  Motivated by applications in cyber security, we develop a simple game model
for describing how a learning agent's private information influences an
observing agent's inference process. The model describes a situation in which
one of the agents (attacker) is deciding which of two targets to attack, one
with a known reward and another with uncertain reward. The attacker receives a
single private sample from the uncertain target's distribution and updates its
belief of the target quality. The other agent (defender) knows the true
rewards, but does not see the sample that the attacker has received. This leads
to agents possessing asymmetric information: the attacker is uncertain over the
parameter of the distribution, whereas the defender is uncertain about the
observed sample. After the attacker updates its belief, both the attacker and
the defender play a simultaneous move game based on their respective beliefs.
We offer a characterization of the pure strategy equilibria of the game and
explain how the players' decisions are influenced by their prior knowledge and
the payoffs/costs.
