Module Description

Game theory provides a mathematical framework to describe strategic or economic interactions among agents, which can act (play) one out of a set of strategies. The core of a game-theoretic analysis are the payoffs (benefits minus costs) that agents receive when playing against each other's strategy. Evolutionary game theory transfers these concepts to biology where reproductive fitness is modelled by benefits and costs, and replicator equations enable analysis of the stability of equilibria as well as numerically investigations of non-equilibrium dynamics. 

Game-theoretic concepts are widely transferred and applied to social, economic or biological problems to explain opinion formation and decision-making in societies or companies, as well as collective behaviour in biology. 

In this module we will study the foundations of economic as well as evolutionary game theory, consider examples of real-world dilemma situations (strategic, climate, vaccination) including public goods games and multiplayer games.

Learning Objectives

This course introduces analytical and computational tools for mathematical optimization. Upon successful completion of this course you should be able to:

• Understand and explain the central concepts of non-cooperative, cooperative and evolutionary game theory including the minimax theorem, dominance, Nash equilibria, replicator equations and evolutionarily stable strategies.

• Understand, explain, and apply strategic and normal form game concepts to analyse real-world conflict situations and aid decision-making.

• Apply game-theoretic concepts in agent-based simulations in unstructured and structured populations, including spreading and decision-making on networks.

• Transfer game-theoretic concepts to model social, economic or biological problems and analyse the models through mathematical reasoning and computer simulations.

Office hours

Tuesdays 10:00am-11:50am. My office is on the second floor of the School of Computer Science building, room 215.

Collaboration Policy

• Assignments. Students are allowed, and even encouraged, to discuss assignment briefs but not share solutions as this will constitute cheating. Likewise, using ChatGPT or similar LLMs must not replace student work and can only be used as a tool to assist the student learning in accordance with the University regulation and code of conduct.

• Exam. No collaboration is allowed.

Attendance

Students are expected to attend all scheduled lectures. You are welcome to attend all the (optional) office hours provided by the module lecturers and teaching assistants.

Module Content

All module materials can be downloaded from the links below.