Techniques and Tools for Solving Complex Problems

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.

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:

Beginners:

• 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.

Intermediate:

• 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

 

Presenter

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.