HIERARCHAL INDUCTIVE PROCESS MODELING AND ANALYSIS ...

3 COMPUTATIONAL RESULTS
The main topic in this paper, is to determine how to optimize the usage we make of
HIPM to assist scientists in there decision making process when it comes to selecting
a model that most accurately represent an ecosystem. The first need is to narrow
down the number of possible good fit models capable of describing the system. We
did this feeding additional time series about one of the state variable into HIPM,
thus providing more constraints; so did this assumption hold true
Secondly, if
adding more constraints to HIPM does reduce that number, are observations for a
specific state variable holding more reducing power than the other state variables
The data collected helped us answer these questions as well as discuss the efficiency
of HIPM in its current state.
There were thirty-one different experiments performed, each returning a measure of
fit value (reMSE) every one of the 1120 models tested in every experiment. This
makes for a large amount of data to analyze. To get a better idea of what this data
looks like, the measures of fit values of models that had an reMSE between 0 and
2 were graphed, ranking and graphing them from lowest to highest (see Figure 4, 5
and 6) value. We did not look at reMSE higher than 2.0 since, as stated previously,
models with reMSE higher than 1.0 are typically classified as poorly performing
models as it indicates a very large difference between observed and expected values.
We estimated that the (0,2) range would be sufficient for our purpose, as it would
encompass most models. Based on these initial results we decided to pick an reMSE
of 0.5 as our good fit model cutoff; any model under that cutoff is considered of good
fit. This choice of cutoff was made because the multiple graphs seemed to exhibit a
turning point or slight step pattern around this reMSE value, such as portrayed in
the graph for experiments 1, 5 or 20.
20