The higher cost values are achieved for the exponential distributed service time as compared to gamma distributed service time.
However, spatial population structure promotes cooperation and it allows cooperators to invade at higher cost values with regard of both types of costs (i.e. compare 5.e with 5.g and 5.f with 5.h).
The results for cost-sensitive learning with MetaCost using varying misclassification costs are shown in Tables 7 and 8. Classifiers do not seem to be very sensitive to the misclassification cost so performance variations were observed at relatively high cost values.
Our results also indicate, in line with the general conclusion of previous models that spatial structure favours cooperation [ 6, 8, 19, 79], as high values of cooperation evolve for both public actions (hunting and group defense, see large red bubbles) even at high cost values.
Also for higher cost values the instable fix points move closer to the stable fix points, which means that the invasion of cooperators in a population of defectors becomes harder, while the invasion of defectors into a cooperative population becomes more likely.
This means that they achieve the criteria more efficiently that those with a higher cost-value ratio, and therefore represent better value for money.
A validation study on seismic loss assessment in Turkey (Bal et al. 2008) proposes higher cost ratio values than those typically reported in other studies.
This is, however, again due to the decreasing coupling strength which induces higher cost function values that in turn are only partially compensated by the subsequent parameter changes.
The best model was selected on the basis of a high correlation coefficient (r), lowest total cost, highest cost difference, and lowest RMSD values.
During the testing phase of the tool, the preventative programme (X) was ranked highest in cost-value ratio, but only third by benefit score alone.
