  Recent work in the behavioural sciences has begun to overturn the long-held
belief that human decision making is irrational, suboptimal and subject to
biases. This turn to the rational suggests that human decision making may be a
better source of ideas for constraining how machine learning problems are
defined than would otherwise be the case. One promising idea concerns human
decision making that is dependent on apparently irrelevant aspects of the
choice context. Previous work has shown that by taking into account choice
context and making relational observations, people can maximize expected value.
Other work has shown that Partially observable Markov decision processes
(POMDPs) are a useful way to formulate human-like decision problems. Here, we
propose a novel POMDP model for contextual choice tasks and show that, despite
the apparent irrationalities, a reinforcement learner can take advantage of the
way that humans make decisions. We suggest that human irrationalities may offer
a productive source of inspiration for improving the design of AI architectures
and machine learning methods.
