200
X-Machines for Agent-Based Modeling: FLAME Perspectives
FIGURE 7.9: The agent-based modeling simulation result with stochastic
model. The circle shows missing data points in agent-based results using same
initial settings in both models.
FIGURE 7.10: Zoom in to find shortest possible error between simulated
results in agent-based, stochastic and original datasets.
sprintf(data, "%d", protein_dist[i]);
fputs(data, file);
}
fputs("\n", file);
//close the file
(void)fclose(file);
Researchers have compared modeling techniques, such as Norling’s technique [143] comparing a systems dynamics and an agent-based model of a
food web evolution. In this experiment, a few points can be considered.
• Modelers can discover new details about the model. In equation models, because equations collectively represent agent function as one programming code, modeler is robbed with this opportunity to find new
behaviors as a result of this analysis.