Course coordinator
Professor Lionel Page
Course overview
- Study period
- Semester 1, 2026 (23/02/2026 - 20/06/2026)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Economics School
The way that economists think about strategic situations is through the application of game theory. One aim of the course is to teach you some strategic considerations to take into account when making your own choices. A second aim is to predict how other people or organizations behave when they are in strategic settings. We will see that these aims are closely related. We will learn new concepts, methods and terminology. A third aim is to apply these tools to settings from economics and other disciplines. The course will emphasize examples.
Non-cooperative game theory is the principal method that economists use to think about and analyseﾠstrategic situations.ﾠThis course willﾠintroduce you to many of the main concepts, methods and terminology of game theory and show how these tools may be applied to settings from economics and other disciplines. The course will use examples and applications to motivate concepts.
Course requirements
Assumed background
Knowledge of microeconomics up to the level of ECON1010, as well as simple univariate calculus is assumed.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ECON1010 or 1011
Course contact
School enquiries
School Enquiries, School of Economics
All enquiries regarding student and academic administration (i.e. non-course content information, e.g., class allocation, timetables, extension to assessment due date, etc.) should be directed toﾠenquiries@economics.uq.edu.au.ﾠ
Enquiries relating specifically to course content should be directed to the Course Coordinator/Lecturer.
Course staff
Lecturer
Professor Lionel Page
Tutor
Miss Anne-Claire Bouton
Mr Mosharop Hossian
Mr Josiah Ruthenberg
Mr Jasper Bambrick
Senior tutor
Mr Josiah Ruthenberg
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Lectures commence in Week 1
Tutorials commence in Week 2
Please see the Learning Activities section of this Course Profile for the timetabling implications of public holidays.
Important Dates:
- Public Holidays: Fri 3 April (Good Friday), Mon 4 May (Labour Day).
- Mid-Semester Break: 6 April - 10 April. Semester 1 classes recommence on Mon 13 April.
Students should refer to the timetable prior to the commencement of classes to ensure that they have the most up to date information, as from time to time late room changes may occur.
Aims and outcomes
The way that economists think about strategic situations is through the application of game theory.
The aims of the course are:
1. To teach you some strategic considerations to take into account when making your own choices.
2. To predict how other people or organizations behave when they are in strategic settings.ﾠ
3. To apply these tools to settings from economics and other disciplines.ﾠ
We will see that these aims are closely related. We will learn new concepts, methods and terminology.ﾠThe course will use examples and applications to motivate concepts.ﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Identify strategic considerations facing decision makers.
LO2.
Recognise tools and concepts of game theory.
LO3.
Predict how people or organizations in strategic settings will behave.
LO4.
Apply game theoretic tools and concepts to a variety of settings in economics and other disciplines.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Examination |
In-semester Exam
|
30% |
27/03/2026 12:00 pm
Exam will take place during lecture on Friday 27 March. |
| Paper/ Report/ Annotation | Report: Game Theory in the News | 20% |
24/04/2026 3:59 pm |
| Examination |
End-of-semester Exam
|
50% |
End of Semester Exam Period 6/06/2026 - 20/06/2026 |
Assessment details
In-semester Exam
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 30%
- Due date
27/03/2026 12:00 pm
Exam will take place during lecture on Friday 27 March.
- Learning outcomes
- L01, L02, L03, L04
Task description
In this assessment, you will answer questions and solve exercises that demonstrate your understanding of the key concepts and tools presented in the first four lectures. The task is designed to assess your skills in analysis and problem-solving. Completing this assessment will help you build technical skills that you can carry into the rest of your studies in economics.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 60 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Closed book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Exam papers will be collected in person.
Deferral or extension
You may be able to defer this exam.
Report: Game Theory in the News
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 20%
- Due date
24/04/2026 3:59 pm
- Learning outcomes
- L01, L02, L03, L04
Task description
In this assessment, you will create a report that demonstrates your understanding of game theory and your ability to apply it to a real-world scenario or problem. The task is designed to assess your skills in analysis and critical thinking and to support your development in economic analysis. Completing this assessment will help you build practical skills that you can carry into future academic and professional contexts.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Upload your report via Turnitin on Blackboard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
The maximum extension time is 7 days in order to ensure timely feedback to other students.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
End-of-semester Exam
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 50%
- Due date
End of Semester Exam Period
6/06/2026 - 20/06/2026
- Learning outcomes
- L01, L02, L03, L04
Task description
End-of-semester exam in person. Composed of exercises on all the course content.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Closed book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0% - 29% |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30% - 46% |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47% - 49% |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50% - 64% |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65% - 74% |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75% - 84% |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85% - 100% |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
A student's final overall end of semester percentage mark will be rounded to determine their final grade. For example, 64.5% rounds to 65%, while 64.4% rounds to 64%.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Using AI at UQ
Visit the AI Student Hub for essential information on understanding and using Artificial Intelligence in your studies responsibly.
Plagiarism
The School of Economics is committed to reducing the incidence of plagiarism. You are encouraged to read the UQ Student Integrity and Misconduct Policy available in the Policies and Procedures section of this course profile.
The Academic Integrity Module (AIM) outlines your obligations and responsibilities as a UQ student. It is compulsory for all new to UQ students to complete the AIM.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
Course Material including the course outline and tutorial answers will be posted on Blackboard.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 (23 Feb - 01 Mar) |
Lecture |
Lecture 1: Decisions Under Uncertainty Expected Utility Theorem Learning outcomes: L02 |
Week 2 (02 Mar - 08 Mar) |
Tutorial |
Tutorial 1: Decisions Under Uncertainty Learning outcomes: L02 |
Lecture |
Lecture 2: Normal Form Games and Dominance Presentation of normal form games, dominance Learning outcomes: L01, L02, L03 |
|
Week 3 (09 Mar - 15 Mar) |
Tutorial |
Tutorial 2: Normal Form Games and Dominance Presentation of normal form games, dominance Learning outcomes: L01, L02, L03 |
Lecture |
Lecture 3: Nash Equilibrium I Pure Strategy Nash Equilibrium, Mixed Strategies Learning outcomes: L01, L02, L03 |
|
Week 4 (16 Mar - 22 Mar) |
Tutorial |
Tutorial 3: Nash Equilibrium I Pure Strategy Nash Equilibrium, Mixed Strategies Learning outcomes: L01, L02, L03 |
Lecture |
Lecture 4: Nash Equilibrium II Mixed Strategy Nash Equilibrium Learning outcomes: L01, L02, L03 |
|
Week 5 (23 Mar - 29 Mar) |
Lecture |
In-semester Exam Exam will take place during lecture on Friday 27 March. Learning outcomes: L01, L02, L03, L04 |
Week 6 (30 Mar - 05 Apr) |
Tutorial |
Tutorial 4: Nash Equilibrium II Mixed Strategy Nash Equilibrium Learning outcomes: L01, L02, L03 |
Lecture |
Lecture 5: Bayesian games Extending the notion of Nash Equilibrium to situations with incomplete information. Lecture recording will replace the in-person lecture this week due to the Good Friday public holiday on 3 April. Learning outcomes: L01, L02, L03, L04 |
|
Mid-sem break (06 Apr - 12 Apr) |
No student involvement (Breaks, information) |
Mid-Semester Break No lecture or tutorial will be held during Mid-Semester Break |
Week 7 (13 Apr - 19 Apr) |
Tutorial |
Tutorial 5: Bayesian games Learning outcomes: L01, L02, L03, L04 |
Lecture |
Lecture 6: Extensive Form Games Extensive form game with perfect information, Game trees and backward induction Learning outcomes: L01, L02, L03 |
|
Week 8 (20 Apr - 26 Apr) |
Tutorial |
Tutorial 6: Extensive Form Games Extensive form game with perfect information, Game trees and backward induction. Learning outcomes: L01, L02, L03 |
Lecture |
Lecture 7: Extensive form games with imperfect information Learning outcomes: L01, L02, L03 |
|
Week 9 (27 Apr - 03 May) |
Tutorial |
Tutorial 7: Extensive form game with imperfect information Learning outcomes: L01, L02, L03, L04 |
Lecture |
Lecture 8: Extensive Form Games with incomplete information Learning outcomes: L01, L02, L03 |
|
Week 10 (04 May - 10 May) |
Tutorial |
Tutorial 8: Extensive form game with incomplete information Learning outcomes: L01, L02, L03, L04 |
Lecture |
Lecture 9: Long-term Relationship Learning outcomes: L01, L02, L03, L04 |
|
Week 11 (11 May - 17 May) |
Tutorial |
Tutorial 9: Long-term Relationship Learning outcomes: L01, L02, L03, L04 |
Lecture |
Lecture 10: Mechanism design 1 - contracts and market design Learning outcomes: L01, L02, L03, L04 |
|
Week 12 (18 May - 24 May) |
Tutorial |
Tutorial 10: Mechanism design 1 - contracts and market design Learning outcomes: L01, L02, L03, L04 |
Lecture |
Lecture 11: Mechanism design 2 - Auction and contests Learning outcomes: L01, L02, L03, L04 |
|
Week 13 (25 May - 31 May) |
Tutorial |
Tutorial 11: Mechanism design 2 - Auction and contests Learning outcomes: L01, L02, L03, L04 |
Lecture |
Lecture 12: Political applications - Voting and coalitions Learning outcomes: L01, L02, L03, L04 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments for Students Policy and Procedure
- AI for Assessment Guide
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.