I have values between 0 and 1, like [0.9, 0.8,...], indicating that a value closer to 1 is more probable than one close to 0, but without all the values adding up to 1. I guess I can't call it probability, but what else can I call those values?
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You mean, an event with "0.9" doesn't actually have a 9 in 10 chance for success, but it's more probable than the one with "0.8"?
I guess you could call it "Bias". This is what's used to indicate tendency towards given option/outcome/point while not stating the value precisely, so the event with bias 0.9 is more biased towards success than the one with bias of 0.8.
You may also talk about unnormalized probability, where the values do add up to a certain constant but not to 1 and proportionally correspond to relative probabilities - you'd need to normalize them by dividing each by their sum. But if no such easy transformation is possible, "bias" is the safe choice.
I'm risking retribution by not answering your actual question, but if SF in his answer has deduced what you're actually getting at, I suggest that you shy away from using a scale from 0 to 1 to show 'pseudo-probabilities' - it's just going to produce confusion by adapting an agreed convention in a non-standard way. Probabilities involve ratio data not merely ordinal data. ( http://www.usablestats.com/lessons/noir )
Why not use instead a Likert-like scale ( http://en.wikipedia.org/wiki/Likert_scale ):
A = certain to happen B = very probably going to happen ...
Z = certain not to happen (use as many graduations as you think sensible).
This will almost certainly be subjective (ie not totally accurate) but is only a reformulation of your own idea, without the confusion of terminology. Yes, someone will ask what a 'C' classification say actually means, but that's fair enough and much fairer than leaving some people believing that a pseudo-probability of 0.8 means a probability of 0.8 :-/