ARTICULATION DOCUMENT
T332 Simulation and Modelling
MODULE STRUCTURE
Module Category: Diploma in Game Design
Level (year of study): Year 2
Credit Units: 4
Curriculum Hours: 90
Contact Hours: 60 over 15 weeks (4 hours per week)
Module Assessment: Continuous Assessment: 40%
- 15 Daily Grades
Summative Assessment: 60%
Understanding Test 1: 15% Written, Open Book, Online
Understanding Test 2: 15% Graded Assignment
Understanding Test 3: 30% Written, Open book, Online
SECTION 2: MODULE OVERVIEW
T332 Simulation and Modeling (for Game Designers) is a practice and problem based
learning module that takes an introductory look at exploring the relationship between
simulation and games. Games, share some similarities with simulations in that they are
created to imitate or replicate aspects of the real world over time (more specifically the real
world systems) by making representation of real world systems as abstract models. The
module introduces basic (hands-on) forms of modelling to simulate and experiment with
various kinds of real world systems across the lessons. By understanding how a variety of
fundamental systems (behaviour) are modelled students can understand better system
rules
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Simulation and Modelling
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that underlie the design of games such as: feedback, randomness, physics, human behaviour,
AI, natural phenomenon, queues, flow, socio-economic relationships etc.
MODULE OUTCOMES AND LEARNING OUTCOMES
This module is designed to meet the following outcomes:
a. Students will learn fundamental system modelling skills that will enhance the design of
game worlds and systems (for entertainment, training, education etc.). By modelling
types of real world systems for simulation, students will become aware that games as a
form of interactive simulation model a variety of systems i.e. simulations used as games,
simulations in games, games used as simulations.
b. Learn critical systems thinking, analysis and theory to gain insight into real world systems
behaviour and rules [1].
c. Build language and vocabulary of the concepts of simulation, models and systems theory
and their interrelationships.
d. Understand process and stages of simulation such as: problem formation (what to model),
modelling/abstraction, experimentation, verification and validation and calibration
(REFINEMENT) etc.
e. Students can select and develop class work into portfolio pieces. NOTE: this course is NOT
about learning a 3D modelling software.
The module is further divided into twelve learning objectives involving industry skills and
knowledge required to support the outcomes of the module:
1. Understand that Games, share some similarities with simulations in that they are created
to imitate or replicate aspects of the real world over time (more specifically the real
world systems) by making representation of real world systems and behaviours) as
abstract models that describe systems in terms of state, entities, attributes, processes,
events, activities, queue, creating, scheduling, randomness, constants, variable, variates,
and distribution etc. [1] [2] [3].
2. Understand systems consist of entities (or subsystems) interacting together to accomplish
a goal (over time) [1] [2]. There are many types of systems that exist such as abstract
(open, closed, reinforcing, balancing and even multi-agent systems etc) that is found in
different domains of reality such as: mechanistic, living animate (human behaviour,
intelligence robots), ecological, environmental (physics, henomenon, terrain), social
(agent, lifts, traffic, economics.),that can range from simple to complex Ecosystems.
[3][4][5][6]
3. Understand that (actual real world) systems can be modelled as a combination of the
following:
 Deterministic vs Stochastic models (Non-random predictable vs Random
unpredictable). Both models can be either static or dynamic.
 Static vs Dynamic models (Snapshot at single point in time vs Changes over Time).
Dynamic models can be Discrete vs Continuous models (changes at finite number of
events/time (finite states) vs continually changing over time (infinite states))
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Simulation and Modelling
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4. Understand that models can be described in variety of forms such as: physical, conceptual
(mathematical, logical (processes)), specification, application (software or programming
language) and learn to apply modelling in these forms across different types of (systems)
models. Know that the use of models have their limitations and strengths [1]. NOTE:
Some well-known mathematical models such as those in Game Theory are applicable to
Multi-agent systems.
5. Understand that a model is an abstract representation of an actual real world system and
describes a system(s) in terms of state, entities, attributes, processes, events, activities,
queue, creating, scheduling, randomness, constants, variable, varieties, distribution, etc..
6. Understand that a simulation is an imitation of a real world system (process, device, social
situation etc.) over time and is the operation of a (interacting) model(s) that represents
the system being simulated [1] [2]. As such there are a various types of simulations such
as: live, virtual and constructive, but all are interactive and immersive to a degree [3].
7. Understand that with the ability to model and simulate, the role and purpose of a
simulation is a cost-effective and alternative mean to study and analyze real world
system behaviour (e.g. emergent behaviour) to inform decision-making (esp. for
comparative designs choices)
8. Understand that simulations can be effective means to study a system as they can
embody the qualities such as:
a. Ability to compress time, expand time
b. Ability to control sources of variation
c. Avoids errors in measurement
d. Ability to stop and review
e. Ability to restore system state
f. Facilitates replicationg.
g. Modeller can control level of detail
9. Understand and be able to apply the fundamental process of simulation for study to
include phases such as: problem formation (Be able apply to identify, abstract, analyse
real world system(s) and the underlying entities and their interactions (relationships e.g.
cause and effect).), modelling, experimentation, verification and validation and
calibration (REFINEMENT).
10. Understand that there are a variety of approaches/paradigms to modelling for
simulation e.g. Agent based. Discrete events etc [1]
11. Understand and apply modelling by
a. Creating models such as: physical, conceptual (e.g. agent based. Discrete events etc.)
or by application.
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b. Across different types of types of (system) models such as: Deterministic vs,
Stochastic, Static vs Dynamic, Discrete vs Continuous. NOTE: tools will include likes of
NETLOGO and MINDSTORM etc.
c. Understand the use of the models, the limitations and their strengths [1]
12. Understand that there are wide array of applications of SIMULATION apart from games
(in fact so long as there are an identifiable system(s)) and at the same time understand
the use of the multitude of applications of simulation and modelling in games.
Allocated time per day
Module Coverage
Discussions in
Study Cluster
1. System Dynamics of Games
Theme: Feedback system
2. Building a System Dynamic Model:
Causal Loop Diagram
Theme: Identifying Positive Vs
Negative Feedback Systems
3. System Dynamic Modelling - Stock
and Flow
Theme: Stock and Flow Diagram
4. Modeling Systems in Physical Live &
Application Levels for Study
Theme: Roller Coaster
5. Systems Thinking: Stochastic vs
Deterministic
Theme: Chess, Snakes and Ladders
6. System Thinking: Randomness
Theme: Randomness in game
7. Modelling Queues and Social
Behaviour
Theme: Supermarket
8. Modelling and Simulating
Intelligence: Kinematic Movement
Theme: Kinematic Movement
9. Modelling and Simulating
Intelligence: Steering Behaviour
Theme: Steering Behaviour
10. Modelling and Simulating
Intelligence: Intelligent Pathfinding
Theme: Maze
11. Modelling and Simulating
Intelligence - Decision Tree
Resource
gathering
and team
work
Skills acquisition
and practice
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Simulation and Modelling
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Theme: Decision Tree
12. Modelling and Simulating
Intelligence - Finite State Machine
Theme: AI Tic-Tac-Toe
13. Modelling and Simulating
Intelligence - Intelligent Agent
Theme: Robots: Reactive Agents:
Vacuum Cleaner-Cum-Land-Mine
Detector
14. Modelling and Simulating Natural or
Environmental Phenomenon.
Theme: Basic of a particle system
15. Modelling and Simulating Natural or
Environmental Phenomenon –
Particle System
Theme: Terrain. Collision Particles
(maths)/Physics
Total = 15 Problems = 90 hours
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TEACHING AND LEARNING
This module equips students who wish to pursue a career in the Creative Industries with
necessary creative, critical, and technical skills. The module emphasise traditional
intellectual skills in terms of writing short reports, and conducting appropriate research
when preparing for projects. Students are routinely required to conduct seminar discussions
and presentations. The Module inspires and nurtures creative expression, in terms of both
form and content, and in the context of both individual and group productions. Students are
encouraged to analyse contemporary culture to develop their area of expertise. Throughout
the module, engagement with new, digital technologies is emphasised.
The module is predominantly delivered via a problem based learning style curriculum.
However, up to 15% of the module uses a technical hands-on tutorials style curriculum. The
module is predominantly taught in a classroom and also involves a mini project with a
simulated industry client.
LEARNING RESOURCES
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Various online resources and tutorials that covers the daily topic and example of
application.
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Simulation and Modelling
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