AIMS
SESMO's Compendium on Fundamentals of Modelling, Principles and Good Practice— is a collection of educational resources designed for students, researchers, and practitioners that provides in-depth instruction in significant specialties in socio-environmental modelling topics.
A long-term virtual special issue with defined Modules (examples below) and Guest Editors
- Articles are research or review type with on-line educational resources
- Written by experts with constructive reviewing and guest editing
- Open access and free, supported by the publisher iEMSs
- Audience spans undergraduate, graduate, researchers and practitioners
- Articles layered, building depth from plain English introductions to references to further literature
- Case studies can be used as a method to illustrate generic issues in modelling and lessons
- Articles are coordinated, referring to relevant literature covered in the same module and other modules. To achieve this, the guest editors will work collaboratively with the editors to cultivate these links.
- Each article is published as it is accepted and proofed, and identifies the module and guest editor(s) to which it belongs
- An overview article will be published by the Guest Editors in SESMO initially so as to indicate the above, the aims of the Compendium and the coverage of the various Modules.
The proposed Modules for the Compendium are listed below. Other topics, beyond those listed, will be sourced over time. If you are interested in contributing as Guest editor or author, please contact Sondoss Elsawah (s.elsawah@adfa.edu.au) or Tony Jakeman (tony.jakeman@anu.edu.au).
SCOPE OF THE COMPENDIUM
MODULES and articles
A) OVERARCHING FUNDAMENTALS
- Types of Environmental Systems
- Types of models
- Introduction to Steps, phases and overall principles and fundamentals
- Problem framing
- System conceptualisation
- Systems thinking
- Scenarios, visioning, and future methods
- Principles of human SES research
B) PARTICIPATION, ENGAGEMENT AND CO-DESIGN
- Gamifying
- Participatory modeling
- Process design and evaluation
- Behavioural aspects related to modeling (e.g. biases, social norms…etc)
- Co-design methodologies
C) UNCERTAINTY, SENSITIVITY AND PARAMETER ESTIMATION
- Types of uncertainty: aleatory, epistemic etc.
- Basic Monte Carlo to more sophisticated approaches
- Holistic approaches
D) Decision Making Under Deep Uncertainty (DMDU)
E) OPTIMISATION
- Principles and different purposes/contexts (optimizing performance of a model, such as in calibration) versus tradeoffs of model outputs
- Methods: Steepest descent, genetic algorithms, etc.
- Multi-criteria, cost-benefit etc.
F) FORECASTING
G) MODEL BASED MANAGEMENT and the -ilities
- Model semantics
- Model documentation
- Reliability
- Reusability and Composability
- Interoperability
H) AI/Machine Learning
- What are AI and ML: where valuable
- Role/utility and types of emulation/surrogate modelling
- Deep learning and ANNs
I) INTEGRATED MODEL PARADIGMS
- OVERVIEW
- Bayesian Networks
- System Dynamics
- Fuzzy Cognitive mapping
- ABMs
- Multi-criteria decision analysis
- Hybrid models
J) BIG DATA
K) SCALING
L) VISUALISATION
M) SOFTWARE AND WORKFLOW
N) JUSTICE AND EQUITY
- Modelling and ethics