代写SYSEN 6000: Foundations of Complex Systems Syllabus - Fall 2024代做Python编程

SYSEN 6000: Foundations of Complex Systems

Syllabus - Fall 2024

1. Course Description:

The complexity of the systems we create has grown exponentially, to the point where no single human is capable of grasping even a substantial part of the entire system. This raises important challenges—for example, designing a system that no one understands may lead to failures due to unanticipated emergent behavior. At several points in the 20th century, scientists have attempted to define a “general theory of systems” that would transcend disciplines and shed light into how emergent behavior. arises from the interactions between system components. This course reviews the foundations of complex systems, surveying several theories that are meant to explain and predict the behavior. of complex systems, as well as tools and methods to help us engineer those complex systems.

2. Learning Outcomes:

The principal aim of this course is to give students the theoretical foundations and toolset required to analyze and, in some cases, predict, design, and optimize the behavior. of complex systems.

By the end of this course, students will:

•   Understand the key concepts of systems theory: complexity (including its architecture and the theories to quantify it), emergence, entropy/information, and others.

•   Understand the duality between systems, graphs, and matrices and be able to apply network theory to characterize systems.

•   Understand the main concepts of non-linear dynamics and chaos including bifurcations, chaotic oscillations, attractors, multi-stability, and others.

•   Understand the main concepts of network theory including scale-free, small-world, centrality measure, etc.

•   Understand the main characteristics of human behavior. in decision-making, including the theory of bounded rationality and basic precepts of game theory (zero-sum games, Nash equilibrium).

•   Understand how complex systems can evolve and adapt to their environment.

•   Use deterministic and stochastic global optimization methods to optimize the design or operation of a complex system with and without uncertainty.

•   Use statistical learning techniques to predict the behavior. of complex systems.

•   Understand how intelligent behavior. emerges in natural and engineered systems.

•   Understand how complex behavior. can arise from simple agents and interactions as well as collaboration strategies.

3. Class Format and Procedures:

The course is organized in several modules, each taught by a different instructor, and usually consisting of one or two lectures (usually sequential but not always). For each module, the students must do substantial reading in advance. Each module will introduce related concepts within a general topic such as complexity, emergence, collective behavior, self-organization, evolution and adaptation, and pattern formation. Students will learn to use a variety of mathematical tools to analyze these systems, such as nonlinear dynamics and controls, game theory, network theory, mixed-integer programming, evolutionary computation, genetic algorithms, supervised and unsupervised learning techniques, deep learning, and control systems. Students will be required to do a substantial amount of reading, and there will be roughly weekly homework and a final project related to one or more of the topics covered.

Course participation is essential for the success of the course, and therefore it will contribute to the student’s grade in the course.

There will be (roughly) weekly homework. Homework will be submitted via Canvas.

4. Course Requirements:

• Reading and in-class questions: Students will be required to do substantial reading for this course. Some lectures will have short quizzes at the beginning concerning the readings. In addition, one or more short in-class exercises will be done in a few lectures.

• Homework: Homework will be due roughly weekly, every Friday before the start of the class. Assignments will be made available in the Canvas site during class and will be due at 2pm on the date indicated on Canvas. Students must submit the homework individually via the Assignment feature on the Canvas site.

• Final  Project: Students  will  write  a journal/conference-style. paper  on a topic of their choice. The paper can be a systems-theoretical one or an application of one of the tools or methods seen in class to a specific problem. Students are expected to consult with their research advisors and pick a topic relevant to their thesis research for this course project. Students will also be required to present the results of their projects in front of the class and Systems faculty at the end of the semester.

5. Grading Procedures:

•   60% Homework

•   20% Final Paper

•    10% Final Presentation

•    10% Participation

Letter grades will be based on the weighted average specified above and assigned as follows:

A+ = 97-100%

A = 94-96%

A- = 90-93%

B+ = 87-89%

B = 84-86%

B- = 80-83%

C+ = 76-79%

C = 74-76%

C- = 70-73%

D+ = 67-69%

D = 64-66%

D- = 61-63%

F < 61%

The Final Project report and presentation will be graded on the basis of the following criteria: (a) technical depth; (b) appropriate use of concepts, tools and methods; (c) technical communication.

Assignments will be given a numerical grade between 0 and 100 based on a weighted average of the scores for all questions.

There will be no extension on homework assignments irrespective of the excuse. Instead, we will drop the lowest homework assignment grade at the end. This should provide enough flexibility. Please use this homework assignment wisely.