Abductive, Inductive, Deductive Table

Abductive, Inductive, Deductive Table.md

	Inductive Reasoning	Abductive Reasoning	Deductive Reasoning
Description	Generalizes from examples to broader conclusions. 🧩	Infers the most likely explanation from incomplete evidence. 🔍	Applies a general rule to reach a specific conclusion. 📏
AKA	Pattern Recognition, Generalization, Bottom-Up Reasoning, Empirical Reasoning 🧠	Ockham's Razor 🪒, Inference to Best Explanation, Hypothesis Formation, Plausible Inference 🎯	Syllogistic Reasoning, Logical Deduction, Rule-Based Reasoning, Top-Down Reasoning 🧮
History & Invention	Formalized by Aristotle (384–322 BCE), popularized by Francis Bacon in the scientific method. 🧪	Coined by Charles Peirce (1839–1914) for hypothesis generation. 🧐	Developed by Aristotle, refined by Euclid in geometry (c. 300 BCE). 📏
Natural Language Example	"All the swans I've seen are white, so all swans are white." 🦢	"The ground is wet, so it probably rained." 🌧️	"All humans are mortal. Socrates is human, so Socrates is mortal." 🧑‍🏫
Math Example	"I've seen 10 even numbers, so all numbers must be even." 🔢	"The answer is 2, so it was likely 1 + 1." ➕	"If x = 2 and y = 3, then x + y = 5." ✖️
Science Example	Metals expand when heated, so all metals expand. 🔬	Diagnosing a disease from symptoms. 💉	Using laws of motion to predict falling objects. 🌍
Error Example	Concluding all swans are white from limited observations. 🚫🦢	Assuming rain caused wet grass, ignoring sprinklers. 🚫🌧️	If "All birds fly," then wrongly concluding penguins fly. ❌🐧
Step-by-Step Process	1. Observe examples. 2. Identify patterns. 3. Generalize. 🔄	1. Gather evidence. 2. Form best explanation. 3. Infer outcome. 🔍	1. Start with rule. 2. Apply to case. 3. Reach conclusion. ✔️
Strength of Conclusion	Less rigorous, likely but not certain. ⚖️	Moderately rigorous, best guess but not guaranteed. 🧐	Most rigorous, certain if premises are true. 💪
Type of Uncertainty	High, due to limited examples. 🔄	Moderate, based on best guess. 🤔	Low, conclusion follows if premises are valid. 🛑
Cognitive Effort & Time	Moderate; pattern recognition takes time. ⏳	Moderate; quick, best cause from limited data. ⏱	Varies; quick for simple cases, more effort for complex proofs. 🧠⏲️
Data Requirements	Requires many examples for generalization. 📊	Requires some evidence, not exhaustive data. 🔍	Requires clear premises, effort in complex cases. 🗂️
Typical Applications	Hypothesis formation, pattern recognition. 🧪	Diagnosis, detective work. 🕵️‍♂️	Mathematical proofs, logic arguments. 🧮
Machine Learning Application	Used in supervised learning for predictions. 🤖	Seen in anomaly detection, causal inference. 📉	Found in rule-based systems, logic programming. 📏
System 1 & System 2	System 1: Quick pattern recognition. System 2: Careful analysis. 💡	System 1: Intuitive guesses. System 2: Best hypothesis evaluation. 🧐	System 1: Rarely used. System 2: Dominates for logical, methodical thinking. 📏
Sensitivity to Biases	High: Prone to confirmation and availability bias from limited examples.	Moderate: Affected by biases like representativeness and availability when picking explanations.	Low: Logical process is robust, but biased premises lead to flawed conclusions.