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Multiple Realizability

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FunctionalismIdentity Theory (Type and Token)+1 more→→Computational Theory of MindDownward Causation+8 more
multiple-realizability Putnam reduction special-sciences

Core Idea

Multiple realizability, introduced by Hilary Putnam, is the observation that a single mental state type (such as pain) can be physically realized by very different material substrates across different species, and potentially in artificial systems. A human, an octopus, and a hypothetical silicon robot might all be in pain, yet have entirely different physical implementations. This undermines type identity theory, which requires a one-to-one correspondence between mental types and neural types. Multiple realizability became a cornerstone argument for functionalism and for the autonomy of the special sciences (psychology, economics) from lower-level physical sciences.

How It's Best Learned

Consider Putnam's original argument and Fodor's defense of the special sciences. Then engage with responses: Kim's challenge that multiply realizable properties cannot be causally efficacious, and the suggestion that mental types should simply be construed more locally (human pain, octopus pain).

Common Misconceptions

Explainer

To see why multiple realizability matters, you need to hold two things in mind simultaneously: the functionalist picture you've already studied, and the identity theory it was partly designed to replace. Type identity theory made a bold, clean claim: pain just *is* a particular brain state — say, C-fiber firing. Mental state types are neural state types. This is appealingly simple and clearly physicalist. The question is whether it's true.

Hilary Putnam's multiple realizability argument starts with a thought experiment. Consider pain across species. Humans feel pain; so (we have good reason to believe) do octopuses, mice, and potentially many other creatures. But human nervous systems and octopus nervous systems are radically different. The neural state that realizes pain in a human is almost certainly a different physical state from whatever realizes pain in an octopus. If pain were identical to a specific neural type, it would follow that human pain and octopus pain are simply *different mental states* — which contradicts our reason for calling them both pain in the first place. Extend this to hypothetical silicon-based intelligences, and the point becomes sharper: if anything that functions as pain counts as pain, then mental types cannot be identified with any particular physical type.

This is not a claim that mental states are non-physical. Each particular instance of pain — your pain right now — is physically realized in your neural tissue. Multiple realizability is a point about types, not tokens. It says there is no single physical type that corresponds to the mental type "pain." Every token pain is physical; no physical type exhausts what pain is. The distinction matters: you can be a thoroughgoing physicalist and accept multiple realizability, as long as you give up type reductionism.

The philosophical payoff is enormous. If mental state types are defined by their functional roles — what causes them, what they cause, how they interact with other states — rather than by physical composition, then psychology becomes an autonomous science. You can have genuine psychological laws ("pain causes avoidance behavior") that hold across species with different physical implementations. This is Jerry Fodor's point about the special sciences: psychology, economics, and ecology study patterns that cannot be reduced to physics without loss, because the same pattern can be physically realized in many ways.

The main challenge, pressed by Kim among others, is that multiply realizable properties may not be causally efficacious at the type level — if "pain" picks out different physical states in different creatures, what single causal power does it name? Critics also question whether human pain and octopus pain really do share a common mental type, or whether we are just using the same word for functionally similar but ultimately distinct states. These challenges don't defeat multiple realizability, but they show that the functionalism it supports still faces serious pressure — pressure you'll examine further when you reach the Chinese Room and related debates.

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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 → Boolean Type and Truth Values → Comparison Operators and Boolean Tests → Logical Operators and Boolean Algebra → Boolean Algebra and Fundamental Laws → Logic Gates Fundamentals → Implementing Boolean Functions with Gates → Karnaugh Map Simplification → Combinational Circuit Design → Flip-Flops and Latches → Finite State Machines (FSMs) → Deterministic Finite Automata (DFA) → Nondeterministic Finite Automata (NFA) → Two-Way Finite Automata → NFA to DFA Conversion (Subset Construction) → DFA Properties and Minimization Algorithms → Regular Languages: Definition and Characterization → Context-Free Grammars (CFGs) → Pushdown Automata (PDA) → Equivalence of CFGs and Pushdown Automata → Closure Properties of Context-Free Languages → Limitations of Context-Free Languages → Pumping Lemma for Context-Free Languages → Turing Machines → Variants of Turing Machines and Equivalence → Universal Turing Machine and Self-Simulation → Church-Turing Thesis and Computability → Functionalism → Multiple Realizability

Longest path: 97 steps · 622 total prerequisite topics

Prerequisites (3)

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