@mimi

heh, glad i took off on a slow news day.

have a great evening!

news today.
I tried to find some news on my own that are worth reading and found none.
Good NIght.

@WoodsDweller

heh, i hope that the mccains endorse biden long and loud. it will be good for centrists to see who their allies really are.

https://www.alternet.org/2019/04/conservative-mccain-family-to-back-joe-...

The family of late Sen. John McCain is expected to support former Vice President Joe Biden ... in a crowded Democratic field, and — if he wins the Democratic nomination — in a race against President Donald Trump.

I thought that tape of Biden boasting about working with Jesse Helms was gold, but this is classic.

Get ready to pop your ears, this campaign is going down fast.

@snoopydawg

i reckon this is going to make the u.s. domestic oil and gas industry very happy. frack happy to be precise.

on the other hand, the rest of his constituency is going to be pretty well convinced that his legendary status as a dealmaker exists only in his head.

MoA

If gas prices keep rising Trump will risk being reelected. Gas prices are already rising fastly because of his actions. It's gone up $.70 here in two weeks and people in California are close to paying $5 gallon. If he goes to war with Iran it will crash the global economy because Iran is threatening to close the Hormuz strait. Same thing if he goes to war with Venezuela. The sanctions on them are affecting prices too.

Farmers in Nebraska have lost millions from his tariffs and because their farms are flooded. The number of farmers who are declaring bankruptcy is almost at an all time high. Anyone hearing much about the floods? I'm not. I have to search for articles that cover it. The media doesn't seem to be able to walk and chew gum at the same time. It's Trump, Mueller and Russia Gate.

@UntimelyRippd

you certainly sound reasonable. one thing that i noticed about your use of language though is that moral concerns ("deserve" and "earned" in context) are mixed in together with concerns about accuracy of data and prediction. if i am reading your examples correctly, the moral concerns trump the concerns for the accuracy of the science.

i guess like leftish people i have a moral concern about any innocent person being imprisoned and i have a low bar for how people get to deserve healthcare. all humans deserve it.

Over the past couple of decades several people have written books or published studies on fundamental cognitive differences between folks who tend rightwards versus leftwards in their political orientation. Here is a difference I noticed quite a while ago:
Rightish/libertarianish types get angry if anyone gets something they "don't deserve" or "haven't earned".
Leftish types get angry if anyone doesn't get something they "deserve" or "have earned". (These are, of course, generalizations. Also, there is a tendency on both sides to weight the judgement scale in favor of people (whether individuals, or classes) for whom they have particular sympathy, and against those for whom they have particular antipathy.)
Thus, for example, right-wingers would rather 100 people go without healthcare they need, than allow 1 person to (free-of-charge) self-indulgently go to the doctor when it isn't necessary. Conversely (number-wise) they'd rather imprison 1 innocent person, than let 10 guilty criminals go free. In both cases, these attitudes are often colored by an overall sense of moral superiority: The so-called "innocent" are probably guilty of something, anyway; it's childish and venal to seek medical care for small discomforts -- you should suffer in silence.
In the world of machine learning and artificial intelligence -- those "algorithms" we're always hearing about, though it's an abuse of the technical term -- this is a well-understood parameter of systems whose purpose is to classify events or images or applicants or patients or suspects: Sensitivity versus Specificity. If I'm engineering a classifier to diagnose your lump as benign or malignant based on microscope images of your biopsy, I need to decide where to bias the system with respect to "False Negatives" (malignancies that are classified as benign) and "False Positives" (benign lumps that are classified as malignant). A sensitive classifier is one that has a very low False Negative rate. A specific classifier is one that has a very low False Positive rate. Typically, these two characteristics are in conflict -- as I "relax" the criteria for calling a sample "Malignant", I'm probably going to increase the rate of benign tumors that get "roped in" with the malignancies. Similarly, if "tighten" the criteria for calling a Malignancy, fewer benign tumors will be mis-classified, but at the risk of overlooking malignancies.
In the real world, with real data, it is almost impossible to design/develop a classifier that will have perfect sensitivity and perfect specificity, even if I have a perfect "model" for making the prediction, because what I almost never have is perfect data. The instruments that take the data are imperfect, and the people who record the data are imperfect. Thus, we're always left making a decision about where the tradeoff lies -- about the relative risks and costs of a False Negative versus the relative risks and costs of a False Positive.
In the ongoing quest for wisdom, it behooves the pilgrim to ask certain questions, when considering any policy stance: Where do I draw the line(s), and why? What are the real costs, weighted by my value system, of False Negatives? Of False Positives? Am I being honest in my answers? Am I being reasonable?