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What do wars, elections, job-hunting, couples, parenting, disease, stock markets, pistol duels, art valuations and YOU buying an apple from a street vendor have in common? The answer is Game Theory. All of these cases require that the people involved devise plans of action to achieve a goal; be it a victory in war or in an election, a higher paying job, a happier relationship, the containment of a biological attack or the lowest price YOU can pay for that apple!

Game theory is a mathematical theory of interaction, which is used to predict future outcomes. This module – an interdisciplinary introduction to game theory - is a bridge between the world of mathematics and science and the world of the humanities and the social and historical sciences. Students are introduced to game theory as a descriptive tool that is not bound by the topics of any single discipline. The power of game theory as a descriptive theory has historically been enhanced by various disciplines, which over the years have contributed new solution concepts. The most influential discoveries made in philosophy, politics, economics, finance, war studies, biology, psychology, law and history will be discussed.

Teaching Delivery

Two lectures per week totalling 3 hours with one 1-hour workshop per week.

Module Aims and Objectives

Game theory has two components; a descriptive theory coupled with a solution theory. Students will be familiarised with the methods used to describe strategic and dynamic games of complete and incomplete information, in addition to the descriptive theories of voting, auctions, bargaining and evolutionary games. Students should also be able to apply the solution concepts (i.e. algorithms used to make predictions) of dominance, Nash equilibrium (NE), mixed strategy NE, sub-game perfect NE, Bayesian NE and finally pooling and separating equilibria. By the end of the course, students should be able to reproduce a variety of basic formal arguments, which we call games, coupled with elegant solutions.

There is an additional coding component to the module that is used to describe and solve games in Python. The ability to code or learn to code during the module is optional, but encouraged.

The 20 or so games discussed in the module are meant to empower students with an extra tool-kit which they can use in their pathway studies, and later in their professional careers, to argue persuasively for or against a predicted scenario. This is an ideal module for students who wish to pursue careers in competitive environments where teamwork is essential (e.g. politics, finance, entrepreneurship, consulting).