The following is a sketch for granting America the benefits of ranked-choice voting, without requiring electoral reform, by collecting and publishing common knowledge that alters the game theory of party politics.
The story goes like this:
- Org builds powerful opinion surveying stack
- Org predicts the performance of hypothetical candidates and platforms in the general election before the primary
- Profit (via prediction markets)
- Org predictions are believed to be ~accurate
- Party primaries begin to nominate generally popular candidates, or else lose to parties that do
- America gets the benefits of RCV
Polarization and its discontents
Every governmental approval rating is nose-diving. This is caused by and contributes to the centrifuging of our political landscape. If we can neutralize key drivers of polarization, we might put enough slack into the knot to start to untie it. Two key drivers are internet-mediated polarization and politically-mediated polarization, and the thesis is that both can be addressed by surfacing the truth of who wants what across the country.
Internet-mediated polarization hardly needs an introduction. We live in bubbles of manufactured outrage and algorithmically rewarded scissor statements. Our podcasts, feeds, and op-eds are full of claims about the hypocrisy of our out groups. They can be amusing or enraging, and either way, the algorithm decides we need more. One goal is to contextualize and neutralize the worst of these.
Politically-mediated polarization is a process in which unpopular candidates narrowly win elections, anger most voters, and in so doing create conditions for the process to repeat. Another goal is to tilt the information landscape in favor of more widely supported candidates running and winning.
The key to addressing both is making helping who wants what become common knowledge. This post will get into the specifics, but first, consider why it is that generally popular candidates do not run. Where “generally popular” means candidates who can win blowout victories in a general election. In a country of 300 million, such candidates exist, so where are they?
Why generally popular candidates don’t run
Concisely, the electoral mechanics of the US don’t encourage them to run. Consider a toy example where you have an even split of red and blue voters arranged in a line according to where their beliefs fall on a spectrum. We would expect the most viable candidates (represented below as circles) to be positioned towards the middle of that distribution as they attempt to win over enough of the center to secure a majority.
In countries with mandatory and ranked choice voting, you do get this (see: Australia) however, in America we get this:
And when one candidate wins, they are so far ideologically from the voters in the losing party, it pisses them off, and contributes to negative polarization.
Mainly, our closed primary system is to blame. Candidates are forced to enter their primary with a position closer to the fringes of their party, both because of who votes in the primary and how important the support of donors and groups are to the campaign effort. And while candidates do move to the center after the primary, they can only credibly go so far.
Another reason is that candidates need to worry about third parties running in the general election. In this example, the blue candidate moves to the center to capture more voters, but they leave their flank open to a well-run green campaign and lose far blue voters to them, ultimately causing blue to lose in the general election to red.
So what can we do about it?
What about election reform?
Ranked open primaries and ranked-choice voting schemes straightforwardly solve these issues. There’s a catch-22 here because only widely popular candidates support this kind of reform, but they often struggle to get into office in the first place. So we’ll have to bootstrap past this.
Sure, but how?
Imagine you have a perfect oracle who can, before the primaries, know the result of every possible general election matchup. This oracle could then issue statements about each candidate’s chances of winning the general election, even before the primary is held.
For example, consider the oracle says:
🧙 Alice (D) has a 72% of winning the general election. Her worst matchup is Bob (R). 🧙 Bob (R) has a 68% of winning the general election. His worst matchup is Alice (D).
The outcomes of the two primaries can be seen in this 2x2:
The oracle’s predictions mean that at the primary stage, the D primary voters know that they will likely lose the general if they don’t nominate Alice, even if she isn’t their favorite. The same is true for the R voters and Bob. If Alice and Bob are both nominated, instead of a blowout, it will be a close election. The fact that Alice and Bob have blowout potential means they must have been appealing to a wide swath of the electorate, so either one would be a popular moderate and achieve our goal of unwinding political polarization a bit.
A “good enough” oracle
Feasibility rests on understanding how close we must get to a true oracle. I believe we can get the desired effect with a 10 percent margin of error. That is, the system need only identify large discrepancies between possible candidates & platforms. A combination of surveying, preference modeling and electoral simulation can get us there. We’re interested in exposing when there a candidate and/or platform that would threaten a blowout victory, we’re not interested in diving the outcomes of tight races.
To shift the primary outcomes, we must generate common knowledge, which means the predictions must be believed, which means we must have a strong track record. If we can’t call close races, we’ll need to develop it by successfully calling the victories of (supposedly) long shot candidates and other upsets.
Here’s one roadmap:
Demographics layer
This layer holds data made available by the US Census. These are primarily age, sex, race/ethnicity, household composition, and income. The ACS surveys conducted by the office also provide us with information on social, educational, economic, and housing aspects. We will maintain it in the most granular form available, typically at the census block (~1000 people) or tract (~5000 people) level. We will store this data in databases optimized for large-scale statistical analysis (DuckDB or ClickHouse), ensuring that for each region, we have a statistical distribution of each of these traits.
Psychographics layer
Surveys are expensive! One reason is that their answers do not generalize across populations or topics. If you ask one group about property taxes, you can’t infer much about other groups’ opinions, or how the same group feels about adjacent questions about sales taxes. To make it worse, when asking people about questions they have not considered, you need to give them time to learn about and deliberate before their answer is predictive of a vote. For this project to succeed, we need to overcome all of these limitations to get meaningful signal on new political questions rapidly.
We need to discover stable and generative traits of voters which are predictive of their answers across different questions. The exact attributes we track will be the largest research project of our organization. To build out this layer, we must evaluate the psychographics of a few thousand people, ask them political questions, and work out which psychographics are the most predictive of answers. The first schema to try is Haidt’s Moral Foundations Theory, which identified five axes: harm/care, fairness/reciprocity, ingroup/loyalty, authority/respect, and purity/sanctity. Another is Schwartz’s Value Theory, which enumerates two: self-direction, stimulation, hedonism, achievement, power, security, conformity, tradition, benevolence, and universalism.
Once we have a good theory of which psychographic properties predict political stances (perhaps in conjunction with demographics), we then need to determine them at the census block/tract level for everyone in the country. If we’re lucky, these attributes are highly correlated with demographics, and we only need to issue ten thousand surveys to build our model of the country. If we’re unlucky, and demographics and psychographics are rather uncorrelated, we could need as many as 10 million surveys to feel we have a good sketch of the nation. Ultimately, this data is stored in our database alongside the demographic information.
Survey layers
To bridge from demographics and psychometrics to believes, we need survey data. We can import existing surveys, such as the American Community Survey or Pew’s American Trends Panel, but will have to run a substantial amount ourselves so that we can ask for psychographic information. Importing and harmonizing it is no small task, but should yield a rich corpus.
Issue stances layer
Should the US supply Israel with money and weapons? Should we have single-payer healthcare? This layer synthesizes the previous layers to come up with an idea of how each American feels about a given issue. Of course, it wouldn’t have a database of real people, but instead statistical estimates at various demographic cross sections. Such aggregations are enough to general a nation of simulated individuals, which will be useful in electoral modeling.
The primary inputs would be our Survey layer. Fresher data could be brought in through media analysis, where robots read, listen, and tag the opinions of pundits, writers, and other talking heads. This would be necessary identify emerging beliefs before they’ve saturated the public enough to show up in surveys. Over time, doing both kinds of analysis could yield a model of how opinions percolate through the nation.
If there was a website where voters logged in and continually updated their stances (see my postcitizens-lobby) that could also be an input.
A significant challenge of this layer is determining what to track and how to define the contents of a stance. How can we quantify someone’s stance on US-Israel relations? Is a vector of responses to questions with 1-to-5 scale answers good enough? Probably not! And it’s not enough to know someone’s opinion; we need to understand the importance of a stance relative to others.
There is a lot to work out here, still, but building this is a considerable public good in itself.
Election sim layer
This layer brings it all together to predict hypothetical election outcomes before the primaries. We synthesize information from two approaches.
The first approach is to forget specific candidates and come up with a range of possible issue slates a candidate can run on, and then to build an understanding of how each voter would feel towards that slate (negative to positive) by referencing the issue stances layer, and finally to run a simulation using a model of the electoral dynamics of the country to determine the odds each slate would have in each possible general election. The idea is to understand how slates would perform without understanding the charisma of the candidates running on the slate.
The second approach is to predict the candidate effects. Here, we use our in-house surveying tools to understand people’s attitudes towards candidates contextualized by their demographics and psychometrics. This will capture factors such as incumbent effects, the state of the economy, and other cultural trends not accounted for by the slate-based approach. Again, we run a simulation using a model of the country’s electoral dynamics, which includes the propensity for each cluster of voters actually to participate.
Ideally, this org can use this layer to bet in real-money prediction markets, both to raise funds and to generate the track record of prediction required to alter common knowledge. For details on the mechanics and legality, see my post political-prediction.