Approximations of Algorithmic and Structural Complexity Validate Cognitive-behavioural Experimental Results

Understand

Being able to objectively characterise the intrinsic complexity of behavioural patterns resulting from human or animal decisions is fundamental for deconvolving cognition and designing autonomous artificial intelligence systems.

• Yet complexity is difficult in practice, particularly when strings are short.
• By numerically approximating algorithmic (Kolmogorov) complexity (K), we establish an objective tool to characterise behavioural complexity.
• Next, we approximate structural (Bennett's Logical Depth) complexity (LD) to assess the amount of computation required for generating a behavioural string.