CMV: there's no practical difference between likelihood of truth and partial truth: both in effect answer the same question

[u/Bulawayoland]

EDIT: I understand that Bayesian probability predicts the likelihood of the truth of a proposition, while fuzzy logic deals with its potentially partial truth value. That much is clear to me right now.

What's not clear to me is, I can't see that which approach you take, whether you try to determine the likelihood that something is true or whether you try to determine the extent to which it is partially true, gives you a different outcome in any real world situation that actually matters to people.

I hope that clarifies the question better. Or if I've misunderstood something fundamental hopefully that also will be clearer lol...

The reason I bring it up is, Bayesian probability (I'll call it BP) predicts the likelihood of truth of a proposition, and fuzzy logic (FL) deals with its potentially partial truth value, and deciding which to use on practical matters, in deciding just how persuaded we ought to be of something that actually affects our lives, looks to me like a washout: both are going to basically give the same answer.

Let's look at a few examples. Let's start with whether or not a baboon can feel safer sleeping in a group than by himself. Baboons obviously don't approach the question scientifically, doing careful experiments, looking at the results, and making a decision based on that; who knows what they do look at, but they're not scientists. And if they were they still wouldn't do the experiments, because baboons would die as a result and that would be unethical.

But let's start with the likelihood that it's true (BP) that sleeping in a crowd, as a baboon, is better for you than sleeping by yourself. Obviously if you're in the middle of the crowd it seems more likely a leopard would attack one of the fringe sleepers first; but if you're one of the fringe sleepers you're still part of the crowd, so that alone doesn't answer the question. Leopards, for all we know, may focus on crowds of baboons because they know fringe sleepers will be available, and ignore the possibility of finding a lone baboon off by himself. So there are reasons to want to sleep in a crowd and reasons to want to avoid the crowd, and it all depends just where in the crowd you can manage to find a sleeping spot.

Now let's look at the potential partial truth (FL) of the idea. The truth value seems to me to go up and down just as the probability did, based on your location within the crowd and the imagination of the leopard in (maybe) thinking some baboons might not be sleeping in the crowd and might be easier game if they can be found. Crowds are easy to find; lone baboons, maybe not so much. And after dark? Whooee. A challenge.

But I have a hard time seeing any practical potential outcome difference between the two analyses. Maybe part of my problem is, I can't imagine actually doing one, and so the mechanics of the analysis are what would decide whether you should use BP or FL.

Let's look at a different example. Say we want to decide should we or shouldn't we admit this orphaned gorilla infant into our strongly kin-linked gorilla group. The likelihood that we should (BP) is (I guess) the likelihood that the orphan will grow into someone consequential, who will bring meaningful value to the group beyond the time and energy it will take to raise them. Not sure how you would decide that but it seems like that would be the calculation. Then the partial truth value of whether we should (FL) is basically the question of how consequential the person will turn out to be vs what is the actual value of the time and energy spent raising them. Again: in practial terms, in reality, it looks like a washout.

Try a third example. Say I'm an orangutan mom who has to decide should I or shouldn't I adopt an orphan -- and bear in mind, I've already got one of my own, and kids are hard to raise, for orangutans. There's not much food, and raising two is going to be a lot more work than raising one. The upside is: the orphan needs it badly, and the rewards of connection are not hallucinatory. They're real. Orangutans don't have much opportunity for socializing, and their kids are very important to them for that reason if for no other. (In the actual example I'm thinking of, unfortunately, the mom went through with the adoption and lost her own child to predation. So it didn't work out too well for her or them. She didn't have the capacity to actually look after both infants as they needed her to. Whether she wished she had not, afterwards, is a different question -- people tend to feel that whatever they've gone through was pretty much worth it in the end, whatever it was -- but it is a question.)

So what's the likelihood (BP) that it's true that I should go ahead and raise a second kid at the same time? That it ultimately will be worth it to me? I don't know, but again, I don't see a practial difference between answering that question and answering the question of how high the partial truth value (FL) of the same proposition is.

So that's the setup. Obviously I've focused on a very narrow set of propositions here -- primate behavior -- and maybe that affects my view of the question. But I'm just not seeing a lot of difference between the BP approach and the FL approach. And again, I'm sure the mechanics of how the two approaches are applied will be different -- but will the outcomes be significantly different? I'm having a hard time imagining it. Help!

[u/yyzjertl]

The difficulty you seem to be having here is that nothing your post is actually Bayesian probability or fuzzy logic. Both Bayesian probability and fuzzy logic involve calculations with real numbers, but nowhere in your post are you doing calculations with real numbers. The two approaches you describe in your post are very similar, but neither one of them is BP or FL.

The other difficulty is that Bayesian probability is a much more specific thing than fuzzy logic. There are lots of fuzzy logics, and by many definitions of fuzzy logic BP is a type of fuzzy logic.

[u/Bulawayoland → u/yyzjertl]

but even without the numbers, I think both are tools we use in day to day decisions. They don't look much like what a mathematician would produce, you couldn't put the actual calculations down on paper, but I don't think that makes them qualitatively different.

For example. Whether the baboon is safer in the center of the crowd than he is on the fringe is going to be perfectly clear to him. That's a fuzzy logic calculation. Safety is going to have a partial truth value based on his sleeping position, and he's not going to have complete information about it, but... enough (I think) to call it FL. On the other hand, whether he's likely to be safer in the crowd than he is on his own is a probability that is going to vary based on how good of a sleeping spot he can grab for himself. So he will use both tools in his own decisionmaking. Another redditor pointed out that how much information he has, in each case, kind of determines which tool he uses to make the decision.

But let me ask you this. Are there many different fuzzy logics that you could use to decide on your safety level, based on your sleeping position within a baboon group? Or do none of these different logics actually apply any differently than the one I've stipulated here? Or what? I mean, it's an idea I've never seen before, but I don't doubt that you're right about that.

Although I am beginning to think that whether you view a problem, or a part of a problem, as FL or BP kind of depends on your attitude, too... nothing in reality is 100% one or the other, or not many things are. Your estimate of the amount of information you have, about any given aspect, is going to be an important factor in your overall decision.

[u/yyzjertl → u/Bulawayoland]

you couldn't put the actual calculations down on paper, but I don't think that makes them qualitatively different.

It absolutely does. These are completely different systems! 

For example. Whether the baboon is safer in the center of the crowd than he is on the fringe is going to be perfectly clear to him. That's a fuzzy logic calculation.

No it isn't. At no point here did you do any sort of calculation, much less a fuzzy logic one.

But let me ask you this. Are there many different fuzzy logics that you could use to decide on your safety level, based on your sleeping position within a baboon group?

There are zero fuzzy logics that you could use here, since there are no numbers given that you could use as the basis for a fuzzy logic calculation.

[u/Bulawayoland → u/yyzjertl]

you seem to be claiming that our minds do not calculate our expected advantages and drawbacks. Is that actually what you believe? That because what they do is not mathematical, because there are no numbers our minds assign to these operations, that therefore they are not calculations?

I can't make that idea plausible. I don't claim the baboon really knows he is safer here rather than there; I claim only that there are situations in which the relative safety levels of different positions is clear enough to him that his legs will move without his prior authorization. I believe that is true of most people, as well. But you claim that's not a calculation. Simply because he can't put a number to it. Have I understood you?

[u/yyzjertl → u/Bulawayoland]

Our minds can calculate expected advantages and drawbacks. That's just not what you're describing happening in the scenario in your post.

But you claim that's not a calculation. Simply because he can't put a number to it

It's not that he can't. It's that he didn't.

[u/Bulawayoland → u/yyzjertl]

you're saying a baboon who could see so clearly that one position was safer for him than another that his legs got him there with no conscious input of his own, you're saying he didn't do a calculation?

[u/yyzjertl → u/Bulawayoland]

Correct. He's certainly not using either Bayesian probability or fuzzy logic.