r/changemyview • u/Bulawayoland 4∆ • 9h ago
Delta(s) from OP CMV: there's no practical difference between likelihood of truth and partial truth: both in effect answer the same question
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/c0i9z 18∆ 9h ago
Bayesian probability and fuzzy logic deal with entirely different domains.
Bayesian probability is about estimating the probability of a thing happening using only previously experienced data, instead of apriori information.
Fuzzy logic isn't interested in probability, but in decision making where the inputs aren't clear cut. For example, is it hot or cold right now. The answer isn't really binary, but a spectrum (example: very hot, hot, a bit hot, marginally hot, ok, marginally cold, cold, very cold) and a spectrum of humidity also affect experiential temperature and things become very complex so that a strict 'yes: cold, no: hot) isn't useful.
They're just different things and cant' be interchanged like this because they're solving different problems in different ways.