Simplicity Bias and Algorithmic Probability in the Random Logistic Map

Simplicity bias, where simple patterns have exponentially higher probability than complex patterns, is observed in the digitized trajectories of the random logistic map for specific parameter regimes. This bias persists even with the introduction of small measurement noise, but diminishes as noise levels increase. The study also reveals insights into noise-induced chaos in the logistic map and the counterintuitive implications of algorithmic probability-based induction.