  An agent choosing between various actions tends to take the one with the
lowest cost. But this choice is arguably too rigid (not adaptive) to be useful
in complex situations, e.g., where exploration-exploitation trade-off is
relevant in creative task solving or when stated preferences differ from
revealed ones. Here we study an agent who is willing to sacrifice a fixed
amount of expected utility for adaptation. How can/ought our agent choose an
optimal (in a technical sense) mixed action? We explore consequences of making
this choice via entropy minimization, which is argued to be a specific example
of risk-aversion. This recovers the $\epsilon$-greedy probabilities known in
reinforcement learning. We show that the entropy minimization leads to
rudimentary forms of intelligent behavior: (i) the agent assigns a
non-negligible probability to costly events; but (ii) chooses with a sizable
probability the action related to less cost (lesser of two evils) when
confronted with two actions with comparable costs; (iii) the agent is subject
to effects similar to cognitive dissonance and frustration. Neither of these
features are shown by entropy maximization.
