  Decision making is a fundamental capability of living organisms, and has
recently been gaining increasing importance in many engineering applications.
Here, we consider a simple decision-making principle to identify an optimal
choice in multi-armed bandit (MAB) problems, which is fundamental in the
context of reinforcement learning. We demonstrate that the identification
mechanism of the method is well described by using a competitive ecosystem
model, i.e., the competitive Lotka--Volterra (LV) model. Based on the
"winner-take-all" mechanism in the competitive LV model, we demonstrate that
non-best choices are eliminated and only the best choice survives; the failure
of the non-best choices exponentially decreases while repeating the choice
trials. Furthermore, we apply a mean-field approximation to the proposed
decision-making method and show that the method has an excellent scalability of
$O(\log N)$ with respect to the number of choices $N$. These results allow for
a new perspective on optimal search capabilities in competitive systems.
