We consider settings where owners of electric vehicles (EVs) participate
in a market mechanism to charge their vehicles. Existing
work on such mechanisms has typically assumed that participants
are fully rational and can report their preferences accurately to the
mechanism or to a software agent participating on their behalf.
However, this may not be reasonable in settings with non-expert
human end-users. To explore this, we compare a fully expressive
interface that covers the entire space of preferences to two restricted
interfaces that reduce the space of possible options. To enable this
analysis, we develop a novel game that replicates key features of
an abstract EV charging scenario. In two extensive evaluations
with over 300 users, we show that restricting the users’ preferences
significantly reduces the time they spend deliberating. More surprisingly,
it also leads to an increase in their utility compared to
the fully expressive interface (up to 70%). Finally, we find that a
reinforcement learning agent displays similar performance trends,
enabling a novel methodology for evaluating market interfaces.