Yearbooks

2025

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In recent years, the boundaries between different simulation paradigms such as discrete event simulation, system dynamics and agent-based models have become less distinct. Improvements in computational efficiency also allow much richer and complex models to be built. This module introduces system dynamics (SD) and agent-based models (ABM) as a class of computational models. While SD is concerned with understanding the dynamical interactions amongst the elements of a system covering (man, machine, materials, methods, money and management) in a bid to gain a measurable insight into a system’s local and/or global behaviour over a given horizon time for effective decision making, ABS on the other hand is concerned with deploying a collection of autonomous decision-making entities called agents. Inhere, each agent, individually assesses its situation and makes decisions on the basis of a set of rules. ABS addresses autonomous agents and their interactions with other agents, and their surrounding environments. The module content covers basic theoretical foundations of ABM and then focuses on a few specific application areas where ABM is used for decision-making covering: pedestrian and transport models; production and logistics; as well as biology.

Theme 1: System Dynamics Modelling 

Block Week1:     

1. System Behavioural Patterns: Exponential growth, goal seeking, s-shaped growth, oscillatory growth.
2. Delays, Smoothing and Averaging: Pipeline material flow delays, third order exponential delays, information averaging (moving average, exponential smoothing, information delays).
3. Representing Decision Processes:  Modelling Decision Processes (Types of Decision Models)-- weighted-average decision models, floating goals, multiplicative decision rules
4. Nonlinearities: Nonlinear responses.
5. Initial Conditions: Initialising a model to equilibrium, Simultaneous initial conditions.
6. Vensim Software Hands-on Demo: Creating and converting causal loop diagrams to stock and flow diagrams, and conduct of simulations.

Theme 2: Agent-Based Simulation

Block Week 2:

1. Discrete event simulation overview.
2. Introduction to agent-based simulation and modelling philosophy premised on (routine deployment of human interaction).
3. Agent-based simulation modelling as a decision support tool (based on the principle presented in Macal (2016)).
4. Research in agent-based modelling covering (Design Research Methodology- (as an appropriate methodology for simulation).

Block Week 3:

1. Java for AnyLogic
2. Agent-based modelling in AnyLogic