In the spirit of Slate Star Codex, I will be offering some of my predictions for 2020. In January 2021 I will return to these and grade them.
The predictions fall into three categories: US Politics (there are many of these since it’s an election year), Personal (predictions about what I will do next year), and Other (sports, international events, etc.).
Before I get to the actual predictions, I’m going pre-register what exactly I plan to look at:
- Calibration: among events to which I assign (say) a 70% chance, will roughly 70% of them happen? I will judge calibration by bucketing results, seeing how many events in each bucket should have come true if I were perfect, and seeing how many actually came true. I’m committing to using the following buckets: [0.5, 0.6); [0.6, 0.7); [0.7, 0.8); [0.8, 0.9); [0.9, 0.95); and [0.95, 1). For events below to which I have assigned probability
, I will judge whether the event did not happen as part of the bucket containing
.
- Personal optimism/pessimism: most of the Personal events involve me accomplishing some task. Am I too optimistic, too pessimistic, or about right in my judgment of what I will get done next year?
- 59 of the events are on PredictIt. I first made predictions without looking at PredictIt, then looked at PredictIt and updated on those. While I will mostly be assessing my original predictions, I will also look at how I performed — both with my initial guesses and my updated guesses — relative to PredictIt (using a log scoring rule).
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 am really knowledgeable about the underlying subject; sometimes I just feel like giving two significant digits.
With all that said, here are my probabilities:
I. US Politics
A. Primary elections
1. Biden wins Iowa caucus popular vote (EDIT 1/30/2020: I meant after those who caucused for candidates with less than 15% support realign): 25%
2. Sanders wins Iowa caucus popular vote: 21%
3. Warren wins Iowa caucus popular vote: 10%
4. Buttigieg wins Iowa caucus popular vote: 27%
5. Klobuchar wins Iowa caucus popular vote: 13%
6. Biden wins New Hampshire primary popular vote: 21%
7. Sanders wins New Hampshire primary popular vote: 26%
8. Warren wins New Hampshire primary popular vote: 20%
9. Buttigieg wins New Hampshire primary popular vote: 21%
10. Klobuchar wins New Hampshire primary popular vote: 6%
11. Biden wins Nevada caucus popular vote: 37%
12. Sanders wins Nevada caucus popular vote: 33%
13. Warren wins Nevada caucus popular vote: 9%
14. Buttigieg wins Nevada caucus popular vote: 10%
15. Klobuchar wins Nevada caucus popular vote: 5%
16. Biden wins South Carolina primary popular vote: 67%
17. Sanders wins South Carolina primary popular vote: 15%
18. Warren wins South Carolina primary popular vote: 7%
19. Buttigieg wins South Carolina primary popular vote: 5%
20. Klobuchar wins South Carolina primary popular vote: 2%
21. Biden wins the most Super Tuesday delegates (not counting superdelegates): 43%
22. Sanders wins the most Super Tuesday delegates (not counting superdelegates): 18%
23. Warren wins the most Super Tuesday delegates (not counting superdelegates): 14%
24. Buttigieg wins the most Super Tuesday delegates (not counting superdelegates): 15%
25. Klobuchar wins the most Super Tuesday delegates (not counting superdelegates): 6%
26. Biden wins the Democratic nomination: 38%
27. Sanders wins the Democratic nomination: 16%
28. Warren wins the Democratic nomination: 17%
29. Buttigieg wins the Democratic nomination: 19%
30. Klobuchar wins the Democratic nomination: 6%
31. Multiple convention votes are needed to choose the Democratic nominee: 32%
32. Stacey Abrams is the Democratic running mate: 16%
33. Kamala Harris is the Democratic running mate: 8%
34. Cory Booker is the Democratic running mate: 6%
35. Amy Klobuchar is the Democratic running mate: 5%
36. Pete Buttigieg is the Democratic running mate: 4%
37. Sherrod Brown is the Democratic running mate: 3%
38. Julian Castro is the Democratic running mate: 2%
39. Trump wins the Republican nomination: 96%
B. General election
40. Trump wins the general election popular vote: 26%
41. The Republican nominee wins the 2020 presidential election: 44%
42. The Republican nominee wins Arizona: 42%
43. The Republican nominee wins Colorado: 18%
44. The Republican nominee wins Florida: 55%
45. The Republican nominee wins Georgia: 73%
46. The Republican nominee wins Iowa: 63%
47. The Republican nominee wins Maine: 10%
48. The Republican nominee wins ME-02: 60%
49. The Republican nominee wins Michigan: 40%
50. The Republican nominee wins Minnesota: 28%
51. The Republican nominee wins NE-02: 48%
52. The Republican nominee wins Nevada: 25%
53. The Republican nominee wins New Hampshire: 36%
54. The Republican nominee wins North Carolina: 60%
55. The Republican nominee wins Ohio: 80%
56. The Republican nominee wins Pennsylvania: 45%
57. The Republican nominee wins Texas: 75%
58. The Republican nominee wins Virginia: 20%
59. The Republican nominee wins Wisconsin: 50%
60. Democrats keep the House: 75%
61. Republicans keep the Senate: 80%
62. Doug Jones (D-AL) keeps his seat: 30%
63. Martha McSally (R-AZ) keeps her seat: 50%
64. Cory Gardner (R-CO) keeps his seat: 40%
65. Joni Ernst (R-IA) keeps her seat: 80%
66. Susan Collins (R-ME) keeps her seat: 65%
67. Gary Peters (D-MI) keeps his seat: 75%
68. Jeanne Shaheen (D-NH) keeps her seat: 90%
69. Thom Tillis (R-NC) keeps his seat: 65%
70. John Cornyn (R-TX) keeps his seat: 85%
71. The Republican and Democratic nominees total to less than 95% of the vote: 35%
C. Other
72. Trump is removed from office by the US Senate in 2020: 4%
73. Ruth Bader Ginsburg remains a Supreme Court justice at the end of 2020: 80%
74. Tulsi Gabbard runs for president as an independent: 17%
75. Tulsi Gabbard is a Fox News contributor or anchor by the end of 2020: 23%
II. Personal
A. Blog
76. I write 5 or more blog posts in 2020: 94%
77. I write 10 or more blog posts in 2020: 73%
78. I write 20 or more blog posts in 2020: 48%
79. I write 30 or more blog posts in 2020: 26%
80. I write 50 or more blog posts in 2020: 12%
81. The total number of views of my blog in 2020 is at least 500: 95%
82. The total number of views of my blog in 2020 is at least 1000: 80%
83. The total number of views of my blog in 2020 is at least 2000: 65%
84. The total number of views of my blog in 2020 is at least 5000: 35%
85. The total number of views of my blog in 2020 is at least 10000: 20%
86. The total number of views of my blog in 2020 is at least 100000: 4%
B. Grad school/research
87. I publish a computer science paper in a conference held in 2020 or a journal edition issued in 2020 (RadicalXchange does not count): 58%
88. I do a plurality of my work in 2020 with Tim Roughgarden: 75%
89. I prove a 3/4-approximation positive result for the problem I’m currently working on by the end of 2020: 62%
90. I go to EC (in Budapest) in 2020: 40%
C. EA/Rationality
91. I’m a SPARC staff member in 2020: 33%
92. By the end of 2020, I’m part of a project to implement something like my election-charity platform idea, with a registered domain name: 23%
93. I (co-)run some OBNYC (NYC rationalist) meetup in 2020: 65%
94. I spend at least a month in California in 2020: 80%
95. By the end of 2020, animal welfare considerations will have substantial influence over my diet: 38%
96. I consider myself a vegetarian at the end of 2020: 15%
97. I consider myself a vegan at the end of 2020: 2%
98. I make a donation of at least $50 to a third world poverty charity in 2020 (counting College Pulse donations): 93%
99. I make a donation of at least $50 to an existential risk/long-term future charity in 2020: 45%
100. I make a donation of at least $50 to an animal welfare charity in 2020: 60%
101. I go to the RadicalXchange conference (in Sao Paulo) in 2020: 25%
D. Politics
For events 102 through 108, I have put the options Joe Biden, Bernie Sanders, Elizabeth Warren, Pete Buttigieg, Amy Klobuchar, and None of the Above in a random order that I’ve recorded and called them Option 1 through Option 6. (“None of the Above” includes the possibility that I don’t vote.)
102. I will vote for [Option 1]: 2%
103. I will vote for [Option 2]: 50%
104. I will vote for [Option 3]: 6%
105. I will vote for [Option 4]: 25%
106. I will vote for [Option 5]: 13%
107. I will vote for [Option 6]: 4%
108. I use the same word to describe my political identity at the end of 2020 as I do now: 72%
109. I try to vote-swap in the 2020 presidential election: 30%
110. I successfully vote-swap in the 2020 presidential election: 15%
E. Other
111. I’m a Mathcamp mentor in 2020: 20%
112. I publish a non-academic piece of writing in some publication in 2020: 16%
113. I read a book in 2020: 60%
114. I read at least two books in 2020: 35%
115. I read at least three books in 2020: 25%
116. I write at least three puzzle hunt style puzzles in 2020: 30%
117. I write a song (with music, not just lyrics) in 2020: 15%
118. I play squash on at least 25 days in 2020: 78%
119. I visit a country that is not the United States, Hungary, or Brazil in 2020: 25%
III. Other
A. International events
120. Benjamin Netanyahu is the Prime Minister of Israel at the end of 2020: 25%
121. Benny Gantz is the Prime Minister of Israel at the end of 2020: 35%
122. A date is set for a Scottish independence referendum in 2020 (the date doesn’t have to be in 2020): 24%
123. A war breaks out between two countries, both of which either have population in the top 40 or have nuclear weapons. For this to be labeled “true,” it must be a war between the two countries’ governments; for example, if the United States initiates a counter-insurgency operation in Nigeria, that will not count: 7%
124. In July-December 2020, there is a protest in Hong Kong that draws more than 1 million protesters according to the protest organizers or CHRF: 35%
B. Tennis
125. Roger Federer wins a grand slam tournament in 2020: 37%
126. Someone besides Djokovic, Nadal, and Federer wins a men’s singles grand slam tournament in 2020: 50%
127. Serena Williams wins a grand slam tournament in 2020: 50%
128. Four different people win the women’s singles grand slam tournaments in 2020: 55%
C. Computer science
129. The unique games conjecture is widely considered resolved by the end of 2020: 6%
130. P vs. NP is widely considered resolved by the end of 2020: 1%
D. Other
131. The third book in the Kingkiller Chronicle has a publication date set by the end of 2020 (the date doesn’t have to be in 2020): 16%
132. Despacito remains the most-watched YouTube video at the end of 2020: 72%
***
PredictIt has a market for 59 of the 132 above events. Below I’ve included my probability for each event before looking at PredictIt (these are the probabilities listed above), PredictIt’s probability, and my updated probability after looking at PredictIt.
| Prediction # | Original Prob. | PredictIt Prob. | Updated Prob. |
| 1 | 25% | 16% | 20% |
| 2 | 21% | 36% | 29% |
| 3 | 10% | 11% | 11% |
| 4 | 27% | 25% | 26% |
| 5 | 13% | 7% | 10% |
| 6 | 21% | 15% | 18% |
| 7 | 26% | 47% | 36% |
| 8 | 20% | 10% | 16% |
| 9 | 21% | 19% | 20% |
| 10 | 6% | 5% | 5% |
| 11 | 37% | 39% | 38% |
| 12 | 33% | 37% | 35% |
| 13 | 9% | 8% | 9% |
| 14 | 10% | 7% | 9% |
| 15 | 5% | 4% | 4% |
| 16 | 67% | 75% | 71% |
| 17 | 15% | 10% | 13% |
| 18 | 7% | 3% | 5% |
| 19 | 5% | 5% | 5% |
| 20 | 2% | 2% | 2% |
| 26 | 38% | 32% | 38% |
| 27 | 16% | 23% | 19% |
| 28 | 17% | 10% | 16% |
| 29 | 19% | 11% | 17% |
| 30 | 6% | 4% | 5% |
| 31 | 32% | 37% | 36% |
| 32 | 16% | 11% | 12% |
| 33 | 8% | 12% | 11% |
| 34 | 6% | 4% | 5% |
| 35 | 5% | 9% | 8% |
| 36 | 4% | 6% | 5% |
| 37 | 3% | 3% | 3% |
| 38 | 2% | 5% | 4% |
| 39 | 96% | 89% | 95% |
| 40 | 26% | 29% | 28% |
| 41 | 44% | 47% | 45% |
| 42 | 42% | 57% | 52% |
| 43 | 18% | 22% | 21% |
| 44 | 55% | 62% | 60% |
| 45 | 73% | 72% | 72% |
| 46 | 63% | 65% | 64% |
| 48 | 60% | 79% | 75% |
| 49 | 40% | 39% | 39% |
| 50 | 28% | 36% | 33% |
| 52 | 25% | 25% | 25% |
| 53 | 36% | 35% | 35% |
| 54 | 60% | 57% | 58% |
| 55 | 80% | 69% | 72% |
| 56 | 45% | 43% | 44% |
| 57 | 75% | 80% | 78% |
| 58 | 20% | 17% | 18% |
| 59 | 50% | 44% | 46% |
| 62 | 30% | 16% | 20% |
| 63 | 50% | 40% | 43% |
| 64 | 40% | 21% | 27% |
| 66 | 65% | 51% | 55% |
| 69 | 65% | 53% | 57% |
| 72 | 4% | 10% | 5% |
| 120 | 25% | 40% | 39% |
(A note regarding predictions 32 through 38: for technical reasons I couldn’t figure out precisely what probabilities that PredictIt prices implied. I made an assumption that seemed reasonable to me.)
***
Here are the Personal events that I will be considering when judging whether I was optimistic or pessimistic: 76 through 80 (weight 1/5 each); 80 through 86 (weight 1/6 each); 87; 89; 92-93; 112; 113-115 (weight 1/3 each); 116-119.
Each of these will score a point (or less if they are down-weighted) if I accomplish them. My probabilities imply a predicted score of 5.1 out of 12. Scoring higher is evidence of pessimism; scoring lower is evidence of optimism.
***
Two final notes. First: if you’re interested in competing with me by assigning your own probabilities to some of these events and seeing how you fare against me at the end of the year (as judged by a log scoring rule), please make your predictions by January 5th and send them to me as a text file where each line has the format “[event #], [probability as a decimal]”. Note that I reserve the right to change my predictions for the purpose of the contest by January 5th, should any relevant information come out (e.g. a Democratic candidate drops out).
Second: if you’d like to bet with me on any of the above and I know you, feel free to reach out! I am not adopting Scott Alexander’s policy of being willing to bet with anyone whose opinion differs by 10% or more, but I might nevertheless be willing to make bets.
Unexpected Values 
You’re probably already aware of this, but note that sets of predictions like
trade off against each other to give an inflated calibration score. As an extreme example, if I predicted
then I would be guaranteed a perfect score for calibration (even if were clueless about politics and had no idea who any of these people are).
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(I ended up talking to saprmarks about this; here’s a summary of what I ended up concluding from our conversation.)
The best way to test for calibration is to have lots of *independent* predictions. On the other hand, if you have negatively correlated predictions in the same bucket, that artificially inflates how calibrated you look. Conversely, if you have positively correlated predictions in the same bucket, that makes you look uncalibrated.
saprmarks has demonstrated an example of the former phenomenon with the five 20% predictions he wrote down. Since exactly one of them will happen, you can artificially inflate your calibration score by making many such sets of five predictions. But importantly, this works because the predictions fall into the same bucket (when evaluating calibration). If they fall in different buckets (because they have different probabilities) then it doesn’t artificially inflate calibration score (or maybe does a little? not sure).
And example of positive correlation making you look uncalibrated is if you wrote down the following predictions: “Biden wins New Hampshire: 20%”; “A candidate whose last name has 5 letters wins New Hampshire: 20%”; “A candidate who was a senator from Delaware wins New Hampshire: 20%”; etc. Then even if you’re perfectly calibrated, with 80% probability none of these will happen (so you’ll look underconfident) and with 20% probability all will happen (so you’ll look very overconfident). But again, this is the case if they end up in the same bucket.
My negatively correlated predictions tend to fall into different buckets. The New Hampshire ones are split 3-1-1 across different buckets; same for Iowa. For Nevada and South Carolina they are split 2-2-1 and 2-1-1-1, respectively. So it’s not ideal, but my guess is that the effect on my calibration scores will be minimal.
I also have some positively correlated predictions in the same buckets, e.g. “Sanders wins Iowa” and “Sanders wins New Hampshire”, which should have the opposite effect.
So on net I think I feel pretty okay about this. I think these sets of predictions might end up with me overstating my calibration a little, but not very much.
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