Huh, I've never even heard of parimutuel betting. I had to read the WIkipedia page on it. In that case, am not sure my suggestion of using the geometric distribution would work.

Also, yes, finding the gradient between the stated odds of a horse winning and the observed odds is a common method (to find irregularities in how odds are calculated). I had a colleague who, like me, has a Phd in math. Unlike me, he is (or was) athletic. He played tennis as often as he could. When sports betting became legalized, he discovered 1) you could bet on every point of a professional tennis match 2) there were many irregularities of how the probabilities were calculated.

1) The link "Swarm Intelligence" (which is on the link to "generated a correct prediction" in this original post) doesn't open. Googled "Swarm intelligence kentucky derby" to find an explanation of how this model predicted the winner and didn't find any matching results. So, does anyone have a link (that works)?

2) If I were trying to predict the winner of the Kentucky Derby using data, I would think that predicting the winner would be nearly impossible. However, it might be possible to get a lower bound of the mean with respect to the geometric distribution. That is, the geometric distribution (assuming the probability of a horse winning is reasonably correct) would give the number of trials that a model would need to correctly predict the winner. If it would then be possible, assuming a theoretical infinite number of trials, to develop a model that has a lower bound of the mean than the geometric distribution predicts, then power of this model isn't predicting the winner but coming up with a betting strategy that, over the long term of betting on horses, would guarantee a profit.

The problem with the analysis (according to the article I read -- didn't read the formal analysis) is that using income as a measure of class misses the high variance of goods in the US. For instance, the article mentions that the median price of a home is about $500k. This likely isn't enough to buy a studio apartment in lower Manhattan. So, a more accurate measure would be a dimensional analysis that uses income as a ratio with respect to what a median priced basket of goods (eg, a house, average grocery bill, etc) in various parts of the US.

In other words, it would give a normalized unit measurement of how much, say, earning $130k in Des Moines, Iowa differs from making the same amount living in Cambridge, MA. Then, "upper middle class" would, I assume, be a measure of a standard distribution on a normal distribution (eg, the top 1/3 of the distribution).

Since ITER began construction (many years ago), one significant change is that new types of superconducting magnets have been developed. So, one primary reason not to follow in the path of ITER, or invest in it, is that the technology is now somewhat outdated.

For instance, Commonwealth Fusion is using a new type of superconducting magnet that can generate approximately 20 Tesla (https://news.mit.edu/2024/tests-show-high-temperature-superconducting-magnets-fusion-ready-0304). In comparison, ITER is using a magnet that can generate about 13 Tesla. The difference, then, is a potentially longer confinement time of the plasma and with a much smaller and less expensive to manufacture reactor.

Have a casual interest in modeling plasma (it's a long story why). Because of this interest, have specific questions about the investment (that weren't answered in the article):

* Why (only) a tokamak design? There are two primary types of fusion reactors, magnetic and inertial confinement. There are also hybrid approaches, such as what Helion Energy is using, a magneto-inertial fusion. So, my question, why choose only this approach? If you're going to make a large investment, why not invest in the many types of approaches to make a fusion reactor?

* Is the investment going to include investing in private companies? And, if so, will that include investments in companies outside of the UK?

* The reason for the investment in a supercomputer is because of plasma tearing instability (I assume). That is, the plasma has to stay hot enough and for a long enough time to generate fusion. Both, generating the plasma and the process of fusion, is probabilistic. In other words, there are many instabilities that occur and that cause the plasma to cool (from the temperature needed for fusion). So, my question, what exactly would an investment in a supercomputer provide? Typically, investment in a supercomputer means that a problem can be linearly divided into smaller subproblems that are easier to compute. Would be interested to know what specific methods are going to be used to model the plasma.

* Finally -- and this isn't a question -- modeling plasma is complex. It's one of the most complex mathematical models that I've learned and attempted to make. If you're interested, this introductory video is excellent. Although it's from about 10 years ago (I think), the problems with understanding the physics of plasma in a fusion reactor are still relevant: https://www.youtube.com/watch?...

We may both be right. It could be both Gunnigam and Del Naja. Another quote from the article:

"On October 28, 2022, the day Duley and Del Naja entered Ukraine, a “David Jones” also crossed the border at the same location, according to a source familiar with immigration procedures. The source also told us the date of birth listed on Jones’ passport. It was the same as Robin Gunningham’s birthday."

If you could, quote the passage from the article where you found it. Also, because there are no page numbers, point out where in the article you found it approximately.

Here's why I think it's Del Naja. This is a quote from the article:

"Horenka resident Tetiana Reznychenko told us she made coffee for the two men who did the bathtub mural and saw the two painters without their masks. As we swiped through the lineup on a cellphone, Reznychenko shook her head no. Then, when shown one of the photos, her eyes widened, even as she denied having seen the man in the picture.

That man was Robert Del Naja."

The quote is taken from directly below the three people that the author(s) think could be Banksy.

First, I skimmed the long article. So, what I'm proposing may be in the article.

If you haven't read the article, it proposes that Del Naja is Banksy. If you don't know, Del Naja is a member of the band Massive Attack. If you also don't know, Massive Attack is a popular and successful rock band. This means that the band tours around the world. So, here's my proposed test:

1st, get all the dates that Banksy put up graffiti and where he did so. For instance, London, 3/11/24 -- I just made this date up. Next, have all touring dates of Massive Attack and that it's confirmed Del Naja was on stage. For instance, Massive Attack is going to be in Helsinki, Finland on May 27th and Del Naja is expected to be on stage. Then, these two list of dates must coincide and there must be no contradictions. If, say, on one date Banksy left a graffiti on a wall in New York City, then Massive Attack must either have been touring in or near New York City or they must have been not touring. But, if Massive Attack, say, was in Singapore and Del Naja was on stage, then this is a contradiction.

Finally, simply see how many contradictions to these dates there are. If there are zero, then it's highly likely that Del Naja is Banksy. If there are many contradictions, then it's highly likely that he's not.

You've misrepresented the quote. Here is the full quote, from the second paragraph of the article:

"Dozens of those developers who spoke to Ars in recent months say they’re wary of traveling to a country that has shown a callous disregard for—or outright hostility toward—the safety of international travelers. That’s especially true for developers from various minority groups, those with transgender identities, and those who feel they could be targeted for outspoken political beliefs."

The reason you've misrepresented the quote is because these "dozens" may represent many more who don't attend. This is the question I asked and hasn't been answered, with quantitative data. That is, how many won't attend (as compared with previous years)? Then, this is also how you misrepresented the quote, by assuming that this represents data of the conference when it's only the number of people who were interviewed for the article."

The article only quotes people who aren't going to attend. There was no quantitative data presented, either from previous years or, especially, from previous years when international travelers may have had fears or concerns about visiting the US.

To be clear, am not discounting the concerns. I do think they're legitimate. But I also think it's important to get quantitative data on how many do attend the conference, how many are international attendees, and how this compares to previous years. Why is this important? Because it's a quantitative measure of how Trump is destroying the economy. That is, this is a measure that can't be directly made and has to be implicitly, through comparison with other years. And it's one that can only be made through the experience of the US going through a president as disastrous as Trump.

The IEEPA were the 70% of the tariffs that were ruled illegal. From what I understand, all tariffs that Trump issued were challenged. The ones not issued using IEEPA were ruled legal.

Here's a link that explains that Trump's tariffs on steel and aluminum were ruled legal: https://www.nbcnews.com/politi...

Regarding the 12 month delay to rescind the tariffs, I read this on a legal blog. I just did a search to provide the link and couldn't find it. So, this either means that this is wrong or I can't find the link. In any case, I should note, I didn't read the ruling to get this information but was provided by someone who did.

1) 30% of Trump's tariffs were ruled legal.

2) The administration has been given one year before the tariffs have to be rescinded.

Couldn't find this with a Google search so am asking: what's the statistical definition of clinical addiction? The plaintiffs, I presume, have to statistically demonstrate that the products of Meta and YouTube are "defective" (am using quotes because am unsure if this is the exact word used in the lawsuit) because they induce behavior that's similar to addictive products, such as drugs and alcohol.

So, surprisingly, I couldn't find a website that simply explains the statistical definition of clinical addiction. Does anyone have a link to one?