This study explores how the complexity of rule-based systems affects the capabilities of models trained to predict these rules, specifically focusing on elementary cellular automata (ECA). The research reveals that higher complexity in rules leads to greater intelligence in models as seen in their performance on reasoning and chess move prediction tasks. Uniform, periodic, and highly chaotic systems generally result in poorer performance, indicating a specific level of complexity beneficial for intelligence.
