On-line decision problems – in which a decision is made based
on a sequence of past events without knowledge of the future –
have been extensively studied in theoretical computer science.
A famous example is the Prediction from Expert Advice problem,
in which an agent has to make a decision informed by the
predictions of a set of experts. An optimal solution to this problem
is the Multiplicative Weights Update Method (MWUM).
In this paper, we investigate how humans behave in a Prediction
from Expert Advice task. We compare MWUM and several
other algorithms proposed in the computer science literature
against human behavior. We find that MWUM provides
the best fit to people’s choices.