Predictions for 2021

Just as I did last year, I have some probabilistic predictions for 2021. In January 2022 I will return to grade them, just as in a week or two I’ll grade my 2020 predictions. This year the predictions fall into four categories: U.S. politics (#1-17 below), COVID (#18-39), Miscellaneous (#40-53), and Personal (#54-100).

Note that these predictions are not necessarily well-informed. You should consider my U.S. politics predictions to be more or less reasonable, but it’s totally possible that my COVID predictions are crazy. This is just to say: if you think you have decent COVID information, don’t use my predictions to inform your decisions.

As last year, I’ll be assessing my predictions on calibration (the details are the same as last year, except that [0.9, 1) will be one bucket). I will also be evaluating the predictions on personal pessimism/optimism — whether I’m too optimistic, too pessimistic, or about right in my judgment of what I will get done next year — this time with weights proportional to the importance of each goal (see below).

I’d also like to advertise a challenge for my readers. You can email me with your predictions for a subset of my predictions with your own prediction. I’ll judge your predictions against mine using the logarithmic scoring rule. I have the advantage of having chosen the questions, but you have the advantage of having seen my predictions. In theory this gives you the upper hand: for example, you could put down my probabilities for every event except a few where you think I clearly messed up. Consider this an exercise in second-order knowledge: figuring out how much weight to put on my probabilities despite not knowing my reasoning behind them. Your score will be the sum, over all questions you choose, of your log score minus my log score.1 (So you can guarantee yourself a score of 0 by sending me an empty list.) [Edit: the “choose at least 40 predictions” requirement has been removed.]

Please make your predictions by January 4th at 11:59pm ET and send them to me as a text file where each line has the format “[event #], [probability as a decimal]”. I will throw out any predictions that resolve (or nearly resolve) by January 4th.

Finally, a note: many of the probabilities I give are numbers like 34% (not multiples of 5%). Don’t take this to mean that I somehow precisely calculated the probabilities or that I’m really knowledgeable about the underlying subject; sometimes I just feel like giving two significant digits.

With all that said, here are my predictions:

I. US Politics

  1. Jon Ossoff wins his election: 45%
  2. Raphael Warnock wins his election: 60%
  3. Ossoff and Warnock both win their elections: 42%
  4. Democrats hold the Virginia State House: 61%
  5. Andrew Yang is elected mayor of New York: 24%
  6. The average Democratic overperformance in margin in congressional and state legislative elections, as calculated by FiveThirtyEight (see e.g. here), is at least 5%: 21%
  7. …at least 0%: 38%
  8. …at least -5%: 66%
  9. Major* legislation not directly related to COVID (excluding international agreements) passes: 45%
  10. Major* infrastructure legislation passes: 18%
  11. Joe Biden signs an executive order authorizing a major cancellation of student debt: 59%
  12. Biden is the president of the United States at the end of 2021: 94%
  13. Donald Trump receives a presidential pardon (possibly from himself): 35%
  14. Hunter Biden is charged with a crime: 15%
  15. Donald Trump is charged with a crime: 28%
  16. At least one member of the Senate stops caucusing with the party they are currently caucusing with: 23%
  17. Donald Trump has a TV show or network at some time in 2021: 21%

* For legislation to be considered major, a substantial amount of effort/political capital needs to be spent on it. Major legislation passes on average once every 2-3 years. Examples include the 2009 stimulus bill, Obamacare, and Trump’s 2017 tax law.

II. COVID

  1. I receive my first dose of a COVID vaccine by the end of March: 12%
  2. …the end of April: 34%
  3. …the end of May: 60%
  4. …the end of June: 74%
  5. …the end of July: 82%
  6. …the end of August: 87%
  7. …the end of 2021: 97%
  8. At least 50% of people living in the U.S. receive at least one COVID vaccine dose by the end of 2021: 75%
  9. At least 60%: 58%
  10. At least 70%: 43%
  11. At least 80%: 20%
  12. At least 90%: 4%
  13. Per official statistics, at least 100 thousand Americans die of COVID in 2021: 82%
  14. …at least 200 thousand: 64%
  15. …at least 500 thousand: 25%
  16. …at least 1 million: 8%
  17. I or one of the seven people I share 25% of my genes with tests positive for COVID: 30%
  18. I test positive for COVID: 5%
  19. I go to my office at Columbia at least once by the end of May: 32%
  20. EC is held at least partially in Budapest: 36%
  21. Canada/USA Mathcamp is held in person (I have no inside information on this): 25%
  22. SPARC is held in person (I have no inside information on this): 38%

III. Miscellaneous

  1. China is involved in an international (counting Taiwan and Hong Kong) conflict that has 1,000 casualties: 9%
  2. A normalization of relationships between Israel and at least one majority-Muslim country is initiated in 2021 during the Biden administration: 48%
  3. Putin is the president of Russia at the end of 2021: 88%
  4. Benjamin Netanyahu is the Prime Minister of Israel at the end of 2021: 57%
  5. Scott Alexander starts publishing again: 85%
  6. Taylor Swift releases her tenth studio album: 65% (45b, won’t be graded — Taylor Swift releases her eleventh studio album: 13%)
  7. “Foklore” wins a Grammy for Album of the Year: 61%
  8. The third book in the Kingkiller Chronicle has a publication date set by the end of 2021 (the date doesn’t have to be in 2021): 13%
  9. Roger Federer wins a grand slam tournament in 2021: 26%
  10. Someone besides Djokovic, Nadal, and Federer wins a men’s singles grand slam tournament in 2021: 59%
  11. Serena Williams wins a grand slam tournament in 2021: 32%
  12. All women’s singles grand slam tournaments in 2021 are won by different people: 68%
  13. P vs. NP is widely considered resolved by the end of 2021: 1%
  14. A (non-trivial) update on GPT-3 is released: 62%

IV. Personal

A. Academic

  1. I summarize for my blog, or review for a journal, at least 20 papers: 75%
  2. …at least 30 papers: 65%
  3. …at least 40 papers: 48%
  4. …at least 50 papers: 20%
  5. I attend EC (counts if I go to at least five talks): 74%
  6. The paper I’m writing on aggregating predictions is accepted to a conference or journal: 73%
  7. I write and submit a paper on prediction aggregation and online learning (this is a different one from the one in #59): 77%
  8. …and that paper is accepted: 47%
  9. I resolve the “preventing arbitrage from collusion” scoring rules problem: 40%
  10. My scoring rules paper from a while ago finally gets accepted somewhere: 55%
  11. I publish, or begin writing with the intention to publish, a paper following up directly on “No-Regret and Incentive Compatible Online Learning”: 45%
  12. I publish a computer science paper in a conference held in 2021 or a journal edition issued in 2021: 85%
  13. I publish a paper or note on Zipf’s law: 28%

B. Blog

  1. I write 10 or more blog posts in 2021: 92%
  2. I write 20 or more blog posts in 2021: 78%
  3. I write 30 or more blog posts in 2021: 50%
  4. I write 50 or more blog posts in 2021: 9%
  5. The total number of views of my blog in 2021 is at least 5,000: 95%
  6. The total number of views of my blog in 2021 is at least 10,000: 83%
  7. The total number of views of my blog in 2021 is at least 20,000: 64%
  8. The total number of views of my blog in 2021 is at least 50,000: 27%
  9. The total number of views of my blog in 2021 is at least 100,000: 11%
  10. (Intentionally vague to avoid spoliers) I write a blog post about big aliens: 42%
  11. I publish a blog post on setting the right price: 36%
  12. I publish a blog post about Pi: 33%
  13. I publish a blog post about slowly converging series: 40%
  14. I publish a blog post on Zipf’s law: 80%
  15. I publish a blog post on Bayesian injustice: 25%

C. Other

  1. I vote in the Democratic primary of the New York mayoral election: 93%
  2. I rank Andrew Yang first in the Democratic primary of the New York mayoral election: 51%
  3. I stick to my virtue points system, or some variation, through the end of 2021: 70%
  4. I’m a SPARC staff member in 2021: 31%
  5. I’m a Mathcamp mentor in 2021: 55%
  6. I (co-)run some OBNYC (NYC rationalist) meetup in 2021: 47%
  7. I consider myself a vegetarian at the end of 2021: 29%
  8. I consider myself a vegan at the end of 2021: 4%
  9. I make a donation of at least $500 to a third world poverty charity in 2021: 66%
  10. I make a donation of at least $500 to existential risk/long-term in 2021: 79%
  11. I make a donation of at least $500 to animal welfare in 2021: 23%
  12. I have a tentative plan to take a gap year (or I take a gap year): 24%
  13. I play squash on at least 10 days in 2021: 65%
  14. I play squash on at least 20 days in 2021: 44%
  15. I visit a country that is not Hungary: 23%
  16. I publish a non-academic piece of writing in some publication in 2021: 33%
  17. I read a book in 2021: 65%
  18. I read at least two books in 2021: 44%
  19. I read at least three books in 2021: 30%

1. That is, for each question, if I assign probability p to the outcome that ends up happening and you assign probability q, your score will be \ln q - \ln p. For instance, if you say that Ossoff has a 20% chance of winning and he ends up losing, your score will be \ln 0.8 - \ln 0.55 (since I assigned a 0.55 chance to Ossoff losing).

9 thoughts on “Predictions for 2021

  1. Im quite surprised by the probability of the 42, would’ve expected something like 99%

    Thanks for an interesting idea! Probably would do something like that for myself

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  2. Tennis probabilities seem wildly off:

    Federer isn’t entered into Australian and wont enter RG so we’re looking at the probability he wins one of Wimbledon and the US. Betfair gives him odds of 10% and 6% respectively. (Generally punters are especially bullish on Federer, so we should expect these to be overestimates). Upper bound on his probability of winning either is 16% <> 62%.

    S. Williams is @ AO is 6%, @ RG is 6%, @ W is 8%, @ USO is 9%. Assuming these are independent she has a 26% chance. (Again, in reality they are probably correlated and her chances are lower). < 32%.

    Feels like you haven't updated (on the Mens side at least) for the emergence of the next generation of serious competition.

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    1. Text got messed up due to abuse of inequalities it appears hopefully this is better:

      Tennis probabilities seem wildly off:

      Federer isn’t entered into Australian and wont enter RG so we’re looking at the probability he wins one of Wimbledon and the US. Betfair gives him odds of 10% and 6% respectively. (Generally punters are especially bullish on Federer, so we should expect these to be overestimates). Upper bound on his probability of winning either is 16% 62%.

      S. Williams is @ AO is 6%, @ RG is 6%, @ W is 8%, @ USO is 9%. Assuming these are independent she has a 26% chance. (Again, in reality they are probably correlated and her chances are lower). < 32%.

      Feels like you haven't updated (on the Mens side at least) for the emergence of the next generation of serious competition.

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      1. Don’t see a difference. Maybe try phrasing things without using inequality signs at all? Not sure why it’s having issues, sorry.

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    2. Yup, my probabilities might be uninformed in some cases! Sounds like this is such a case. (Though note that I said 26%, not 62%, for Federer.)

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      1. Federer isn’t entered into Australian and wont enter RG so we’re looking at the probability he wins one of Wimbledon and the US. Betfair gives him odds of 10% and 6% respectively. (Generally punters are especially bullish on Federer, so we should expect these to be overestimates). Upper bound on his probability of winning either is 16% vs 26%. (16% already being a fairly heavy overestimate).

        Big 3 @ AO is 53%, @ RG is 63%, @ W is 45%, @ UO is 51%. Already we have a lower bound on anyone else winning at 55% just from Wimbledon, and realistically these events aren’t super correlated so the true figure is closer to 92% vs 62%.

        (Comments butchered by wordpress)

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        1. Huh ok. For what it’s worth, I do think they’re somewhat correlated. (Wait really, I would have put Nadal’s chances of winning RG at substantially above 63%, but I defer to the betting markets. But like, he keeps winning them!)

          I’ve updated to 15% for Federer, 23% for Williams, and like 78% for “someone besides big 3”.

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          1. Cool – it’s really hard for me to ever rate someone above 50% for a tournament (even someone like Nadal at RG). Ultimately you have to win a QF, SF and F against someone who isn’t a pushover AND be in good condition in 3 months time. If he’s fit his odds going into the tournament will be a bit higher, but if he hits the QFs and Thiem, Djokovic, et al are still in it, it’s definitely not a slam dunk.

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