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Again, if the simplest explanation is not more likely to be true than alternatives containing unnecessary complications, the what benefit is choosing the simpler one? What is the justification? These are the essential questions that keep getting dodged.
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The answer is this:
OR says nothing about which
equally reliable & explanatory theory is "more likely to be true".
You can read a lot about this here:
http://en.wikipedia.org/wiki/Occam's_Razor
We use the simplest of equally reliable & explanatory theories for various reasons: practicality, easier to learn, apply, remember, teach, etc. Once the more complicated model provides a bit more explanatory & reliable predictions, then simplicity takes a back seat when we need to be more accurate.
You can argue that, indeed, it is the case, that the simplest theory is "most likely to be true", and then you could create your own maxim for that. But, that's not Occam's Razor.
But, I think you'll be hard pressed to show that this is the case. It seems to me that more complicated models are usually more reliable & explanatory than simpler models. We still use the simpler models sometimes because they are easier and are 'good enough', but we know they are not "more likely to be true". There may be exceptions, but I think they are rare. Of course, at some point you are going to have to define & present a way to measure "simplest".
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Yeah, this is better than my post.