Note: This unit version is currently under review and is subject to change!
CSYS5030: SelfOrganisation and Criticality (2018  Semester 2)
Unit:  CSYS5030: Information Theory and SelfOrganisation (6 CP) 
Mode:  NormalDay 
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 
PreRequisites:  None. 
Brief Handbook Description:  '"Selforganisation" 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. Selforganising systems can be found practically everywhere: gene regulatory networks selforganise into complex patterns and attractors, selfhealing sensor networks reconfigure their topology in response to damage, animal swarms change shape in response to an approaching predator, robotic modules selforganise 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 sociotechnical systems, developing a critical understanding of selforganisation, and complex adaptive systems applied to technological, social, organisational and biological systems. It will cover crossdisciplinary 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: 


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 realworld 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)Assessment Methods: 


Assessment Description: 
The assessments will consist of four assignments. Assignments 14 will be completed individually. Literature review: You will write a short appraisal of an article / report / blog post which provides an informationtheoretic 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 informationtheoretic 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 inclass 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 elearning portal, though which these assignments will be submitted.  
Grading: 


Policies & Procedures:  See the policies page of the faculty website at http://sydney.edu.au/engineering/studentpolicies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies. 
Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp
Week  Description 
Week 1  Lecture/Tutorial: Introduction to information theory and entropy 
Week 2  Lecture/Tutorial: Entropy and mutual information 
Week 3  Lecture/Tutorial: Informationtheoretic 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 (timeseries 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: SelfOrganising 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.