It’s time to stop being polite. Here are 37 takes on how moderation does or doesn't help candidates win — and why I trust outsiders to build election models more than academics.
Interesting, I work in bioinformatics (so need data science + biology domain knowledge). We deal with the same issues. I wish there was more understanding that even in technical fields, analysis is never just mechanical number-crunching—it’s shaped by human discernment: which variables to include, how to frame the question, what assumptions to privilege. At its core, data science is as much an art of judgment as it is a science of measurement. Unlike the physical sciences, where controlled experiments and natural laws provide a stable foundation, data science deals with messy, contingent, and socially constructed data. In practice, this means models are always provisional, interpretive, and deeply dependent on human choices. In theory, too, the field was never purely positivist: it emerged as a hybrid discipline precisely because it had to combine rigorous statistical methods with the softer skills of inference, domain knowledge, and interpretive judgment. That’s what makes it powerful—but also what demands humility, a quality often missing in political data analysis.
I'd elaborate on point 28, the comment, "with most showing no statistically significant effect at all." This is a common and annoying feature in academic debates, using failure to reject a null hypothesis as evidence the null hypothesis is true. In the most extreme form, one person will publish a result, and another person will claim to have refuted it with a study with a smaller sample size and sloppier work that fails to find a significant effect. Absence of evidence is not evidence of absence.
If many different model specifications agree on the direction of the effect, most most are not individually significant statistically at the 5% level, that can give you more confidence in a result than a single specification with a very low p-value. Every directionally correct specification is evidence for the result, even if not very strong on its own.
The authors predict next month's stock market return using 12 monthly values of 15 indicators (stock market return, interest rates, market price-earnings ratio, etc.). That's 300 variables from which they fit 12,000 parameters by generating random Fourier features. Of course this allows them to fit the last 12 months' stock market returns perfectly. They also find it predicts next month's return better than conventional models.
This defies conventional wisdom in which modelers try to focus on a signal and ignore the noise, selecting only a few key indicators with strong statistical evidence and plausible causal links, and combining them only in simple models. Using too many variables and parameters in overly complex model is supposed to lead to "overfitting," models that explain the past perfectly and the future not at all.
"Of course this allows them to fit the last 12 months' stock market returns perfectly. They also find it predicts next month's return better than conventional models."
That paper is 1.5 years old. How has their model done at predicting stock returns over that time?
Too “left” for the centrist, and too mealy mouthed for the liberals. Kamala didn’t run too far to the left (she campaigned with Liz Cheney lmao), but she did try to be everything to everyone. Voters want someone with convictions, that’s why you have people who voted Trump-AOC on the same ticket.
Are there many of those people? Is there data on that? Interested to know. I do agree that at least part of it is that voters like people who seem to speak their mind without caring what others think (which is not quite the same as having convictions; Trump doesn’t really have convictions other than in court).
I think Zohran is a good use case here to show how being like-able is ultimately more important than reaching the median voter, as there is no such thing as an actual ‘median voter’.
I also attached some Wiki election data for AOC’s congressional district, where she over-performed Harris by ~5%. This is all to say, if you base your campaign around reaching the mythical median voter, you’re dooming yourself, voters aren’t looking for milquetoast candidates.
In 3 1/2 years time it’ll be: “did republicans shift too far to the right.” There’ll be hot takes on how the GOP brand is toast. I think we’re in an electoral period like 1884-1900 and we’re in for a few more swings yet.
Wow. An hour video (thankfully with transcript) and a shotgun blast of random definitions, facts, factoids, and footnotes.
A couple of minor points.
1)
Incumbents may become more moderate in office because that is what is necessary to get legislation to pass. Bernie is consistent, and has had almost no actual impact on bills that have become law. AOC is learning how to play nice during recess and share toys and in her short time in the House has more substantial legislative impact than Bernie in his entire career.
2)
The number of tokens (footnote 7) is not the big deal. If it were, then ChatGPT would only be capable of some raw probability of the empirical best next token, which is pretty boring (like a next letter predictor for English that always suggests 'e').
The important thing in LLMs is that they are trained and model associations based on combinations of those tokens. So given a sequence of tokens and a small subset of the trillions-factorial permutations of tokens in the training set, it predicts the most likely next token in the sequence. You think a trillion tokens are a lot? Wrap your brain around a trillion factorial. There is a reason it is a Large Language Model, not a Large Token Model.
In practice, this would make your point even stronger. A small collection of relatively discrete election results is a poor candidate for LLM style methods.
Unfortunately for your point, even though there is a 'LM' in glmnet, it isn't a LLM. The 'glm' is "Generalized Linear Model". Calling it a ML tool is academically accurate, but confusing for people who think ML == AI == LLM == ChatGPT.
It sure looks like a reasonable package to analyze this kind of data, although the devil is in the details.
Calling glmnet “machine learning”… I was at risk of an aneurysm until your final point there.
Anyway, my theory on how social media ruined perception of academia is that it disproved by counter example Plato’s view, namely that cultivating intellectual excellence will entail improvement of other virtues. Bonica and Grumbach’s behavior in this instance is a case in point.
Interesting, I work in bioinformatics (so need data science + biology domain knowledge). We deal with the same issues. I wish there was more understanding that even in technical fields, analysis is never just mechanical number-crunching—it’s shaped by human discernment: which variables to include, how to frame the question, what assumptions to privilege. At its core, data science is as much an art of judgment as it is a science of measurement. Unlike the physical sciences, where controlled experiments and natural laws provide a stable foundation, data science deals with messy, contingent, and socially constructed data. In practice, this means models are always provisional, interpretive, and deeply dependent on human choices. In theory, too, the field was never purely positivist: it emerged as a hybrid discipline precisely because it had to combine rigorous statistical methods with the softer skills of inference, domain knowledge, and interpretive judgment. That’s what makes it powerful—but also what demands humility, a quality often missing in political data analysis.
I'd elaborate on point 28, the comment, "with most showing no statistically significant effect at all." This is a common and annoying feature in academic debates, using failure to reject a null hypothesis as evidence the null hypothesis is true. In the most extreme form, one person will publish a result, and another person will claim to have refuted it with a study with a smaller sample size and sloppier work that fails to find a significant effect. Absence of evidence is not evidence of absence.
If many different model specifications agree on the direction of the effect, most most are not individually significant statistically at the 5% level, that can give you more confidence in a result than a single specification with a very low p-value. Every directionally correct specification is evidence for the result, even if not very strong on its own.
Points 33 - 35 are more controversial than suggested in the post. A seminal paper (https://economics.yale.edu/sites/default/files/2024-01/The%20Journal%20of%20Finance%20-%202023%20-%20KELLY%20-%20The%20Virtue%20of%20Complexity%20in%20Return%20Prediction%20%281%29.pdf) last year that has ignited a firestorm of controversy claims that machine learning beats conventional parsimonious fitting even with small datasets, and that fitting all historical data exactly is a good thing.
The authors predict next month's stock market return using 12 monthly values of 15 indicators (stock market return, interest rates, market price-earnings ratio, etc.). That's 300 variables from which they fit 12,000 parameters by generating random Fourier features. Of course this allows them to fit the last 12 months' stock market returns perfectly. They also find it predicts next month's return better than conventional models.
This defies conventional wisdom in which modelers try to focus on a signal and ignore the noise, selecting only a few key indicators with strong statistical evidence and plausible causal links, and combining them only in simple models. Using too many variables and parameters in overly complex model is supposed to lead to "overfitting," models that explain the past perfectly and the future not at all.
"Of course this allows them to fit the last 12 months' stock market returns perfectly. They also find it predicts next month's return better than conventional models."
That paper is 1.5 years old. How has their model done at predicting stock returns over that time?
I'm going to guess: not as well.
It did fine through June 2025, I haven't seen the July 2025 results yet. But it hasn't done well enough to satisfy critics.
https://www.aqr.com/Insights/Research/Working-Paper/Understanding-The-Virtue-of-Complexity
However the main debate is not whether the model works, but whether it is merely an computationally expensive way to mimic a simple momentum model.
Please create a Silver Bulletin podcast feed!
Too “left” for the centrist, and too mealy mouthed for the liberals. Kamala didn’t run too far to the left (she campaigned with Liz Cheney lmao), but she did try to be everything to everyone. Voters want someone with convictions, that’s why you have people who voted Trump-AOC on the same ticket.
Are there many of those people? Is there data on that? Interested to know. I do agree that at least part of it is that voters like people who seem to speak their mind without caring what others think (which is not quite the same as having convictions; Trump doesn’t really have convictions other than in court).
I think Zohran is a good use case here to show how being like-able is ultimately more important than reaching the median voter, as there is no such thing as an actual ‘median voter’.
I also attached some Wiki election data for AOC’s congressional district, where she over-performed Harris by ~5%. This is all to say, if you base your campaign around reaching the mythical median voter, you’re dooming yourself, voters aren’t looking for milquetoast candidates.
Thank you for the glorious take down. Couldn't have happened to a nicer person!
As thorough as humanly possible...
In 3 1/2 years time it’ll be: “did republicans shift too far to the right.” There’ll be hot takes on how the GOP brand is toast. I think we’re in an electoral period like 1884-1900 and we’re in for a few more swings yet.
Great conversation!
Wow. An hour video (thankfully with transcript) and a shotgun blast of random definitions, facts, factoids, and footnotes.
A couple of minor points.
1)
Incumbents may become more moderate in office because that is what is necessary to get legislation to pass. Bernie is consistent, and has had almost no actual impact on bills that have become law. AOC is learning how to play nice during recess and share toys and in her short time in the House has more substantial legislative impact than Bernie in his entire career.
2)
The number of tokens (footnote 7) is not the big deal. If it were, then ChatGPT would only be capable of some raw probability of the empirical best next token, which is pretty boring (like a next letter predictor for English that always suggests 'e').
The important thing in LLMs is that they are trained and model associations based on combinations of those tokens. So given a sequence of tokens and a small subset of the trillions-factorial permutations of tokens in the training set, it predicts the most likely next token in the sequence. You think a trillion tokens are a lot? Wrap your brain around a trillion factorial. There is a reason it is a Large Language Model, not a Large Token Model.
In practice, this would make your point even stronger. A small collection of relatively discrete election results is a poor candidate for LLM style methods.
Unfortunately for your point, even though there is a 'LM' in glmnet, it isn't a LLM. The 'glm' is "Generalized Linear Model". Calling it a ML tool is academically accurate, but confusing for people who think ML == AI == LLM == ChatGPT.
It sure looks like a reasonable package to analyze this kind of data, although the devil is in the details.
Calling glmnet “machine learning”… I was at risk of an aneurysm until your final point there.
Anyway, my theory on how social media ruined perception of academia is that it disproved by counter example Plato’s view, namely that cultivating intellectual excellence will entail improvement of other virtues. Bonica and Grumbach’s behavior in this instance is a case in point.