Course Details in 2027/28 Session


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Module Title LM Game Theory (Extended)
SchoolComputer Science
Department Computer Science
Module Code 06 40093
Module Lead Dr Jens Christian Clausen
Level Masters Level
Credits 20
Semester Semester 2
Pre-requisites
Co-requisites
Restrictions None
Contact Hours Lecture-33 hours
Practical Classes and workshops-22 hours
Guided independent study-145 hours
Total: 200 hours
Exclusions
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 allows analysing the stability of equilibria as well as numerically investigating 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 Outcomes By the end of the module students should be able to:
  • 20.1
  • 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
  • 20.2
  • Understand, explain, and apply strategic and normal form game concepts to analyse real-world conflict situations and aid decision-making
  • 20.3
  • Apply game-theoretic concepts in agent-based simulations in unstructured and structured populations, including spreading and decision-making on networks
  • 20.4
  • Transfer game-theoretic concepts to model social, economic or biological problems and analyse the models through mathematical reasoning and computer simulations
  • 20.5
  • Demonstrate an awareness of the current literature in this area
Assessment
Assessment Methods & Exceptions Assessment:

Assessment:
2hr Examination (80%),
Continuous Assessment (20%)

Reassessment:

2hr Examination (100%)
Other
Reading List