Clarity Theory: A Quantification of Effectiveness

Transformation success scales with the performing system's state of clarity. Physical law constrains which transformations are possible; clarity determines which achieve their purpose within operational bounds.

This paper defines clarity, examines its role in transformation, and provides a framework for its quantification:

$$C = R \times U \times A \times Z$$

where relevance (R), understanding (U), access (A), and utilization (Z) are jointly necessary conditions. The multiplicative form captures logical dependency: deficiency in any component constrains the whole.

Prior work has addressed how information carries meaning (Bar-Hillel & Carnap, 1953; Floridi, 2004, 2005) and contributes to biological viability (Kolchinsky & Wolpert, 2018). Constructor theory has formalized transformation in terms of possible and impossible tasks (Deutsch & Marletto, 2015). Clarity Theory inherits the thermodynamic foundation of viability and extends its scope beyond biological persistence to purposeful transformation in any domain, using constructor theory vocabulary to model the relationship between information, entropy, and transformation success.

The framework is illustrated across five domains: computation, biology, cognition, physiology, and social dynamics. In each case, the framework predicts that success depends on all clarity components jointly, with deficits compounding rather than averaging.