A comparative quality ascribed to theories or propositions that measures their closeness to the truth when none are strictly true; verisimilitude assesses how well a theory captures true aspects of its target domain and excludes false ones, enabling comparative evaluation of approximate theories.

Principle

A theory's verisimilitude increases to the extent that it entails or preserves truths about the target domain and decreases to the extent that it entails falsehoods; comparative verisimilitude adjudicates between theories by trade‑offs among true and false consequences rather than by absolute truth.

Demonstration

Illustrative scenario → Situation: Two scientific models predict overlapping but distinct sets of phenomena; both are idealizations that are strictly false. → Recognition: Model A yields more true predictions about key phenomena but contains a false auxiliary assumption; Model B is simpler but omits several true regularities. → Action: Evaluate which model yields a better balance of true versus false consequences relative to the explanatory targets. → Consequence: One model is judged nearer the truth (higher verisimilitude) even though neither is strictly true.

Misapplication

Equating verisimilitude with mere predictive success or simplicity without accounting for the truth content of theoretical claims. The error is to conflate empirical fit on selected data sets with overall closeness to truth across the relevant domain.

Consequence

Verisimilitude provides a principled basis for theory choice and iterative improvement when strict truth is unattainable; it focuses attention on error distribution and content truth rather than solely on instrumental performance.

Reversal

When theories are incommensurable, address different targets, or when the target domain is underspecified, comparative verisimilitude may be ill‑defined or indeterminate; in such cases other criteria (empirical fit, simplicity, coherence) may guide choice.

Boundary

Clearly within: assessment of competing scientific theories understood as approximations to the same target domain. Boundary case: comparing models that idealize different scales or variables. Clearly outside: the binary evaluation of strictly true versus false analytic truths.

Semantic Tension

Tension between predictive/operational criteria (empirical adequacy, simplicity) and truth‑directed criteria (content truth, explanatory scope): verisimilitude mediates these by privileging content accuracy but may conflict with pragmatic evaluation metrics.

Synthesis

Verisimilitude reframes theory appraisal as a matter of comparative closeness to truth: it shifts emphasis from binary truth to the pattern of true and false consequences a theory generates, thereby guiding incremental theory choice and development.