Complex Systems

Recommended Prerequisites

Introduction to Artificial intelligence, Calculus, Linear Algebra, Programming.

Teaching Methods

Lectures: theories and methods used in heuristic problem solving. Students solve in practical classes computer problems of medium complexity and by means of simulations using particular frameworks. This is a group work done under the supervision of the professor. Written synthesis of a recent research work, experimental work involving computer simulations, done individually and includes a written report and an oral presentation.

Learning Outcomes

The main goal of this course is the study of complex system, either physical (e.g., the weather), biologic (e.g., genetic regulatory networks), cognitive (e.g., the mind) or social (e.g., the stock market). We will be focused on the concepts, the models and the tools necessary to the comprehension of these systems. We will start with a conceptual discussion involving the notions of system, complexity, self-organization, emergency, information, computation and evolution. Then, we will proceed with a description of different classes of models of complex systems. We will end with the presentation of different tools and frameworks and several case studies. In the end ,the student will be able to choose the right model and the appropriate tool to analyze a complex system, and extract knowledge from that analysis.

Work Placement(s)

No

Syllabus

1. Concepts (system, complex system, complex adaptive system, dynamic system, complexity and diversity, information, computation, evolution and models).

2. Dynamical Systems

3. Network Science

4. Agent-Based Modeling

5. Tools and frameworks

6. Case Studies.

Head Lecturer(s)

Ernesto Jorge Fernandes Costa

Assessment Methods

Assessment
Synthesis work: 10.0%
Project: 30.0%
Exam: 60.0%

Bibliography

1. Hiroki Sayama, Introduction to the modeling and analysis of complex systems, Binghamton University, SUNNY, 2015.
2. Steven Strogatz, Nonlinear Dynamics and chaos, Perseus Books Publishing, 1994.
3. Frank Giordano, William Fox, Steven Horton, A First course in Mathematical Modeling ( 5th Edition), Brooks/Cole, 2014.
4. Uri Wilenski and William Rand, An introduction to agent-based modeling, MIT Press, 2015.
5. Melanie Mitchell, Complexity: a guided tour, Oxford University Press, 2009.
6. Albert-Laszlo Barabasi, Network Science, Cambridge University Press, 2016.
7. Heinz-Otto Peithgen, Harmut Juergens, and Dietmar Saupe, Chaos and Fractals: new frontiers of science (2nd Edition), Springer, 2004.