In complex systems, certain characteristics produce certain patterns. Small changes can generate large impacts. This patterns emerge from the system.
We can use this patterns in our models.
▪ Patterns for Model Structure: what observed patterns seem to characterize the system and its dynamics, and what variables and processes must be in the model.
If a model is too simple, it neglects essential mechanisms of the real system, limiting its potential to provide understanding and testable predictions regarding the problem it addresses. If a model is too complex, its analysis will be cumbersome and likely to get bogged down in detail. We need a way to find an optimal zone of model complexity, the “Medawar zone”
▪ Patterns for Contrasting Alternative Theories: it is about contrasting alternative decision models or also called theories. First, alternative theories of the agent's decisions are formulated. Next, characteristic patterns at both the individual and higher levels are identified. The alternative theories are then implemented in a bottom-up model and tested by how well they reproduce the patterns.
▪ Patterns for Parameters: CPattern-oriented modeling can reduce uncertainty in model parameters in two ways.
First, it helps make models structurally realistic, which usually makes them less sensitive to parameter uncertainty.
Second, the realism of structure and mechanism of pattern-oriented models helps parameters interact in ways similar to interactions of real mechanisms. It is therefore possible to fit all calibration parameters by finding values that reproduce multiple patterns simultaneously. This technique is known as “inverse modeling”.