Pete Loucks – Integration and Implementation Insights

By Pete Loucks

How does a modeler know the ’optimal’ level of complexity needed in a model when those desiring to gain insights from the use of such a model aren’t sure what information they will eventually need? In other words, what level of model complexity is needed to do a job when the information needs of that job are uncertain and changing?

Simplification is why we model. We wish to abstract the essence of a system we are studying, and estimate its likely performance, without having to deal with all its detail. We know that our simplified models will be wrong. But, we develop them because they can be useful. The simpler and hence the more understandable models are the more likely they will be useful, and used, ‘as long as they do the job.’

By Pete Loucks

When computer technology became available for developing and using graphics interfaces for interactive decision support systems, some of us got excited about the potential of directly involving stakeholders in the modeling and analyses of various water resource systems. Many of us believed that generating pictures that could show the impact of various design and management decisions or assumptions any user might want to make would give them a better understanding of the system being modeled and how they might improve its performance.

We even got fancy with respect performing sensitivity analyses and displaying uncertainty. Our displays were clear, understandable, and colorful. Sometimes we witnessed users even believing what they were seeing. We occasionally had to remind users that our models were, and would continue to be, approximations of reality, at best. It was fun developing and using such tools, and indeed today most models that are used to analyze river basins, groundwater, and coastal zones incorporate interactive, graphics-based, decision support frameworks.

Model complexity – What is the right amount?

Moving from models that synthesize to models that innovate