A Bayesian improvement of the proportionality principle

The principle of proportionality is seen as the highest peak of structural, logical thinking that enables balancing between constitutional principles and their interferences. So far, Alexy's weight formula has been the most advanced approach in structured balancing of proportionality stricto sensu, while this paper shows it as still too subjective. Despite judicial tests—or different, manifestly inappropriate reasonableness tests—proportionality stricto sensu hides some form of the jumping-to-conclusions bias, because the inference is made through a subjective lens. The paper presents structured legal balancing (and hence reasoning) based on Bayes’ theorem. This paper, based on the connection between probability and hypothetical legal norms, transfers the four-part structure of proportionality into the relevant four-part structure of the Bayes tree. This transfer shows the first as flawed due to its failing to consider means that can have the same effects as the considered ones. Only by inclusion of the latter can the total probability of a considered case be established. Numerical proportionality à la Bayes can be further improved through the use of public-value weights that are obtained from surveys or online communities of people giving weights in the real time (collective wisdom). The Bayesian approach can also be used in adjudication as a control in assessing judicial standards in use.