42828 Introduction to Agent Based Modelling
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Credit points: 3 cp
Result type: Grade and marks
Anti-requisite(s): 42085 Modelling for Complex Systems
Description
Complex systems abound. Their emergent characteristics result from an interaction of the system's elements. Just what emergent characteristics will result from such interactions is very difficult, if not impossible, to predict for complex systems. Simulation modelling is one way to explore complex systems. In this subject, students learn how to construct and modify system models using Agent Based Modelling (ABM) or Agent Based Modelling and Simulation (ABMS).
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Characterize complex systems to identify elements suitable for development of ABMs to simulate the system interactions and characteristics. (D.1) |
|---|---|
| 2. | Design and assess the impact of modifications to an existing complex system’s ABM. (C.1) |
| 3. | Use reflection strategies to enhance students’ understanding and contribution to modelling practice. (F.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
- Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
- Reflective: FEIT graduates critically self-review their own and others' performance with a high level of responsibility to improve and practice competently for the benefit of professional practice and society. (F.1)
Teaching and learning strategies
This microcredential uses supported online learning strategies. Self-paced online learning material, including appropriate readings and videos, introduce concepts of systems and complex systems.
Case studies help develop the concepts, techniques of modelling, and the techniques of using ABM. Engaging with real models and modelling tools such as NetLogo builds skills of modelling with ABM and of analysing the outcomes of such modelling. This analysis develops the awareness of the assumptions and limitations generally inherent in any modelling effort.
Weekly asynchronous online consultations at times nominated on the LMS used (e.g. Canvas) and moderated discussion boards enable clarification of issues and peer/expert support for learning. Feedback on assessments, both automated and human, will also be available through the LMS.
Content (topics)
- Introduction to Complex systems
- Artificial society models
- The Sugarscape model - Introduction
- Artificial societies versus traditional models
- Towards generative science: Can you grow it?
- The Sugarscape Model
- Other Case Studies
Assessment
Assessment task 1: ABM Project: Apply appropriate agent based modelling process toolsets to solve the selected problem(s)
| Intent: | Demonstrate their ability to formulate problem, design, interpret and analyse ABM. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 and 2 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1 |
| Type: | Project |
| Groupwork: | Individual |
| Weight: | 70% |
| Length: | 2300 words |
Assessment task 2: ABM Reflection: Use reflection strategies to enhance students’ understanding about ABM.
| Intent: | To identify opportunities for improvement during the project from concept to model development. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 3 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): F.1 |
| Type: | Reflection |
| Groupwork: | Individual |
| Weight: | 30% |
| Length: | 700 words |
Minimum requirements
Overall mark of 50% or more.