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CSYS5030: Self-Organisation and Criticality (2018 - Semester 2)

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Unit: CSYS5030: Information Theory and Self-Organisation (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Postgraduate
Faculty/School: Faculty of Engineering and Information Technologies
Unit Coordinator/s: Dr Lizier, Joseph
Session options: Semester 2
Versions for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Brief Handbook Description: '"Self-organisation" is the evolution of a system into an organised form in the absence of explicit external influences or centralised control. It brings many attractive properties to systems such as robustness, adaptability and scalability. Self-organising systems can be found practically everywhere: gene regulatory networks self-organise into complex patterns and attractors, self-healing sensor networks reconfigure their topology in response to damage, animal swarms change shape in response to an approaching predator, robotic modules self-organise into coordinated motion patterns, and ecosystems develop spatial structures in response to diminishing resources. The unit will study pattern formation and the common principles behind similar patterns in nature and socio-technical systems, developing a critical understanding of self-organisation, and complex adaptive systems applied to technological, social, organisational and biological systems. It will cover cross-disciplinary concepts and methods based on information theory, nonlinear dynamics, including elements of chaos theory and statistical physics, such as fractals, percolation, entropy, open dissipative systems, phase transitions and critical phenomena.
Assumed Knowledge: None.
Lecturer/s: Dr Lizier, Joseph
Dr Harré, Michael
Timetable: CSYS5030 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Laboratory 1.00 1 13
3 Independent Study 7.00 1 13
T&L Activities: This unit of study comprises of regular lectures, as well as laboratory / tutorial sessions. These sessions will take place in a lab with access to relevant computer facilities. Depending on the syllabus, some weeks will comprise tutorials where students will solve problems with the help of tutors, and other weeks will comprise programming or software based laboratory experiments.

During lab sessions with a programming/software component, tutors will be present to assist students develop relevant programming or other computing skills.

Students will use independent study time to further develop their computing skills and to practise solving analytical problems.

Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.

Attribute Development Method Attribute Developed
Develop through lectures, case studies and tutorial examples, a working knowledge of techniques and tools that can be used for complex systems design Design (Level 4)
Develop through lectures and tutorial discussions an understanding of concepts related to self organisation and criticality, such as information theory, entropy methods, chaos theory, fractals and percolation, and how to apply these techniques to understand complex systems Engineering/IT Specialisation (Level 5)
Develop through lectures and tutorial discussions an understanding of:

1 - Programming skills needed for using information theory
2 - Fluency in using open source tools and toolkits for information theory related calculations
3 - How to apply information theory calculations empirically using software to real-world data sets
Maths/Science Methods and Tools (Level 5)
Develop through assignments and case studies an ability to critically dissect and understand the structure and function of complex systems, and the ability to describe this understanding efficiently and quantitatively
Be able to effectively use information theory in understanding the dynamics of complex systems
Information Seeking (Level 3)

For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

Information Seeking (Level 3)
1. Capacity to critically evaluate investigations of self-organisation and criticality in complex systems, and the insights provided
2. To be able to carry out a series of 'computational experiments' on complex systems in order to understand their dynamics and to interpret the result in a technically correct manner. This includes researching and implementing techniques not covered in class.
Maths/Science Methods and Tools (Level 5)
3. Develop scientific programming skills which can be applied in complex system analysis and design
4. Ability to apply and make informed decisions in selecting and using information-theoretic measures and software tools to analyse complex systems
5. Ability to create information-theoretic analyses of real-world data sets, in particular in a student’s domain area of expertise
6. To be able to use the computational and mathematical tools that are appropriate for the analysis of systems that are in a 'critical' or 'complex' state.
Engineering/IT Specialisation (Level 5)
7. Understand basic information-theoretic measures, and advanced measures for time-series, and how to use these to analyse and dissect the nature, structure, function and evolution of complex systems
8. Understand, and successfully use in analysis, the concepts of percolation, chaos theory, phase transitions and fractals.
Design (Level 4)
9. To be able to understand the design of and to extend the design of a piece of software using techniques from class and your own readings
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Literature review* No 10.00 Week 3 (Friday, 11 pm) 1, 4, 7,
2 Info theory project presentation* No 15.00 Week 7 (During your timetabled class) 1, 3, 4, 5, 7,
3 Info theory project report* No 35.00 Week 8 (Friday, 11 pm) 1, 3, 4, 5, 7,
4 Computational Experiments at Criticality* No 40.00 STUVAC (Week 14) (Friday, 8 pm) 2, 3, 6, 8, 9,
Assessment Description: The assessments will consist of four assignments. Assignments 1-4 will be completed individually.

Literature review: You will write a short appraisal of an article / report / blog post which provides an information-theoretic analysis of empirical data, and critically evaluate that study. The reviews will be socially shared and discussed in class.

Info theory project presentation and report: You will create an information-theoretic analysis of a data set of your choosing, selecting appropriate tools to answer a question of interest regarding relationships in that data. You will deliver an in-class presentation, and write a report, describing your approach, the results, discussing implications for the system under study, and critically evaluating your findings.

Computational Experiments at Criticality: You will be given a choice of several different systems that will be presented in class. Having chosen a particular system you will run a series of computational experiments in order to explore the critical dynamics of system. You will then need to extend the system and your analysis in order to explore the consequences of your modifications using the tools covered in class as well as methods you have discovered through your own independent readings.

Written assignments 3 and 4 (without formally applying for special consideration) will be assessed a penalty of 7% per day until depletion, whilst assignment 1 will be assessed at a 20% per day penalty until depletion. Extensions for the Project Presentation will only be granted in the case where formal Special Consideration has been applied for and approved.

Then university has authorised and mandated the use of text based similarity detecting software TURNITIN for all text based written assignments.

* means this assessment must be repeated or will be replaced with a different assessment if missed due to special consideration

IMPORTANT: There may be statistically and educationally defensible moderation when combining the marks from each component to ensure consistency of marking between markers, and alignment of final grades with unit outcomes and grade descriptors.
Assessment Feedback: Feedback for assignments will be through Blackboard e-learning portal, though which these assignments will be submitted.
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Policies & Procedures: See the policies page of the faculty website at for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.
Recommended Reference/s: Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar

Week Description
Week 1 Lecture/Tutorial: Introduction to information theory and entropy
Week 2 Lecture/Tutorial: Entropy and mutual information
Week 3 Lecture/Tutorial: Information-theoretic estimators; local measures
Assessment Due: Literature review*
Week 4 Lecture/Tutorial: Information theory -- case studies
Week 5 Lecture/Tutorial: Statistical significance, and Information dynamics I (time-series analysis)
Week 6 Lecture/Tutorial: Information dynamics II and network inference
Week 7 Lecture/Tutorial: Project presentations
Assessment Due: Info theory project presentation*
Week 8 Lecture/Tutorial: An introduction to Mathematica, Fractals, Chaos, and Criticality
Assessment Due: Info theory project report*
Week 9 Lecture/Tutorial: Game Theory: Bifurcations and critical behaviour in economics
Week 10 Lecture/Tutorial: Measuring complex behaviour: Fractal dimensions, Lyapunov exponent
Week 11 Lecture/Tutorial: Phase Transitions I: Tipping points and structural instability
Week 12 Lecture/Tutorial: Phase Transiitons II: Second order transitions and renormalisation
Week 13 Lecture/Tutorial: Self-Organising Criticality: The heart of complexity?
STUVAC (Week 14) Assessment Due: Computational Experiments at Criticality*

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Graduate Diploma in Complex Systems 2017, 2018, 2019
Master of Complex Systems 2017, 2018, 2019

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
Information Seeking (Level 3) Yes 26.5%
Maths/Science Methods and Tools (Level 5) Yes 43%
Engineering/IT Specialisation (Level 5) Yes 22.5%
Design (Level 4) Yes 8%

These goals are selected from Engineering & IT Graduate Outcomes Table which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.