Course Title: Game Theory and its Applications

Part A: Course Overview

Course Title: Game Theory and its Applications

Credit Points: 12.00

Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 2 2006,
Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 1 2011,
Sem 2 2013,
Sem 2 2015

Course Coordinator: Professor Panlop Zeephongsekul

Course Coordinator Phone: +61 3 9925 3224

Course Coordinator Email:

Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH1324: Introduction to Statistics

MATH2267: Essential Mathematics for Analytics

Course Description

This course introduces you to the basics of game theory and reviews different types of games which are widely used in practice. It also considers the various solution concepts of games and how they can be applied to solve problems occurring in economics and other scientific disciplines.

Through an in depth studies of different types of games you will develop comprehensive knowledge of the many concepts prevalent in game theory, as well as possessing a set of useful tools which will enable you to apply such knowledge in real world contexts. You will appreciate the impact that game theory has made, and will continue to make, in many fields of scientific and other human endeavours.

The course provides a strong foundation for those students wishing to study more advanced level courses in game theory.

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

Personal and professional awareness

  • The ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • The ability to reflect on experience and improve your own future practice
  • The ability to apply the principles of lifelong learning to any new challenges.

Knowledge and technical competence

  • The ability to use the appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.


  • The ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • An understanding of the balance between the complexity and accuracy  of the mathematical and  statistical models used, and the timeliness of the delivery of the solution.

Information literacy

  • The ability to locate and use data and information and evaluate its quality with respect to its authority and relevance.

On completion of this course, you will be able to:

  1. Model competitive real world phenomena using concepts from game theory.
  2. Discuss the theory which underlies games.
  3. Possess a set of   intermediate level game-theoretic skills which can be applied in real world contexts.
  4. Review and critically assess literature which deals with game theory and related materials.
  5. Elucidate the potential or proven relevance of game theory and its impact in many fields of human endeavour which involve conflict of interest between two or more participants.
  6. Communicate game-theoretic ideas and concepts to non-specialist audiences in a language which is accessible and comprehensible. 

Overview of Learning Activities

Key concepts in game theory and how to find different solutions to different types of games will be extensively covered in this course. These will be explained and elucidated with relevant examples in lectures. The assignments will also test your understanding of the topics covered in classes. You will have the opportunity to discuss your progress with teaching staff.

Overview of Learning Resources

You will have access to learning resources comprising of the recommended references, a set of detailed course notes and other relevant materials such as extra notes, assignments, past exam papers and their solutions. These are available online via the RMIT Learning Hub (my RMIT). You will also have access to RMIT Library online and other hardcopy resources

Library Subject Guide for Mathematics & Statistics

Overview of Assessment

☒This course has no hurdle requirements.

Assessment Tasks:

Assessment Task 1: Assignments/Projects
Weighting 25%
This assessment task supports CLOs 1, 2, 3, and 4.

Assessment Task 2: Mid-semester Test
Weighting 25%
This assessment task supports CLOs 1, 2, 3, and 4.

Assessment Task 3: Final Exam
Weighting 50%
This assessment task supports CLOs 1, 2, 3, and 4.