I think there's an important wrinkle in the resolution criteria for this question: would we say that the reign of the last Roman emperor ended in 476, or in 1453?

In this list of 90 state lawmakers accused of sexual misconduct since the start of 2017 compiled by Associated Press, 33 of the lawmakers involved have had to leave office after the allegations against them had surfaced. That gives a naive base rate of 1 - 33/90 = 63% that Cuomo survives the scandal without being forced to leave office in some manner.

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Add a "points gained over the community forecast" metric, averaged (or not, or possibly both) over all questions answered by a user. (Perhaps with standard errors as well?) The scoring system currently awards the user points for both reducing entropy relative to a uniform distribution and also being more accurate than the community. While the second term in the scoring is zero for someone who exactly mimics the community forecast for a question, the information gain term rel to the uniform distribution isn't. This means a user can reliably accumulate po...

My forecast comes from a (1, 1, 0) x (1, 1, 0, 52) seasonal ARIMA estimated on the past 5 years, where shale oil seems to have caused a shift in the past trend. The Python script along with the csv file of past weekly data to run it on can be found here. I add some additional variance to the forecast to reflect model uncertainty. The model ends up with a median of 1.305, with a 50% confidence interval (1.297, 1.313).

— edited by ege_erdil

Similar to @SimonM's approach, but with a slightly different model and a different spot price at $48.5k. I end up with 10-90th percentiles

[58399, 58399, 58481, 63715, 70181, 78044, 89504, 105414, 138002]

A dumb AR(1) estimated on the log transformed past oil consumption data and then simulated forward from 2020 using Monte Carlo gives about 29% odds for this. It's not a perfect model because the residuals in the AR(1) still look autocorrelated, but correcting for the autocorrelation doesn't do much to the result, so I stopped searching for better time series models at this point. I doubt the estimate would change much even with better models. Obviously this approach is rather stupid but I don't think we have any other way of grappling with the question ...
The resolution criteria for this question are unclear. > A great power is a nation generally considered to have large amounts of military might and influence. While there is no established definition, for the purpose of this article, a great power is one of the top 10 nations by military spending according to the most recent report released by the Stockholm International Peace Research Institute (see latest report here). As of 2020, the great powers are therefore the United States, China, India, Russia, Saudi Arabia, France, Germany, the United Kingdom,...

The fact that there is no restriction for the poll to be representative makes this question very likely to resolve positively. You can easily get such a result if you poll a specific subset of physicists, say, a subset working at an institute where the Everett interpretation is particularly popular.

There's a strong base rate in favor of this happening. Since the minimum wage increase in 1981, the US has had a minimum wage increase whenever the ratio of annual median family income to the hourly federal minimum wage has exceeded 10k, and this ratio stood over 11k in 2019. Fitting a logistic regression model to the past data gives very strong chances that there's a minimum wage increase - about 40% per year taking 2019 as a reference point. That alone gives a base rate of 1 - 0.6^4 = 87% in favor of there being a minimum wage increase, even if median ...
There's a simple model proposed by Robert Lucas for the dynamics of "economic miracles" in real GDP per capita growth terms: the GDP per capita growth of a well managed country X is growth_c = growth_(leader) * ((GDP per capita of leader)/(GDP per capita of X))^(theta) where theta is some exponent to be estimated on the data, usually about 2/3. This model describes past economic miracles remarkably well, that of Japan for example. Estimating it on the Chinese data from 1990 to 2018 gives a theta of about 0.64, in which case we can simulate the model fo...

@SimonM 25% agrees exactly with the prediction of this bootstrap script at a 1 year horizon.

@SimonM It doesn't; but after some cursory Googling of the first 10 lawmakers I found that:

3 were removed in less than 1 month
3 were removed in approximately 1 month
1 was removed in approximately 2 months
2 were removed in approximately 3 months
1 was removed in approximately 4 months

The current time window is about 4 months long, so I would say that the probability of Cuomo resigning within the allotted time window conditional on him resigning at all is about 90%.

— edited by ege_erdil

@casens That change is fine.

I think the current (changed) title of the question is misleading. The question is not about whether RH will be proven by 2100, but whether it will be proven to be true conditional on it being proven to be either true or false by 2100. (Also, Riemann's name is misspelled in the title, but that's a smaller quibble...)

— edited by ege_erdil

My estimate is the product of four probabilities: 1. Erdogan is alive and in good physical condition to run for the election in 2023. According to actuarial life tables, someone of Erdogan's age has about 4.5% chances of dying in the next 2.5 years. I adjust this down to 3% because Erdogan doesn't seem to have any apparent health problems, and I give a 1% chance that he might become unfit for office in some other way before 2023. Therefore I give this scenario a probability of 96%. 2. Erdogan will be eligible to run in 2023. Under the current constitut...

Comes from fitting an AR(1) to GISS "Global-mean monthly, seasonal, and annual means" data for global mean temperatures since 1982 and bootstrapping the probability from past errors of the model. I think the community estimate of 40% is slightly too high.