This talk will concern recent progress on the statistical analysis of numerical algorithms with random initial data and its connections to decision-making times. With appropriate randomness, the fluctuations of the iteration count (halting time) of numerous numerical algorithms have been demonstrated to be universal, i.e., independent of the distribution on the initial data. Some rigorous results have followed using random matrix theory. Interestingly, the universality in the halting time seems to be related to both the time it takes to run a Google search and to the experimental work of Bakhtin and Correll on neural computation and human decision-making times.
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