  Adaptive machines have the potential to assist or interfere with human
behavior in a range of contexts, from cognitive decision-making to physical
device assistance. Therefore it is critical to understand how machine learning
algorithms can influence human actions, particularly in situations where
machine goals are misaligned with those of people. Since humans continually
adapt to their environment using a combination of explicit and implicit
strategies, when the environment contains an adaptive machine, the human and
machine play a game. Game theory is an established framework for modeling
interactions between two or more decision-makers that has been applied
extensively in economic markets and machine algorithms. However, existing
approaches make assumptions about, rather than empirically test, how adaptation
by individual humans is affected by interaction with an adaptive machine. Here
we tested learning algorithms for machines playing general-sum games with human
subjects. Our algorithms enable the machine to select the outcome of the
co-adaptive interaction from a constellation of game-theoretic equilibria in
action and policy spaces. Importantly, the machine learning algorithms work
directly from observations of human actions without solving an inverse problem
to estimate the human's utility function as in prior work. Surprisingly, one
algorithm can steer the human-machine interaction to the machine's optimum,
effectively controlling the human's actions even while the human responds
optimally to their perceived cost landscape. Our results show that game theory
can be used to predict and design outcomes of co-adaptive interactions between
intelligent humans and machines.
