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ST222 Games, Decisions and Behaviour

Lecturer(s): Dr Samuel Touchard

Prerequisite(s): ST115 Introduction to Probability or ST111 Probability A.

Commitment: 3 lectures per week. This module runs in term 1.


Throughout their history, game and decision theories have used ideas from mathematics and probability to help understand, explain and direct human behaviour.

Questions explored in the module include: What is probability? A set of axioms, a relative amount of outcomes, a belief? And how can this be elicited? What guides decision-making when outcomes are uncertain? What happens when information is only partial or ambiguous? What if there is more than one person, or how are decisions made in games? How do people perceive and evaluate probabilities and risks? Are they acting rationally or not? Which heuristics and biases come into play? Under which conditions do they occur, and how do they impact decision-making?

Answer will be embedded into theories and illustrated with practical examples from a wide range of applications including engineering, economics, finance, business, sciences, psychology and medicine.


  • Introduce students to several approaches for defining probability with an emphasis on subjective probability
  • Develop normative decision theory under uncertainty
  • Contrast this with descriptive decision theory and point out models based on behavioural sciences
  • Introduce basic game theory


  • Students will be familiar with the mathematical and philosophical basis for a number of alternative approaches to probability including subjective probability.
  • Students will be familiar with normative decision theory and can apply this to model decision-making in practical examples from a wide range of applications.
  • Students will have understood the foundation of and motivation for descriptive decision theory. They will be able to recognise, describe and model deviations from normative theory in examples.
  • Students will have basic knowledge of mathematical game theory and can apply this both to mathematical toy example games as well as use game theory to model suitable 'real world' scenarios.


A list with books and website supporting this module will be provided on the resource page for this module. To get a first taste of what the module is about, the following resources may be helpful:

  • Koerner, "Naive Decision Making: Mathematics Applied to the Social World" (Cambridge University Press)
  • Petersen, "An Introduction to Decision Theory" (Cambridge Introductions to Philosophy)
  • Kahneman, "Thinking, fast and slow" (Macmillan)

Assessment: Exam (100%)

Examination Period: April