I'll try to state the problem as succinctly as I can and afterwards I'll give some motivation for it.

Given a measurable space $(S, \Sigma)$, the set  $P(S)$ of probability measures on $S$ also is a measurable space with $\sigma$-algebra generated by the sets
$$
\beta (<q,Q) \doteq \{ \mu\in P(S) : \mu (Q)<q\}
$$
with $Q\in\Sigma $ and $q $ a rational.

A _Markov kernel_ is a measurable map 
$\tau: S\to P(S)$, and a _nondeterministic kernel_ is, in turn,  a  function $T: S\to \mathrm{Pow}(P(S))$
such that for each measurable set of measures $ \xi\subseteq P(S)$, the set $\{s : T_a(s) \cap \xi \neq \emptyset\} $ is in $\Sigma$. I'm mainly interested in the case where all the sets $T (s)$ are countable.

It can be shown that if the sets 
$T(s)$ are __finite__ then they are of the form
$$
\{ \tau_n(s): n \in\mathbb{N}\},
$$
where each $\tau_n$ is a Markov kernel. 
Note that, although we have a sequence of kernels, for each  $s$ we might nevertheless have a finite set of _measures._  This follows from Kuratowski & Ryll-Nardzewski [selection theorem](http://www.encyclopediaofmath.org/index.php/Selection_theorems#Comments).

For the sake of simplicity, assume that for all $s$ we have $|T(s)|=2$.

>__Question.__ Can one do with just two Markov kernels? Or finitely many?

That is, given a nondeterministic kernel  $T$ with $|T(s)|=2$ for all $s$, are there finitely Markov kernels  $\tau_n$ ($1\leq n \leq N $) such that
$$
T(s)=\{ \tau_n(s): 1\leq n \leq N \}?
$$

---
**Background.** In Computer Science, *Markov decision processes* have been used to model situations where a user interacts with a system having probabilistic behaviour. The  *labelled* Markov DPs  have a measurable space $(S, \Sigma)$ as its set of states and for each $a $ in a set $L $ of labels, a _transition probability_, i.e., a function $\tau_a: S\times\Sigma\to [0,1] $ such that $\tau_a(s,\cdot): \Sigma\to [0,1] $ is  a probability measure on $S$ for each $s $ and $\tau_a(\cdot,Q): S\to [0,1] $ is a measurable map for each  $Q $. This is exactly the same as a Markov kernel.

There is nondeterministic version of labelled Markov processs (i.e., a probabilistic generalization of Kripke frames or [labeled transition systems](https://en.wikipedia.org/wiki/Transition_system#Formal_definition)) for which the main ingredient is an indexed family $T_a$ ($a\in L$) of nondeterministic kernels. Associated to these processes we have a _modal logic_ that allows to express properties of the form _“there is a transition with label $a$ such that the probability to reach a state in which the property $\phi$ holds is less than $q$”._ This is written as $\langle a\rangle_q \phi$, and a state satisfies this formula if belongs to the set
$$
\{s : T_a(s) \cap \beta (<q,[\!| \phi |\!])\neq \emptyset\},
$$
where $[\!| \phi |\!]$ is the set of states that satisfy $\phi$.

When one wants  to express more complex properties, such that $\langle b\rangle_p\langle a\rangle_q \phi$, one needs to require the measurability condition on kernels discussed above.