> so there's an implied total complexity as well as an "generating" one. Dessalles's algorithmic simplicity theory of (cognitive) relevance is formulated in th

> so there's an implied total complexity as well as an "generating" one. Dessalles's algorithmic simplicity theory of (cognitive) relevance is formulated in these terms. >Situations are relevant to human beings when they appear simpler to describe than to generate The discrepancy between generation complexity >the complexity (minimal description) of all parameters that have to be set for the situation s to exist in the "world" i.e, the "pixels" and description complexity > the length of the shortest available description of s (that makes s unique) i.e. the mandelbrot formula is named Unexpectedness in this framework. https://telecom-paris.hal.science/hal-03814119/document https://telecom-paris.hal.science/hal-03814119/document https://simplicitytheory.telecom-paris.fr/ https://simplicitytheory.telecom-paris.fr/ Dessalles published a paper in 2022, Unexpectedness and Bayes’ Rule https://cifma.github.io/Papers-2021/CIFMA_2021_paper_13.pdf https://cifma.github.io/Papers-2021/CIFMA_2021_paper_13.pdf >A great number of methods and of accounts of rationality consider at their foundations some form of Bayesian inference. Yet, Bayes’ rule, because it relies upon probability theory, requires specific axioms to hold (e.g. a measurable space of events). This short document hypothesizes that Bayes’ rule can be seen as a specific instance of a more general inferential template, that can be expressed also in terms of algorithmic complexities, namely through the measure of unexpectedness proposed by Simplicity Theory. Maybe there is a way to plug this into the https://en.wikipedia.org/wiki/Buddhabrot https://en.wikipedia.org/wiki/Buddhabrot fractal someone mentioned above.