During this period, complexity theory deepened as a unified framework for assessing computational difficulty under explicit resource constraints, using reductions to relate problems across classes and differentiating counting from decision. The emergence of algorithmic information theory added a Kolmogorov-like perspective on finite sequences, linking generation length to substring frequencies and informing views on randomness and description length. Research also traced how different computational models relate through resource bounds, connecting space, time, and circuit depth, and extending hardness results to restricted structures such as particular graph classes.