Implication as NOT A OR B, verified
A -> B compared with NOT A OR B: the material implication written without an arrow. The equivalence holds on all four rows, the only false row being A = TRUE, B = FALSE, and the NAND-only form is 2 gates.
An implication promises that if A holds, B holds. It is broken in exactly one situation, A true and B false, and kept in every other, including the two rows where A is false. That is why NOT A OR B says the same thing: either the premise fails, or the conclusion holds. The engine's reading of A -> B is “if A is true, then B is true”. On the four-row table the output is true on rows 0, 1 and 3, Σm(0, 1, 3).
The equivalence check holds: implication and as_or agree on every assignment. The implication is not a tautology, counterexample A = TRUE, B = FALSE, the one broken row; it is satisfiable, witness A = FALSE, B = FALSE. The simplifier found no law that shortens the single operator. The normal-form converter applies implication elimination once, and both the CNF and the DNF are NOT A OR B. The Karnaugh map draws NOT A on cells 0 and 1 and B on cells 1 and 3, two prime implicants, deriving the same expression. The NAND-only form is 2 gates, NAND(A, NAND(B, B)); the NOR-only form needs 5.
Most languages have no implication operator, so NOT A OR B is how a rule such as “if the order is international, a customs code is required” is written: not international or has_customs_code. The thing to watch is the vacuous half: when A is false the rule is satisfied whatever B is, so a test suite with no international orders proves nothing about customs codes. The two rows with A = FALSE are true by default, and the contrapositive, converse and inverse pages show which rewrites preserve the arrow and which do not.
implication := A -> B · outputas_or := NOT A OR B · output
Exact checks
Truth table (4 rows)
| # | A | B | implication | as_or |
|---|---|---|---|---|
| 0 | FALSE | FALSE | TRUE | TRUE |
| 1 | FALSE | TRUE | TRUE | TRUE |
| 2 | TRUE | FALSE | FALSE | FALSE |
| 3 | TRUE | TRUE | TRUE | TRUE |
Plain-language reading
Beginnerimplication is true exactly when the following holds: if A is true, then B is true.
In every other case, implication is false.
About the operators:
- An “if … then” condition only constrains the case where its “if” part holds: when the “if” part is false, the condition is satisfied whatever the “then” part is. It does not say the reverse.
Intermediateimplication is true exactly when the following holds: if A is true, then B is true.
In every other case, implication is false.
Advancedimplication is true exactly when the following holds: if A is true, then B is true [material implication →].
In every other case, implication is false.
Formal notation: A → B
Logic diagram
Simplification, step by step
The engine found no law that shortens this expression; it is already as short as its rewriting laws can make it.
A -> B
Karnaugh map
- cells m0, m1
NOT A - cells m1, m3
B
Minterms and maxterms
implication(A, B) = Σm(0, 1, 3)implication(A, B) = ΠM(2)Variable order: A, B · 4 rows
Normal forms
Conjunctive normal form (CNF)
NOT A OR B1 clauses, 2 literals · complete · Implication elimination ×1
Disjunctive normal form (DNF)
NOT A OR B2 terms, 2 literals · complete · Implication elimination ×1
NAND-only and NOR-only
NAND-only form
NAND(A, NAND(B, B))2 gates · verified equivalent on every row
NOR-only form
NOR(NOR(NOR(A, A), B), NOR(NOR(A, A), B))5 gates · verified equivalent on every row
The same rule as code
Python
# -*- coding: utf-8 -*-
# Generated by Tangibool (tangibool.python/1). Do not edit by hand; regenerate from the source revision.
# Model: not stated
# Source revision: reference
# Interpretation status: not stated
"""Tangibool evaluator (decision model).
Generated deterministically from the validated Boolean model; it evaluates exactly that
model. It does not establish that the model is a complete, legal, or safe statement of a
real-world rule, and evaluating examples does not verify the rule.
Source revision: reference
Interpretation status: not stated
Exporter: 1.0.0 (format tangibool.python/1; engine 1.0.0; semantics tangibool-semantics/1)
Logic digest: d84ae6dfb625ed5edc014d42cf82776b8e4ffdef8bd21b74c6441d2704f99992
Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78
Inputs (dict keys, declared order) and generated parameter names:
'A' -> input_A
'B' -> input_B
Outputs (declared order) and their functions:
'implication' -> output_implication(inputs)
'as_or' -> output_as_or(inputs)
Assumptions (the declared scope, checked by in_scope(inputs)):
none; every complete assignment is in scope.
Input contract:
Every required input must be exactly True or False (type(value) is bool). Missing
inputs, None, strings, integers (including 0 and 1), and keys that are not declared
inputs raise InputError before anything is evaluated, including inputs that a
short-circuiting operator would never read. Results are always bool.
Scope:
Decision functions raise OutOfScopeError when the inputs violate an assumption; an
out-of-scope assignment is never turned into a False result. Pass check_scope=False
to evaluate without the scope check, and use in_scope(inputs) to test it separately.
Usage:
import tangibool_model as model
model.evaluate({'A': False, 'B': False})
# -> {'implication': True, 'as_or': True}
model.output_implication({'A': False, 'B': False})
# -> True
model.evaluate({'A': 1, 'B': False})
# raises InputError: 1 is an int, not a bool
"""
import collections.abc as _abc
from typing import Dict, Mapping, Tuple
__all__ = [
"MODEL_INFO",
"IDENTIFIER_MAP",
"INPUT_NAMES",
"OUTPUT_NAMES",
"ASSUMPTIONS",
"InputError",
"OutOfScopeError",
"minterm_index",
"in_scope",
"evaluate",
"output_implication",
"output_as_or",
]
MODEL_INFO = {
"format": "tangibool.python/1",
"mode": "decision",
"model": None,
"revision": "reference",
"interpretation": None,
"exporter": "1.0.0",
"engine_version": "1.0.0",
"semantics_version": "tangibool-semantics/1",
"logic_digest": "d84ae6dfb625ed5edc014d42cf82776b8e4ffdef8bd21b74c6441d2704f99992",
"assumptions_digest": "d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78",
"primary_output": "implication",
"unused_inputs": (),
"subcircuits": (),
}
# Stable ID -> generated identifier (reversible mapping; names are exact, escaped strings).
IDENTIFIER_MAP = {
"v_84898dbe4c1b": {"kind": "input", "name": "A", "identifier": "input_A"},
"v_1fb5957ec72e": {"kind": "input", "name": "B", "identifier": "input_B"},
"f_de30f61d6db8": {"kind": "output", "name": "implication", "identifier": "output_implication"},
"f_1f67a936bd18": {"kind": "output", "name": "as_or", "identifier": "output_as_or"},
}
INPUT_NAMES = ("A", "B")
OUTPUT_NAMES = ("implication", "as_or")
ASSUMPTIONS = ()
_DECLARED_INPUTS = frozenset(INPUT_NAMES)
_SCOPE_INPUTS = ()
_ASSUMPTION_FORMULAS = {entry["name"]: entry["formula"] for entry in ASSUMPTIONS}
class InputError(ValueError):
"""Raised when inputs are missing, undeclared, or not exactly True/False.
Every required input must satisfy type(value) is bool. Missing inputs, None, strings,
integers (including 0 and 1), and keys that are not declared inputs are rejected before
anything is evaluated. A missing input is never treated as False. The individual problems
are listed in the problems attribute.
"""
def __init__(self, problems):
self.problems = tuple(problems)
super().__init__("Invalid inputs: " + "; ".join(self.problems))
class OutOfScopeError(ValueError):
"""Raised when the inputs violate the model's assumptions (its declared scope).
Outside its assumptions the rule has no decision: this error is never a False result.
The names of the violated assumptions are in the violated attribute.
"""
def __init__(self, violated):
self.violated = tuple(violated)
details = ", ".join(name + " (" + _ASSUMPTION_FORMULAS.get(name, "?") + ")" for name in self.violated)
super().__init__(
"Out of scope: the inputs violate "
+ ("assumption " if len(self.violated) == 1 else "assumptions ")
+ details
+ ". The rule has no decision outside its assumptions; this is not a False result."
)
def _short_repr(value: object) -> str:
text = repr(value)
return text if len(text) <= 60 else text[:57] + "..."
def _checked_inputs(inputs: Mapping[str, bool], required: Tuple[str, ...], check_scope: bool) -> Dict[str, bool]:
"""Validate inputs strictly before anything is evaluated and return a plain copy."""
if type(check_scope) is not bool:
raise TypeError("check_scope must be True or False")
if not isinstance(inputs, _abc.Mapping):
raise InputError(["inputs must be a mapping from input names to True/False, got " + type(inputs).__name__])
problems = []
for key in inputs:
if type(key) is not str or key not in _DECLARED_INPUTS:
declared = ", ".join(repr(name) for name in INPUT_NAMES) or "none"
problems.append("undeclared input " + _short_repr(key) + " (declared inputs: " + declared + ")")
needed = tuple(required) + (_SCOPE_INPUTS if check_scope else ())
for name in needed:
if name not in inputs:
problems.append("missing required input " + repr(name))
for key, value in inputs.items():
if type(key) is str and key in _DECLARED_INPUTS and type(value) is not bool:
problems.append(
"input " + repr(key) + " must be True or False (type bool), got "
+ type(value).__name__ + " " + _short_repr(value)
)
if problems:
raise InputError(list(dict.fromkeys(problems)))
return dict(inputs.items())
def _require_bool_args(function: str, pairs: Tuple[Tuple[str, object], ...]) -> None:
problems = [
"argument " + repr(name) + " of " + function + " must be True or False (type bool), got "
+ type(value).__name__ + " " + _short_repr(value)
for name, value in pairs
if type(value) is not bool
]
if problems:
raise InputError(problems)
def minterm_index(*values: bool) -> int:
"""Row index of an assignment over a declared variable order.
The first value is the most significant bit, so all-False is row 0. Used for indexed
minterm (sum) and maxterm (product) forms.
"""
index = 0
for value in values:
if type(value) is not bool:
raise InputError(["minterm_index() takes True/False values, got " + type(value).__name__ + " " + _short_repr(value)])
index = index * 2 + (1 if value else 0)
return index
def _scope_values(values: Dict[str, bool]) -> Tuple[Tuple[str, bool], ...]:
"""Value of every enabled, confirmed assumption for validated inputs, in declared order."""
return ()
def _require_in_scope(values: Dict[str, bool]) -> None:
violated = tuple(name for name, holds in _scope_values(values) if not holds)
if violated:
raise OutOfScopeError(violated)
def in_scope(inputs: Mapping[str, bool]) -> bool:
"""True when the inputs satisfy every assumption (the model's declared scope).
Requires the inputs the assumptions read; raises InputError for invalid inputs.
"""
values = _checked_inputs(inputs, (), True)
return all(holds for _, holds in _scope_values(values))
# ---- Formula bodies (inputs are validated by the public functions below) ----
def _compute_output_implication(input_A: bool, input_B: bool) -> bool:
"""Decision output 'implication': A -> B"""
return (not input_A) or input_B
def _compute_output_as_or(input_A: bool, input_B: bool) -> bool:
"""Decision output 'as_or': NOT A OR B"""
return (not input_A) or input_B
# ---- Public API ----
def output_implication(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
"""Decision output 'implication' (stable ID f_de30f61d6db8).
'implication' := A -> B
Required inputs: 'A', 'B'.
Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
when check_scope is True and the inputs violate an assumption.
"""
values = _checked_inputs(inputs, ("A", "B"), check_scope)
if check_scope:
_require_in_scope(values)
output_implication_value = _compute_output_implication(values["A"], values["B"])
return output_implication_value
def output_as_or(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
"""Decision output 'as_or' (stable ID f_1f67a936bd18).
'as_or' := NOT A OR B
Required inputs: 'A', 'B'.
Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
when check_scope is True and the inputs violate an assumption.
"""
values = _checked_inputs(inputs, ("A", "B"), check_scope)
if check_scope:
_require_in_scope(values)
output_as_or_value = _compute_output_as_or(values["A"], values["B"])
return output_as_or_value
def evaluate(inputs: Mapping[str, bool], *, check_scope: bool = True) -> Dict[str, bool]:
"""Evaluate every output (declared order); each output is computed once.
Required inputs: 'A', 'B'.
Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
when check_scope is True and the inputs violate an assumption.
"""
values = _checked_inputs(inputs, ("A", "B"), check_scope)
if check_scope:
_require_in_scope(values)
output_implication_value = _compute_output_implication(values["A"], values["B"])
output_as_or_value = _compute_output_as_or(values["A"], values["B"])
return {
"implication": output_implication_value,
"as_or": output_as_or_value,
}
PostgreSQL
-- Tangibool PostgreSQL export (tangibool.postgresql/1)
-- Target: PostgreSQL 16 or later with typed boolean inputs; tested with PostgreSQL 18.3 (PGlite 0.5.8).
-- Not a claim of compatibility with other SQL dialects.
-- Model: not stated
-- Source revision: reference
-- Interpretation status: not stated
-- Exporter: 1.0.0 (engine 1.0.0; semantics tangibool-semantics/1)
-- Logic digest: d84ae6dfb625ed5edc014d42cf82776b8e4ffdef8bd21b74c6441d2704f99992
-- Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78
-- Mode: decision
--
-- Read-only: this file contains only WITH / VALUES / SELECT statements. No DDL, DML,
-- procedures, credentials, network access, or database changes. The expressions are pure
-- Boolean expressions, so their result does not depend on SQL evaluation order.
--
-- NON-NULL INPUT PRECONDITION: NULL is not a truth value of this rule. Every fragment below
-- is defined only when every input column it reads is a non-NULL boolean. The guard template
-- (section 2) marks any NULL input as 'invalid_input' and any row that violates the
-- assumptions as 'out_of_scope', and fills output columns only for 'ok' rows; a NULL in an
-- output column of another row is an envelope marker, not a third truth value. Never wrap
-- the fragments in COALESCE(..., FALSE) or otherwise choose a meaning for unknown inputs.
-- This evaluates the supplied model; it does not show that the model is a complete, legal,
-- or safe statement of a real-world rule.
--
-- Input columns (stable ID: name -> column):
-- v_84898dbe4c1b: A -> "in_A"
-- v_1fb5957ec72e: B -> "in_B"
-- Output columns:
-- f_de30f61d6db8: implication -> "out_implication"
-- f_1f67a936bd18: as_or -> "out_as_or"
-- Assumptions (scope K; exported separately and never folded into an output):
-- none; every complete input row is in scope.
-- ----------------------------------------------------------------------------------------------
-- 1. Expression fragments (parenthesised; non-NULL boolean inputs required)
-- ----------------------------------------------------------------------------------------------
--
-- [out_implication] output implication (stable ID f_de30f61d6db8)
-- implication := A -> B
-- PRECONDITION: "in_A", "in_B" are non-NULL booleans.
-- ((NOT "in_A") OR "in_B")
--
-- [out_as_or] output as_or (stable ID f_1f67a936bd18)
-- as_or := NOT A OR B
-- PRECONDITION: "in_A", "in_B" are non-NULL booleans.
-- ((NOT "in_A") OR "in_B")
--
-- ----------------------------------------------------------------------------------------------
-- 2. Guard template (read-only SELECT over an inputs list)
-- ----------------------------------------------------------------------------------------------
-- Replace the example rows between the markers with your own rows, or replace the inputs
-- CTE with a SELECT that provides the boolean columns listed in the header. status is
-- 'invalid_input' when any input is NULL, 'out_of_scope' when the assumptions are false, and
-- 'ok' otherwise; output columns are filled only for 'ok' rows (NULL elsewhere is an
-- envelope marker). Assumption columns are shown for rows with complete inputs.
WITH inputs ("row_id", "in_A", "in_B") AS (
-- tangibool:example-rows:begin
VALUES
(1, FALSE, FALSE),
(2, FALSE, TRUE),
(3, TRUE, FALSE),
(4, TRUE, TRUE),
(5, NULL::boolean, FALSE)
-- tangibool:example-rows:end
),
checked AS (
SELECT
inputs.*,
CASE
WHEN "in_A" IS NULL OR "in_B" IS NULL THEN 'invalid_input'
ELSE 'ok'
END AS "status"
FROM inputs
)
SELECT
"row_id",
"in_A",
"in_B",
"status",
CASE WHEN "status" = 'ok' THEN ((NOT "in_A") OR "in_B") END AS "out_implication",
CASE WHEN "status" = 'ok' THEN ((NOT "in_A") OR "in_B") END AS "out_as_or"
FROM checked
ORDER BY "row_id";
SMT-LIB
; Tangibool SMT-LIB export (tangibool.smtlib/1) ; Profile: SMT-LIB 2.6 Boolean subset (QF_UF): Bool constants, define-fun, let, push/pop, assert, check-sat, echo. ; Tested with Z3 5.1.0 (z3-solver 5.2.0, WebAssembly build). ; Model: not stated ; Source revision: reference ; Interpretation status: not stated ; Exporter: 1.0.0 (engine 1.0.0; semantics tangibool-semantics/1) ; Logic digest: d84ae6dfb625ed5edc014d42cf82776b8e4ffdef8bd21b74c6441d2704f99992 ; Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78 ; Mode: decision ; ; Reading results: each query block states what sat and unsat mean for its question. A sat ; answer comes with a witness or counterexample (ask for it with (get-model) after sat only); ; unsat settles that scoped query and is solver-checked, not an independently checked proof ; certificate; unknown settles nothing. Blocks are scoped with push/pop, so assertions never ; accumulate across questions. This checks the supplied model, not whether it is a complete, ; legal, or safe statement of a real-world rule. ; ; Symbols (stable ID: name -> symbol): ; v_84898dbe4c1b: A -> in_A ; v_1fb5957ec72e: B -> in_B ; f_de30f61d6db8: implication -> out_implication ; f_1f67a936bd18: as_or -> out_as_or (set-option :produce-models true) (set-info :smt-lib-version 2.6) (set-logic QF_UF) ; ---- Inputs (declared order) (declare-const in_A Bool) ; A (declare-const in_B Bool) ; B ; ---- Outputs (dependencies first) ; output implication (stable ID f_de30f61d6db8): A -> B (define-fun out_implication () Bool (=> in_A in_B)) ; output as_or (stable ID f_1f67a936bd18): NOT A OR B (define-fun out_as_or () Bool (or (not in_A) in_B)) ; ---- Assumptions (the scope K; never folded into an output) ; K: conjunction of the enabled, confirmed assumptions (true when there are none) (define-fun K () Bool true) ; ---- Definitions only: nothing is asserted and no check is run. ; Add (assert ...) and (check-sat) inside (push 1) ... (pop 1) to ask a question.
LaTeX
implication = A \rightarrow B
as_or = \overline{A} + BThe common slip
Reading A -> B as “A and B” or as “B whenever A, and not otherwise”. The table has three true rows, not one: with A = FALSE the implication is true whether B is true or false. The only row that falsifies it is A = TRUE, B = FALSE, which is also the tautology check's counterexample.
Questions
Why is A -> B true when A is false?
Because an implication only promises something about the case where A holds. If A never happens, the promise is never tested and so never broken. Rows 0 and 1 of the table record this: NOT A OR B is true there because NOT A is. The vacuous-truth pitfall page discusses the consequences for testing.
Related
Operators and gates
- NOT gate (inverter): truth table, symbol and the NOT operator
- OR gate: truth table for 2, 3 and 4 inputs, inclusive or explained
- Implication (→, if–then): truth table and why a false antecedent makes it true
Tools
- Truth table generator: any Boolean expression, up to 12 variables, with intermediate columns
- Boolean algebra calculator and simplifier with steps (every law named and verified)
- Karnaugh map solver: 2 to 4 variables, groups, SOP and POS, don’t-cares from assumptions
See also
- Implication (→, if–then): truth table and why a false antecedent makes it true
- Implication elimination: A → B = NOT A OR B, and the →E rule
- Contrapositive: A → B is equivalent to NOT B → NOT A
- The converse slip: A → B is not B → A
- Vacuous truth: a property that holds because nothing satisfies the assumptions
- Necessary vs sufficient: “only if” is not “if”
Worked examples
Generated from the Tangibool engine, semantics tangibool-semantics/1, engine 1.0.0, build . Every table, map, simplification and verdict on these pages was computed by the Tangibool engine when the site was built, and every claim was re-checked by the same exact checker the app uses. Limits are stated where they apply. © 2026 Foreranger LTD.