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/

Zendy Plus

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

Zendy Tools

Zendy Open

When we test a theory using data, it is common to focus on correctness: dothe predictions of the theory match what we see in the data? But we also careabout completeness: how much of the predictable variation in the data iscaptured by the theory? This question is difficult to answer, because ingeneral we do not know how much "predictable variation" there is in theproblem. In this paper, we consider approaches motivated by machine learningalgorithms as a means of constructing a benchmark for the best attainable levelof prediction.  We illustrate our methods on the task of predicting human-generated randomsequences. Relative to an atheoretical machine learning algorithm benchmark, wefind that existing behavioral models explain roughly 15 percent of thepredictable variation in this problem. This fraction is robust across severalvariations on the problem. We also consider a version of this approach foranalyzing field data from domains in which human perception and generation ofrandomness has been used as a conceptual framework; these include sequentialdecision-making and repeated zero-sum games. In these domains, our frameworkfor testing the completeness of theories provides a way of assessing theireffectiveness over different contexts; we find that despite some differences,the existing theories are fairly stable across our field domains in theirperformance relative to the benchmark. Overall, our results indicate that (i)there is a significant amount of structure in this problem that existing modelshave yet to capture and (ii) there are rich domains in which machine learningmay provide a viable approach to testing completeness.

The Theory is Predictive, but is it Complete? An Application to Human Perception of Randomness