based models and use agent-based simulations in research and education. Reading assignments focus on the history and theories behind agent-based programming and the decentralized perspective.

For more information:


SySc 529/629: Business Process Modeling & Simulation

The primary emphasis is on using discrete system models to analyze administrative, decision-making, product development, manufacturing, and service delivery processes. Discrete system models characterize the system as a flow of entities that enter and move through various processes and queues according to probability functions specified by the modeler. Monte Carlo sampling is used to calculate statistical measures of system performance, such as throughput, average queue length, resource utilization, etc. Some processes may also exhibit continuous characteristics, in which case continuous model constructs may be deployed. Continuous system models utilize the numerical integration of differential equations to simulate behavior over time. Such models are often used for studying the systems containing feedback loops, where the outputs are "fed back" and compared with control inputs. Process measurement and the unique challenges of modeling the software development process will also be covered in some detail.

For more information: