English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A general framework is introduced to estimate how much external informationhas been infused into a search algorithm, the so-called active information.This is rephrased as a test of fine-tuning, where tuning corresponds to theamount of pre-specified knowledge that the algorithm makes use of in order toreach a certain target. A function $f$ quantifies specificity for each possibleoutcome $x$ of a search, so that the target of the algorithm is a set of highlyspecified states, whereas fine-tuning occurs if it is much more likely for thealgorithm to reach the target than by chance. The distribution of a randomoutcome $X$ of the algorithm involves a parameter $\theta$ that quantifies howmuch background information that has been infused. A simple choice of thisparameter is to use $\theta f$ in order to exponentially tilt the distributionof the outcome of the search algorithm under the null distribution of notuning, so that an exponential family of distributions is obtained. Suchalgorithms are obtained by iterating a Metropolis-Hastings type of Markovchain, and this makes it possible to compute the their active information underequilibrium and non-equilibrium of the Markov chain, with or without stoppingwhen the targeted set of fine-tuned states has been reached. Other choices oftuning parameters $\theta$ are discussed as well. Nonparametric and parametricestimators of active information and tests of fine-tuning are developed whenrepeated and independent outcomes of the algorithm are available. The theory isillustrated with examples from cosmology, student learning, reinforcementlearning, a Moran type model of population genetics, and evolutionaryprogramming.

Assessing, testing and estimating the amount of fine-tuning by means of active information