Measuring logic complexity can guide pattern discovery in empirical systems

We explore a definition of complexity based on logic functions, which are widely used as compact descriptions of rules in diverse fields of contemporary science. Detailed numerical analysis shows that (i) logic complexity is effective in discriminating between classes of functions commonly employed in modelling contexts; (ii) it extends the notion of canalisation, used in the study of genetic regulation, to a more general and detailed measure; (iii) it is tightly linked to the resilience of a function's output to noise affecting its inputs. We demonstrate its utility by measuring it in empirical data on gene regulation, digital circuitry, and propositional calculus. Logic complexity is exceptionally low in these systems. The asymmetry between "on" and "off" states in the data correlates with the complexity in a non-null way; a model of random Boolean networks clarifies this trend and indicates a common hierarchical architecture in the three systems.

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