Techniques and Tools for Solving Complex Problems

The course modules will provide modelling strategies and present some applications from the field of complex system modelling.

About this event

Area of Interest: Capability

Course overview

These short courses aim to introduce the state-of-the-art concepts and tools of Systems Thinking (ST), System Dynamics (SD) and Bayesian Networks (BN) and their applications to complex decision making and problem-solving across a range of fields. The course modules will provide modelling strategies and present some applications from the field of complex system modelling.

The program is made up of 5 modules Systems Thinking (Module 1), System Dynamics (Beginners and Intermediate levels) and Bayesian Network (Beginners and Intermediate levels). Each of the 5 modules will run over 1 day each, broken into 2 x three hours sessions per module.

Duration: Each of the 5 modules will run over 1 day each, broken into 2 x three hours sessions per module.  You may choose to attend 1 module, or all 5.

Individual modules can be booked here:

Module 1

Module 2

Module 3

Module 4

Module 5

Pre-requests: A laptop computer and freely available BN and SD modelling packages are required. Basic Mathematical and Computer skills are also required.

Required Software: Freely available BN (GeNIe) and SD modelling (VensimPLE) packages.

Delivery Mode: On-campus and remote - you choose how you would like to attend.

Who should attend?

This course is designed to accommodate a diverse audience, including:

• Decision makers such as government agencies and industry managers/practitioners to help identify problem intervention and new areas of investment.

• Researchers and academics who are seeking a new research tool.

• Students studying across defence, business, public health and engineering might find these courses complement or connecting the key ideas learnt from other courses.

Courses Outline

Each module is approx. 6 hours long (three 2-hour sessions) and will be delivered on-campus and/or online. Learners can choose to enrol in single modules or the entire program. Each module will include a range of case studies, guest speakers, scenarios, research and key concept areas.

There are two level of courses are offered, Beginners or Intermediate:


• This module is intended for participants that are new to Systems concept and have no or a little experience with Systems modelling.

• No statistical or specific mathematical education is required. Only basic understanding of computer skills is needed.


• This module in the sequence of Systems modelling courses is intended for participants who want to go to next step, building models dealing with more advanced concepts.

• Prerequisites: To complete the Beginners module, or Participants need to have experience building small models and have theoretical knowledge of Systems approach (BN and/orSD).

Module 1: Systems Thinking

• Introduction to Systems Thinking

• Systems Methods – (Systems tools/techniques, e.g. System Dynamics, Bayesian Networks, MicMac analysis)

• Causal Loop Modelling

• System Archetypes

• Causal Loop Modelling Exercise (Drawing Causal Loop Diagrams - CLDs using Vensim)

Module 2: System Dynamics - Beginners

• Overview of Systems Approach

• Modelling steps and procedures

• System Dynamics Tool – introduction to SD software Vensim PLE

• The most common behaviour and structures of dynamic systems

• Dynamics of Stocks and Flows

• Model building exercise (Group/individual)

Module 3: System Dynamics - Intermediate

• Generic behaviour patterns in System Dynamics Models – unconstrained growth/decay; s-shaped growth; goal seeking behaviour; and oscillation.

• Vensim model examples – Individual simulation experiments, making custom graph, making custom table

• Prey & Predator model (competition)

• Construct a model of the scenario and explore the tragedy of the commons

• Using your model, simulate and analyse several strategies

Module 4: Bayesian Network - Beginners

• Introduction to Bayes’ theorem and Bayesian inference

• Bayesian inference: Real world examples

• Introduction to Bayesian Networks

• Bayesian Networks in action: Case study examples

• Bayesian Network conceptualisation: Modelling steps and procedure

Module 5: Bayesian Network - Intermediate

• Bayesian Networks: Intermediate theoretical concepts

• Bayesian Network software package training

• Modelling exercise: building a Bayesian Network (group/individual)

• Scenario/sensitivity analysis of developed model



Dr Sahin

Dr Oguz (Oz) Sahin is a systems modeller with specialised expertise in a range of modelling approaches to problems of asset and resource management, risk assessment and climate change adaptation. His research interests include: sustainable built environment; climate change adaptation risk assessment; ecosystem based approaches; integrated water, energy and climate modelling; integrated decision support systems using coupled system dynamics and GIS modelling; Bayesian Network modelling, multiple criteria decision analysis, operational research methods and models. Dr Sahin authored or co-authored more than 150 refereed publication outputs.