I have the same complaint about those slides that I had about another paper linked to. Slide number 6 says: "Realism = existence of counterfactual outcomes". That doesn't seem right to me. A realistic model could allow for a nondeterministic relationship between "hidden variables" and measurement outcomes. For example, you could have a model along the following lines:
- Each twin pair is associated with a hidden variable [itex]\lambda[/itex], randomly produced according to a probability distribution [itex]P(\lambda)[/itex]
- Alice will measure spin-up with probability [itex]P_A(\lambda, \alpha)[/itex] where [itex]\alpha[/itex] is the setting of her measuring device.
- Bob will measure spin-up with probability [itex]P_B(\lambda, \beta)[/itex] where [itex]\beta[/itex] is the setting of his measuring device.
- For fixed lambda, the probabilities are independent; the probability of both Alice and Bob getting spin-up is given by: [itex]P_{A\&B}(\lambda, \alpha, \beta) = P_A(\lambda, \alpha) \cdot P_B(\lambda, \beta)[/itex]
I would call such a model "realistic"; it's just not deterministic, and the values of the "hidden variables" are not directly measurable. But in such a model, there are no counterfactual outcomes (there is no definite answer to a question of the form "What measurement result would Alice have gotten if she had chosen setting [itex]\alpha'[/itex] instead of [itex]\alpha[/itex]). So I don't think it's correct to equate realism with counterfactual outcomes.
On the other hand, for the purposes of Bell's proof as applied to EPR, it doesn't actually matter, because the perfect correlations (or anti-correlations) between Alice's and Bob's results when their settings are equal implies that any local hidden-variables explanation must, in fact, be counterfactually definite. But the counterfactuality is a conclusion, rather than an assumption.