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Process Reliabilism

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The Justified True Belief Account of KnowledgeEpistemic Luck+4 more→→Bayesian EpistemologyFallibilism and Justified Belief+3 more
reliabilism Goldman process externalism generality-problem

Core Idea

Alvin Goldman's process reliabilism holds that a belief is justified if and only if it is produced by a reliable cognitive process — a process that tends to produce true beliefs in the actual world and in nearby possible worlds. Perception, memory, and valid deductive inference count as reliable processes; wishful thinking, astrology, and hasty generalization do not. Reliabilism is an externalist theory: the agent need not be aware that the process is reliable for the belief to count as justified. This approach promises to handle Gettier cases (Gettier beliefs arise from processes that fail to reliably track truth in the relevant case) and explain why children and animals can have knowledge.

How It's Best Learned

Apply reliabilism to a range of cases: perceptual beliefs, clairvoyance, testimony, inference to the best explanation. The generality problem — how to individuate the relevant process type — is the central technical challenge to work through.

Common Misconceptions

Explainer

From your work on justified true belief, you know the classical account: knowledge is believing something true for good reasons. From Gettier problems, you know that account fails because good reasons can lead to true beliefs by accident. Various responses to Gettier tried to patch the definition — adding a "no false lemmas" clause, requiring causal connections to the fact — but each patch faced new counterexamples. Process reliabilism, developed by Alvin Goldman, steps back and asks a different question: instead of analyzing what makes a *belief* justified, what makes the *cognitive process that produced it* a good one?

Goldman's answer is reliability: a cognitive process is epistemically good if it tends to produce true beliefs. Perception counts — your visual system reliably tracks features of the physical environment. Memory counts — it reliably preserves information about your past experiences. Valid deductive inference counts — if your premises are true and the inference is valid, the conclusion must be true; reliability is maximal. What doesn't count: wishful thinking (wanting something to be true is no guide to whether it is), reading horoscopes, or forming beliefs through pure random guessing. These processes have low truth-producing ratios in the actual world, so beliefs produced by them lack justification.

The critical innovation is externalism: you don't have to know that your belief-forming process is reliable for your belief to be justified. A child who forms accurate perceptual beliefs doesn't need to understand optics or neuroscience; a dog that correctly identifies its owner doesn't need to reflect on the reliability of its nose. Justification is constituted by the actual relationship between the process and truth — it's a fact about the world, not about what the agent can introspect. This contrasts with internalist theories (like classical foundationalism), which require justification to be accessible from the agent's first-person perspective. Internalism struggled to explain how children and animals could count as knowing anything; reliabilism handles them naturally.

The main technical challenge for reliabilism is the generality problem: every token belief is produced by a process that can be described at many levels of generality. My current visual belief was produced by "using my eyes," but also by "using my eyes in good lighting," and also by "using my eyes while sitting in this chair in this building on this day." Which description picks out the relevant process type for evaluating reliability? "Using eyes" is reliable; "using eyes under certain specific conditions on a specific day" might be reliable or unreliable depending on how narrowly we define it. There is no principled answer that Goldman's original formulation provides. This doesn't refute reliabilism, but it means the theory is incomplete until we can say, non-arbitrarily, which process description is the right one to evaluate. Working through this problem is the central task of reliabilist epistemology beyond the introductory level.

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Prerequisite Chain

Understanding Zero → The Number Zero → Counting to Five → Counting to 10 → Counting to 20 → Counting a Set of Objects Up to 20 → Cardinality: The Last Number Counted → Matching Numerals to Quantities → Subitizing Small Quantities → Addition Within 10 → Number Bonds to 10 → Addition Within 20 → Doubles and Near Doubles → Doubles Facts Within 10 → Near Doubles Facts Within 20 → Mental Math Strategies for Addition → Mental Math: Adding and Subtracting Tens → Addition Within 100 → Repeated Addition as Multiplication → Multiplication as Equal Groups → Multiplication: Arrays → Basic Multiplication Facts (0s, 1s, 2s, 5s, 10s) → Multiplication Facts Within 100 → Division as Equal Sharing → Division as Grouping (Measurement Division) → Division: Grouping (Repeated Subtraction) Model → Division: Fair Sharing Model → Division as Equal Sharing → Division as Grouping → Basic Division Facts → Division Facts Within 100 → Multiplication and Division Fact Families → Relationship Between Multiplication and Division → Division Facts as Inverse of Multiplication → Remainders and Quotients in Division → Division Word Problems → Multi-Step Word Problems → Solving Multi-Step Word Problems → Multiplication Word Problems → Division Word Problems → Introduction to Long Division → Factors and Multiples → Prime and Composite Numbers → Equivalent Fractions → Relating Fractions and Decimals → Decimal Place Value → Integers and the Number Line → Comparing and Ordering Integers → Absolute Value → Adding Integers → Subtracting Integers → Multiplying Integers → Introduction to Exponents → Order of Operations → Integer Order of Operations → Variable Expressions → The Distributive Property → Variables and Expressions Review → Introduction to Polynomials → Adding and Subtracting Polynomials → Multiplying Polynomials → Factorial → Permutations → Combinations → Counting Principles: Addition and Multiplication Rules → Introduction to Graph Theory → Propositional Logic Foundations → Logical Equivalences → Boolean Algebra → Introduction to Propositional Logic → Introduction to Predicate Logic (First-Order Logic) → First-Order Logic Syntax → Terms and Atomic Formulas in FOL → Variable Binding and Scope → Open and Closed Formulas in First-Order Logic → Variable Substitution and Capture-Avoidance in First-Order Logic → Quantifier Instantiation Rules in First-Order Proof Systems → Universal Quantification: Meaning and Scope → Free Variables and Bound Variables → Substitution and Instantiation in Predicate Logic → Terms and Atomic Formulas → Formulas and Well-Formed Expressions → Structures and Interpretations → Model Interpretation and Satisfaction → Interpretation, Truth, and Satisfaction of Formulas → Logical Consequence and Entailment → Soundness Theorem and Validity of Proof Systems → Deductive Reasoning and Formal Proof Systems → First-Order Resolution → Propositional Resolution → Semantic Tableaux (Propositional) → Semantic Tableaux (First-Order) → Decidable Fragments of First-Order Logic → Gödel's Completeness Theorem for First-Order Logic → Gödel's Incompleteness Theorems → Introduction to Intuitionistic Logic → Introduction to Modal Logic → The Sensitivity Condition and Tracking Truth → Anti-Luck Conditions and Sensitivity → Epistemic Luck → Responses to the Gettier Problem → Process Reliabilism

Longest path: 102 steps · 546 total prerequisite topics

Prerequisites (6)

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