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Courses One Page Flyer Spring 2010

SySc 510: Systems Ideas & Sustainability


This course examines systems-theoretic ideas about sustainability.  Graph theory, non-linear dynamics, game and decision theory, thermodynamics, and theories of complexity and complex adaptive systems (CAS) offer insights into the challenge of sustainability and suggest principles that can help us meet this challenge.  These insights and principles will be explored in this course primarily via four texts.  Though not explicitly a ‘systems’ book, The Ecology of Commerce gives an overview of the sustainability challenge, invoking a variety of systems ideas. Complexity Theory for a Sustainable Future is a recent anthology of articles about sustainability drawing directly upon the complexity and CAS literature, especially dynamic systems, networks, and ideas about uncertainty and information.  Limits to Growth: The 30 Year Update revisits and updates one of the first books to raise public awareness of sustainability challenge in the 1970s.  Panarchy describes the work of C.S. Holling and colleagues on the dynamics of transformations in ecological and social systems.  The course begins with a talk1 introducing some systems ideas relevant to sustainability and locates the sustainability challenge within a larger macro-historical systems-theoretic perspective.2

1 http://www.sysc.pdx.edu/download/papers/sustain07.pdf 
2 http://www.pdx.edu/sites/www.pdx.edu.sysc/files/media_assets/hhh_sp.pdf

 

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 552: Game Theory


Game theory involves the study of cooperation and competition among self-interested agents. It provides a theoretical framework for reasoning about a wide variety of phenomena, e.g., from who pays for dinner on a date or what strategy to adopt in an eBay auction, to more weighty issues such as strategies for combating global warming or the biological conditions necessary for multicellular cooperation in animals and plants.

Emphasis in this course is on understanding the findings of game theory and its usefulness in analyzing a variety of interesting phenomena, rather than on the purely technical aspects of the theory. The course presents the basic ideas of game theory, starting with how to represent and classify different kinds of interactions in terms of archetypal game structures. Of special interest are those paradoxical situations (games) where individual rationality (self-interest) leads to a collectively irrational outcome, such as in Prisoner’s Dilemma or Tragedy of the Commons type interactions.

Game theory is useful across a broad range of scientific disciplines because situations involving conflicts of interest and potential benefits for cooperation are ubiquitous, and because the meaning of both "players" and "benefits" are very general. For example, players could be competing genes where benefit is measured in the number of copies in future generations or players could be negotiating countries where benefits are measured in trade surpluses. Game Theory is especially relevant to the disciplines of Economics, Biology, Computer Science, Math, Political Science, Sociology, and Psychology.

In addition to Game Theory basics (and depending on student interest) advanced topics may include:
 
•    Evolutionary Game Theory
•    Agent-based Modeling
•    Auction Theory
•    Mechanism Design
•    Social Choice Theory
 
•    Coalition Theory
•    Resource Management and Sustainability Applications
•    Folk Theorem
•    Bayesian Games
 

Prerequisites: Basic Algebra, Probability, and Logic skills

 

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 2010