tr352

The EU needs China. No green deal without Chinese batteries, solar cells and rare earth metals.

And then then the different kinds of the latter: angelic (locally or globally) and demonic.

So does the 74181 ALU.

In early 1995 I ran Windows 95 release candidate 1. I got it on CD-R from a friend. I had to spend my savings on extra RAM in order to get it running. For a brief period of time I was the coolest kid on the block.

Many of the later MSX models had a disk drive built in and came with MSX DOS. I was running MSX DOS on a Philips VG8235. I remember playing around with CP/M software such as Turbo Pascal and Wordstar.

> For example, in Bayesian logic: x AND y is xy, x OR y is x + y - xy

No it isn't. P(x AND y) = P(x)P(y) only in the special case where x and y are independent. Unlike fuzzy logic, probabilistic logic is not truth functional.

In addition to the other replies here, one fundamental difference between probabilistic and fuzzy logic is that fuzzy logic is truth functional and probabilistic logic isn't. Truth functional means, for instance, that if we know the (numerical) truth values of the propositions A and of B then we also know the (numerical) truth values of the propositions (A and B), (A or B), and so on. In probabilistic logic this does not hold. That is, P(A and B) is not fully determined by P(A) and P(B). If A and B are independent, we have P(A and B) = P(A)P(B), but in general we only know that P(A)+P(B)-1 <= P(A and B) <= min(P(A),P(B)). I also believe there's no generally accepted notion of conditioning in fuzzy logic, whereas conditioning is crucial in any probabilistic approach, see e.g. Bayes' theorem.

> P.S.: I only have 1 spot left in 2021 for personalized evidence-based recommendations.

Yeah right :)

Can you given an example of a bias that is correct?

This study was discussed in the article.

ThemePark Corona edition.

Ranks represent subjective degrees of belief but they do not, like subjective probabilities, nicely map to relative frequencies. So compared to probabilities, ranks are indeed limited in this regard. For instance, you can learn probabilities from data but there's no obvious way to learn ranks from data.

But for subjective belief they're still useful. Consider the problem of diagnosing a system with components that fail in rare cases. However we have no idea about failure probabilities. We can then use ranks. A diagnosis for some observed behaviour would then be the least surprising (i.e., lowest ranked) failure state that explains the observed behaviour. This is also the reason for least-surprising-first execution: the most important prediction or hypothesis is the most likely one and thus the least surprising one. There are some concrete examples in the paper which demonstrate this.

I am currently thinking about combining probabilities with ranks so that you can reason about both kinds of uncertainty in the same model. This could be implemented using a programming language that supports both ranked choice statements and probabilistic choice statements.

Author of this work here (and a bit surprised to find this on hacker news).

I don't disagree with your point about probability. However, there are various alternatives to probability which can be more appropriate for certain applications. For instance, with degrees of surprise (or ranks as they are called) you do not need to know exact probabilities, although the price you pay is that the scale is more coarse grained. If you reason about events that are either very probable or very improbable, then degrees of surprise are easier to work with than probabilities.

There's also a computational benefit. Execution of a ranked program is done much like a non-deterministic program where choice points execute "least surprising first". Compare that with the sampling techniques necessary in probabilistic programming that do not even provide exact results.