Overview

Many decisions depend not only on the environment but on what other decision-makers may do. Game theory provides a formal language for analysing these interactions.

The course moves from simple strategic games to incomplete information, repeated interaction and equilibrium refinement. It then connects these ideas to autonomous systems, where an agent must infer the likely behaviour and type of other agents before acting.

A recurring example is driving. A human driver does not simply observe another car; the driver continually forms beliefs about whether the other driver is cautious, aggressive, distracted or likely to yield.

Learning objectives

By the end of the course, learners should be able to:

• represent strategic interactions using players, actions, payoffs and beliefs;
• solve simple dominance and Nash-equilibrium problems;
• reason about incomplete information and different player types;
• explain why repeated interaction changes incentives;
• understand the intuition behind trembling-hand and related refinements;
• apply strategic reasoning to autonomous agents and driving behaviour.

Intended participants and prerequisites

Course structure

The course is organised into six units. Each unit combines concise explanation, worked examples and short analytical exercises.

Unit	Subject	Principal questions
1	Strategic games	How do outcomes depend on the simultaneous choices of several players?
2	Nash equilibrium	What does it mean for no player to want a unilateral change?
3	Incomplete information	How do beliefs about hidden player types affect strategy?
4	Repeated interaction	How do future consequences change present behaviour?
5	Trembles and robustness	Which equilibria survive the possibility of small mistakes?
6	Autonomous agents	Why must a self-driving system reason about the likely behaviour of other drivers?

Teaching and assessment

Teaching is delivered through concise lectures, technical notes, worked examples and guided exercises. AI-supported interaction may be used to explain concepts, generate additional practice and challenge a learner's reasoning, but assessment remains based on the learner's demonstrated understanding.

Component	Purpose	Weight
Short exercises	Check understanding of the principal concepts	40%
Applied investigation	Use the course framework on a new problem or dataset	30%
Final assessment	Integrated reasoning across the course	30%

A certificate of completion is issued only when the required assessments have been completed at the stated standard. Participation alone does not constitute completion.

Faculty

Materials and technical requirements

Learning environment

Learners need a current web browser and access to the Gordon learning platform. Where a course uses code, spreadsheets, datasets or external readings, the relevant requirements are identified inside the unit.

Course materials

Required readings, datasets and practice materials are identified within each unit. Public courses are designed so that required core materials can be accessed without a separate tuition charge.