T332 Simulation and Modelling
This document addresses the content related abilities, with reference to the module. Abilities
of thinking, learning, problem solving and team work, communication, debating and
defending are addressed by the system wide curricular practices at the institute.
Module Description
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
that underlie the design of games such as: feedback, randomness, physics, human behaviour,
AI, natural phenomenon, queues, flow, socio-economic relationships etc.
Module Objectives
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, phenomenon, 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))
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, variates, 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 replication
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.
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.
Module Syllabus
The module helps students consider the design of simulations to provide an immersive user
experience within a realistic game world represented by an interactive model. In creating
such worlds students are introduced to the contemporary tools and professional practices
whilst learning the craft of modelling realistic world features such as, events, dynamic
(systems), social (agents), human behaviour, nature, physics, and environments.
Each lesson challenges students to learn through practice and team work, the process,
principles and the tools in designing, modelling, validating and verifying simulations
(whether analog to digital)for immersive fidelity.
At the end of the module students will be required to develop and deliver on practical
assignments and projects that are suited for a professional portfolio.
Module Coverage
1.
2.
System Dynamics of Games
Theme: Feedback system
Building a System Dynamic Model: Causal
Loop Diagram
Theme: Identifying Positive Vs Negative
Feedback Systems
Allocated time per day
(One day-One problem PBL pedagogy)
Discussions in
Resource
Formal Lab
Study Cluster
gathering
Experiment
and team
work
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System Dynamic Modelling - Stock and
Flow
Theme: Stock and Flow Diagram
Modeling Systems in Physical Live &
Application Levels for Study
Theme: Roller Coaster
Systems Thinking: Stochastic vs
Deterministic
Theme: Chess, Snakes and Ladders
System Thinking: Randomness
Theme: Randomness in game
Modelling Queues and Social Behaviour
Theme: Supermarket
Modelling and Simulating Intelligence:
Kinematic Movement
Theme: Kinematic Movement
Modelling and Simulating Intelligence:
Theme: Steering Behaviour
Modelling and Simulating Intelligence:
Theme: Intelligent Pathfinding (Maze)
Modelling and Simulating Intelligence Theme: Decision Tree
Modelling and Simulating Intelligence Theme: AI(Tic-Tac-Toe), Finite State
Machine
Modelling and Simulating Intelligence Theme: Robots: Reactive Agents: Vacuum
Cleaner-Cum-Land-Mine Detector
(Intelligent Agent)
Modelling and Simulating Natural or
Environmental Phenomenon.
Theme: Basic of a particle system
Modelling and Simulating Natural or
Environmental Phenomenon –
Theme: Particle System Terrain. Collision
Particles (maths)/Physics
Total = 15 Problems = 90 hours
Strictly Confidential. For Articulation Purpose Only.
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