MXB261 Modelling and Simulation Science
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | MXB261 |
|---|---|
| Equivalent(s): | INB360, MAB480 |
| Assumed Knowledge: | Programming experience; sound achievement in Senior Mathematics B is assumed knowledge. |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $592 |
| Domestic tuition unit fee | $3,816 |
| International unit fee | $4,872 |
Unit Outline: Semester 2 2026, Gardens Point, Internal
| Unit code: | MXB261 |
|---|---|
| Credit points: | 12 |
| Equivalent: | INB360, MAB480 |
| Assumed Knowledge: | Programming experience; sound achievement in Senior Mathematics B is assumed knowledge. |
| Coordinators: | Adrianne Jenner | adrianne.jenner@qut.edu.au David Warne | david.warne@qut.edu.au Kate Helmstedt | kate.helmstedt@qut.edu.au |
Overview
With the growing importance of strategic decision-making in business and data science, understanding game theory is essential for analyzing competitive and cooperative interactions. This unit introduces the mathematical foundations of game theory and its applications in these fields. You will explore concepts such as Nash equilibrium, cooperative games, Bayesian games, and mechanism design, with a focus on computational techniques. Through hands-on simulations, you will model and analyze strategic interactions in real-world scenarios, including pricing strategies, auctions, network games, and resource allocation. The unit emphasizes the use of computational tools to study equilibrium behavior, optimization, and decision-making under uncertainty. By combining mathematical theory with computational practice, this unit equips you with the skills to design and analyze strategic decision-making models in business and data science applications.
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply programming skills to implement numerical simulations and algorithms for modeling strategic interactions.
- Analyse, visualise and interpret simulation data.
- Conduct and synthesise research on simulation techniques for a real-world situation.
- Communicate research in written and visual formats for a scientific audience.
- Work in a group to solve problems and express a coherent argument.
Content
The major topics in this unit are:
- Introduction to Application Areas
- Introduction to MATLAB and NetLogo
- Core concepts in game theory, including Nash equilibrium and subgame perfection
- Use of numerical methods for finding Nash equilibria
- Games on networks analysis
- Basics of mechanism design and matching theory
- Visualisation of multidimensional data
- Data analysis, such as long term dynamics, rare events, and the matching of simulations to experimental data
Learning Approaches
This unit is available for you to study in either on-campus or online mode. Learning in this unit includes weekly pre-recorded lectures, online activities, computer practicals, lecture consultation and a unit communications channel, designed to facilitate communication with your peers and teaching staff outside of scheduled classes. You can expect to spend 10 - 15 hours per week involved in preparing for and attending all scheduled computer practicals and lecture consultations, completing assessment tasks, and undertaking your own independent study to consolidate your learning.
This unit engages you in your learning through a theory-to-practice approach, with delivery of topic content followed by practical exercises to develop programming skills and present opportunities for problem-solving, both individually and in group work. As with any unit requiring programming, you may need to devote additional hours each week to practise programming and analysis skills.
Feedback on Learning and Assessment
You will receive feedback via generic comments to the cohort via QUT Canvas, as well as individual feedback during attendance at the computer workshops. There will be written feedback on all assessment tasks, with feedback on earlier assessment tasks being received prior to the submission of the next task. Appointments can be made with the unit coordinator to receive feedback on the final examination.
Assessment
Overview
This unit is assessed through problem-solving tasks, a simulation project (group work), and a final exam.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
Programming Exercise
A programming implementation of a basic simulation algorithm.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
A program development exercise, involving background research, implementation of a simulation algorithm, analysis of resulting data using visualisation techniques, and an oral presentation. Submission: Week 11, with oral presentations in Week 12 and/or 13.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
A written examination, evaluating concepts, comprehension of techniques, and application of this knowledge, drawing on all material covered throughout the semester.
The examination will be at a local testing centre. For students enrolled as internal or on-campus, the local testing centre will be on QUT campus. For students enrolled as online, QUT Examinations will provide testing centre information.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
Learning materials to support your learning in this unit is available in your Canvas unit site. Various readings will be assigned, available online or at the QUT library.
The software required for this unit is installed in the computer labs and/or freely available. You are not expected to purchase any software or other resources for this unit.
Risk Assessment Statement
There is minimal health and safety risk in this unit. It is your responsibility to familiarise yourself with the Health and Safety policies and procedures applicable within campus areas and laboratories.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: Project (applied) - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Problem Solving Task, Project (applied), Examination (invigilated) - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Project (applied) - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Problem Solving Task, Project (applied)
Unit Outline: Semester 2 2026, Online
| Unit code: | MXB261 |
|---|---|
| Credit points: | 12 |
| Equivalent: | INB360, MAB480 |
| Assumed Knowledge: | Programming experience; sound achievement in Senior Mathematics B is assumed knowledge. |
Overview
With the growing importance of strategic decision-making in business and data science, understanding game theory is essential for analyzing competitive and cooperative interactions. This unit introduces the mathematical foundations of game theory and its applications in these fields. You will explore concepts such as Nash equilibrium, cooperative games, Bayesian games, and mechanism design, with a focus on computational techniques. Through hands-on simulations, you will model and analyze strategic interactions in real-world scenarios, including pricing strategies, auctions, network games, and resource allocation. The unit emphasizes the use of computational tools to study equilibrium behavior, optimization, and decision-making under uncertainty. By combining mathematical theory with computational practice, this unit equips you with the skills to design and analyze strategic decision-making models in business and data science applications.
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply programming skills to implement numerical simulations and algorithms for modeling strategic interactions.
- Analyse, visualise and interpret simulation data.
- Conduct and synthesise research on simulation techniques for a real-world situation.
- Communicate research in written and visual formats for a scientific audience.
- Work in a group to solve problems and express a coherent argument.
Content
The major topics in this unit are:
- Introduction to Application Areas
- Introduction to MATLAB and NetLogo
- Core concepts in game theory, including Nash equilibrium and subgame perfection
- Use of numerical methods for finding Nash equilibria
- Games on networks analysis
- Basics of mechanism design and matching theory
- Visualisation of multidimensional data
- Data analysis, such as long term dynamics, rare events, and the matching of simulations to experimental data
Learning Approaches
This unit is available for you to study in either on-campus or online mode. Learning in this unit includes weekly pre-recorded lectures, online activities, computer practicals, lecture consultation and a unit communications channel, designed to facilitate communication with your peers and teaching staff outside of scheduled classes. You can expect to spend 10 - 15 hours per week involved in preparing for and attending all scheduled computer practicals and lecture consultations, completing assessment tasks, and undertaking your own independent study to consolidate your learning.
This unit engages you in your learning through a theory-to-practice approach, with delivery of topic content followed by practical exercises to develop programming skills and present opportunities for problem-solving, both individually and in group work. As with any unit requiring programming, you may need to devote additional hours each week to practise programming and analysis skills.
Feedback on Learning and Assessment
You will receive feedback via generic comments to the cohort via QUT Canvas, as well as individual feedback during attendance at the computer workshops. There will be written feedback on all assessment tasks, with feedback on earlier assessment tasks being received prior to the submission of the next task. Appointments can be made with the unit coordinator to receive feedback on the final examination.
Assessment
Overview
This unit is assessed through problem-solving tasks, a simulation project (group work), and a final exam.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
Programming Exercise
A programming implementation of a basic simulation algorithm.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
A program development exercise, involving background research, implementation of a simulation algorithm, analysis of resulting data using visualisation techniques, and an oral presentation. Submission: Week 11, with oral presentations in Week 12 and/or 13.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
A written examination, evaluating concepts, comprehension of techniques, and application of this knowledge, drawing on all material covered throughout the semester.
The examination will be at a local testing centre. For students enrolled as internal or on-campus, the local testing centre will be on QUT campus. For students enrolled as online, QUT Examinations will provide testing centre information.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
Learning materials to support your learning in this unit is available in your Canvas unit site. Various readings will be assigned, available online or at the QUT library.
The software required for this unit is installed in the computer labs and/or freely available. You are not expected to purchase any software or other resources for this unit.
Risk Assessment Statement
There is minimal health and safety risk in this unit. It is your responsibility to familiarise yourself with the Health and Safety policies and procedures applicable within campus areas and laboratories.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: Project (applied) - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Problem Solving Task, Project (applied), Examination (invigilated) - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Project (applied) - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Problem Solving Task, Project (applied)