Model structure--the relationship between model elements.
Calibration--using data-sets to ensure that model matches. Differences between these two constitutes specification error.
Validation --Tests to measure models predictive capacity. Requires a second data-set.
Model Development Principles
• Concentrate on transparency. Models should be communicable. This necessitates the development of nested modeling.
• Limit interconnectivity between model elements to the minimum possible.
• Model structure should be transparent. Relationships between model elements shoul be clear, and should (when possible) correspond to participants pre-existing mental categories.
• Equations available, data-sources documented. When possible, a model should be packaged to contain it's own documentation.