and methods based on *Semantics of Minimal Models* [@bid86; @gel88; @mcc80; @min82; @van91]. Neither of the previous work considered minimization of the risk that beliefs sanctioned by the proposed semantics are false in the real world. A false belief may suggest wrong decisions and lead to harmful actions. As reasoning errors caused by incompleteness of information are inevitable, minimization of the number and likelihood of false beliefs becomes practically important a goal.

The following sections introduce the *semantics of typical models* and show that it minimizes the expected number of erroneous beliefs over all ways of reasoning with incomplete information.

Evidence
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At any moment the world is in exactly one of its possible states, so in exactly one of possible worlds represented by the corresponding model of $S$. Let $p(w)$ denote the probability that at a randomly chosen moment the world is in a state belonging to a possible world $w \in W$ represented by a model $\mu$ of $S$. Then to every $\mu \in MOD(S)$ representing the corresponding $w \in W$ one may assign a probability $p(\mu) = p(w)$ such that $\sum_{\mu \in MOD(S)} p(\mu) = \sum_{w \in W} p(w) = 1$. So the probability $p(F)$ that a 