General

  • General

    As an interdisciplinary domain, complex systems draws contributions from many different fields, such as the study of self-organization from physics, that of spontaneous order from the social sciences, chaos from mathematics, adaptation from biology, and many others. Complex systems is therefore often used as a broad term encompassing a research approach to problems in many diverse disciplines, including statistical physics, information theory, nonlinear dynamics, anthropology, computer science, meteorology, sociology, economics, psychology, and biology. Overview

    This is a seminar course. After this course, you won't be in a situation where someone would say: "So you have an MSc/PhD in complex systems, but you have no idea about X!?", where X is one of the prominent topics in complex systems. These topics include either historically important ideas or upcoming research directions that we do not currently properly cover in any other course. They might include topics such as fractals, scaling, swarm dynamics, agent-based models, social dynamics, econophysics, cellular automata, Turing patterns, evolutionary dynamics, self-organized criticality, information entropy, etc.
    Image Credits: Wikipedia

    If you are a newcomer (missed the session on April 16):

    Assistant: Abbas K. Rizi
    Please contact the assistant about your decision in selecting the topic or in case of any inquiry: abbas.karimirizi[@]aalto.fi

    Topics
    • Fractals
    • Reaction-diffusion models
    • Scaling
    • Evolutionary dynamics (punctuated equilibrium etc)
    • Self-organized criticality (Sandpile Model, Bak, etc)
    • Statistical mechanics approach to complex systems
    • Swarm dynamics
    • Agent-based models
    • Social dynamics
    • Econophysics
    • Cellular automata
    • Cities as complex systems
    • Phase transitions and critical phenomena

    How is this course designed?

    This is a graduate-level seminar course, meaning that the students will be responsible for researching a topic, distilling the central ideas in it, and teaching it to the others. Each student is given a single topic, and they prepare a one hour lecture on that topic. They will also prepare an easy (with solving time around 2 hours) and illustrative exercise on the topics. In order to complete the course, you will also need to attend all of the lectures (exceptions given within reason) and solve 60% of the exercises.

    Lecture: The lecture will be your summary of your research on the topic you have chosen. It should be an introduction to the topic, with the aim of getting the audience excited about learning more. Pick only the central ideas to present even if you have spent some time on some details. Favor visualizations and graphics and avoid detailed calculations and technical points. You will get a 1h time slot. The presentation should be 45 minutes and around 5-10 minutes time prepared for questions.

    Exercise: Prepare an exercise for your fellow students to solve. Choose one central model/idea or an exciting example in your topic as you will not be able to cover everything. Good exercises make the students feel like they have accomplished something, and it is a good idea to again choose something that can be easily visualized. Prepare the exercise in a way that it will take an average student around 2h to solve (including all the reporting). Try to reduce the boring parts of the task by preparing hints or code (e.g., give the students the visualization code, but let them code a model and play with the parameters). If it is a programming task, then using public libraries created for the topic is encouraged. An example of a good exercise could be a simulation study, where you have written some of the code and direct the students towards interesting phenomena to explore. The exercise should also have a goal that you can check that the other students have completed successfully, but there doesn't need to be a single correct solution.

    ZOOM: This class will be held as a Zoom meeting. All the sessions will be recorded and published on MyCourses.

    Workload

    Credits: 5 ECT. = 133h (assuming 15 students)

    Sessions are on Thursdays from 12:15 to 15:00, except the one (April 20) which is on Wednesday. There would be up to 3 presentations per a session. Please see the Time Schedule and Details of the Presentations.

    •  Lectures                                        3*6h = 18h
    •  Solving exercises                          15*2*0.6h = 18h
    •  Researching the topic                   40h
    •  Preparing the lecture                     15h
    •  Preparing the exercise                 ‌ 15h
    •  Going through the solutions          4h
    •  Reflection                                      23h
    •  Total                                              133h


    Grading

    There is a binary passed/failed system for this course. Each exercise will be evaluated through a peer grading system.

    To pass this course, attendees must:

    • attend all the sessions. If you have missed the 1st session (April 16), then it is OK.
    • prepare the materials (content and exercises) and have a coherent* 45 min presentation.
    • answer to all of the exercises, but one! Almost all the exercises would be graded as passed/failed.
    * a coherent presentation is one in which 70% of the audience has a satisfactory feeling after the talk and the discussion are over!

    Learning Outcomes

    • Attendees will understand and be able to explain to others the main ideas of a wide range of topics in complex systems
    • Attendees will have hands-on experience in applying a method/idea related to selected topics in complex systems
    • Attendees will have a more in-depth understanding of a single topic in complex systems. They will able to evaluate which are the central ideas of this topic.
    • Improving presentation skills
    • The ability to turn material on a wide topic into an interesting exercise with a set amount of work

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