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.