Predicting algorithmic complexity for individuals

How difficult is it to simulate a an algorithm in one's mind and correctly deduce its outcome? In this paper, we present a predictive modeling task in the domain of algorithmic thinking in a railway environment. We present metrics, either based on algorithmic complexity (e.g. lines of code) or on the effect on cognitive resources an algorithm simulation can have (e.g. context switching). We implement the metrics within a benchmark and evaluate their predictive performance on an individual level, by assigning a complexity threshold to each individual. We compare these results to a standard statistical correlation analysis and suggest a different perspective for determining the predictive powers of a complexity metrics as models.