Transitions of Reasoning Mechanisms in AI: A Quantitative Benchmarking Study of Accuracy and Cost-Effectiveness

Abstract

This work rank-orders quantitatively nine historical reasoning paradigms: inductive, deductive, abductive, analogical causal, fuzzy, Chain-of-Thought, Tree-of-Thought, and knowledge graph by accuracy per GPU demand using a currency free 1–5 Reliability Index. A Quantative benchmarking study which symbolically rungs (Index 1–2) provide 0.54 Hits@10 on edge hardware, while generative methods (Index 4–5) attain 0.67 under cloud GPUs. Every benchmarked method operates on either free Colab or one RTX 3060 alone, ensuring replicability by undergraduate researchers. Accompanying open notebook repository converts the traditional narrative into an evidence-based, hardware-aware decision map.