Integrated modeling: a modeler’s perspective

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.