Argument from Consequences (Scheme)

Also Known As: Pragmatic Argument Consequentialist Reasoning

Discourse Mechanics ID: argument_from_consequences

Definition

An argumentation scheme that evaluates a claim, policy, or action based on its consequences. In its legitimate form (pragmatic reasoning), it assesses whether the outcomes of adopting a position are desirable or undesirable. It becomes fallacious when used to argue that a factual claim is true or false based on whether its consequences are pleasant or unpleasant.

Examples

Legitimate: We should invest in renewable energy because the consequences include reduced pollution and energy independence. Fallacious: Climate change cannot be real because the consequences would be too terrible.

Legitimate: We should require calorie counts on restaurant menus because evidence shows it helps consumers make healthier choices and reduces obesity rates. Fallacious: The study showing that sugar causes addiction must be wrong, because if it were true, the entire beverage industry would need to be restructured.

Legitimate: We should implement two-factor authentication across all company accounts because the consequence of not doing so is significantly higher vulnerability to data breaches. Fallacious: We cannot accept that our product has a design flaw, because acknowledging it would expose the company to lawsuits.

Formal Logic Pattern

FOL Pattern

The First-Order Logic formula representing this reasoning pattern's logical structure.

FOL (First-Order Logic) uses quantifiers (∀ = for all, ∃ = there exists), connectives (∧ = and, ∨ = or, ⇒ = implies, ¬ = not), and predicates to capture the essential form of a reasoning pattern. For example, the Ad Hominem fallacy: Person(x) ∧ HasFlaw(x) ⇒ Invalid(Claim(x)). These patterns allow automated verification of logical validity.

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Action(A) ∧ Causes(A,C) ∧ Good(C) ⇒ ShouldDo(A)

Formal Verification: Not formally decidable

Verification Steps

Verification Steps

Binary yes/no questions that an AI must answer to detect a reasoning pattern in a text.

Each of the 452 aspects has verification steps — simple yes/no questions designed to systematically detect whether a pattern appears in a text. For ad hominem: "Does the argument attack a person rather than their claim?" For false dichotomy: "Are only two options presented when more exist?" This ensures consistent, reproducible analysis.

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Binary (yes/no) questions an LLM must answer to identify this aspect:

1

Is a policy, action, or belief being evaluated?
Type: binary
2

Is the evaluation based on the predicted consequences of accepting or rejecting it?
Type: binary
3

Are the predicted consequences empirically supported rather than merely asserted?
Type: binary
4

Is the argument about practical desirability rather than truth or falsity?
Type: binary

Description

Why It Works

Consequences are practically relevant to decision-making. The scheme works well for evaluating policies but is misapplied when consequences are used to determine factual truth.