This module introduces students to the mathematical study of multiple interactive agent decision making. This is an introduction to Game Theory through notions such as Nash Equilibria, Evolutionary Game Theory and Bayesian Games. Students will learn Game Theory in an active way through role playing and student-led activities.

• represent games in both Extensive Form and Normal Form.

• understand concepts linked to mixed strategies.

• compute dominant strategies.

• identify best responses.

• define and compute Nash Equilibria.

• understand and carry out backward induction.

• understand concepts of subgame perfection.

• compute equilibria and best responses in repeated games.

• understand concepts of Evolutionary Game Theory

• compute evolutionary Stable Strategies

• represent incomplete information games

• understand concepts of Bayesian Nash Equilibria and rationalizability

22 fifty-minute lectures

5 fifty-minute exercise classes.

Students are also expected to undertake at least 50 hours private study including preparation of worked solutions for tutorial classes.

Problem solving, logical thinking, mathematical formulation of real-life situations.

Formative assessment is carried out by means of regular exercises.  Feedback to students on their solutions and their progress towards learning outcomes is provided during tutorial classes.

There is a summative coursework assessment which asks students to demonstrate an understanding of the building blocks through the use of game theoretic software (Sagemath/Python).

The major component of summative assessment is the written examination at the end of the module.  This gives students the opportunity to demonstrate their overall achievement of learning outcomes.  It also allows them to give evidence of the higher levels of knowledge and understanding required for above average marks.

The examination paper has a choice of three from four equally weighted questions.

• Representations and basic concepts.

• Analysing behaviour in static settings.

• Further analysis of extensive form games.

• Repeated games.

• Evolutionary Game Theory.

• Games with incomplete information

Watson J. 2013. Strategy: An Introduction to Game Theory. 3^rd ed. Norton