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Predicting algorithmic complexity for individuals

Sara Todorovikj
Chemnitz University of Technology ~ Predictive Analytics
Mr. Daniel Brand
Chemnitz University of Technology ~ Predictive Analytics
Marco Ragni
TU Chemnitz ~ Behavioral and Social Sciences

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.



Algorithmic thinking
predictive modeling
problem solving
cognitive processes

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Cite this as:

Todorovikj, S., Brand, D., & Ragni, M. (2022, July). Predicting algorithmic complexity for individuals. Paper presented at Virtual MathPsych/ICCM 2022. Via