Integrated modeling: a modeler’s perspective
This page contains a brief outline of its final contents.
A “one model fits all” paradigm is inappropriate for understanding and modeling complex reality – for example, linked human-natural systems. Integrated modelling brings together multiple modeling techniques into a single platform, including:
- Spatial data and GIS for visualizing both input and output maps;
- Equations and look up tables, typical deterministic physical science models;
- Bayesian probabilistic models, expert-based approaches that operate well in data poor conditions;
- Process-based models, representing ecological processes dynamically when appropriate knowledge is available;
- Agent-based models, representing ecological and social agents dynamically and interdependently.
Models and data are developed and contributed independently, one concept at a time, without a need for coordination:
- Modellers can contribute their models and data to the network;
- Models and data can become available to the other users, embracing the open-data paradigm but respecting confidentiality where needed;
- Web-based technology enables data and model sharing and model processing;
- Artificial Intelligence ranks and selects the most appropriate models for the context of interest;
- Readable, rich documentation of the models used and the rationale of their choice is built automatically.