  To better understand the process by which humans make navigation decisions
when tasked with multiple stopovers, we analyze motion data captured from
shoppers in a grocery store. We discover several trends in the data that are
consistent with a noisy decision making process for the order of item
retrieval, and decompose a shopping trip into a sequence of discrete choices
about the next item to retrieve. Our analysis reveals that the likelihood of
inverting any two items in the order is monotonically bound to the entropy of
the pair-wise ordering task. Based on this analysis, we propose a noisy
distance estimation model for predicting the order of item retrieval given a
shopping list. We show that our model theoretically reproduces the entropy law
seen in the data with high accuracy, and in practice matches the trends in the
data when used to simulate the same shopping lists. Our approach has direct
applications to improving simulations of human navigation in retail and other
settings.
