A different kind of newsletter.
Get crowdsourced economic forecasts in your inbox.
Frequently Asked Questions
- How does it work?
- Why forecasting?
- Is there money involved?
- Who's behind it?
- What's with the name?
- How can I help?
- How is Nonrival different from other forecasting platforms?
- How can I learn more about forecasting?
- How do I get better at forecasting?
- Are you a "superforecaster"?
- How do you pick topics?
- How does the scoring work?
- I have a question you didn't answer
How does it work?
Nonrival isn’t a typical newsletter: It’s a forecasting tournament in your inbox. As a reader, you’ll learn about a topic, make a prediction, learn from others, and improve your thinking over time.
Every week you’ll get a newsletter exploring a key question about the economy or tech—How many jobs will go remote? How much venture capital will be invested next quarter? Will there be a recession this year? The newsletter provides key information and a variety of perspectives on the topic—and then readers make a prediction.
Later in the week you get a follow-up email showing how your forecast compares to the group and highlighting the most thoughtful reasons readers gave for their predictions.
Accurate predictions are really useful. They're an essential part of good decision-making. Should you bring an umbrella to work? It depends on the likelihood of rain. Should funding for the NIH be higher? It depends on what effects we think that will have. Is an investment opportunity worth it? It depends on how we expect it to turn out. Not everything important can be predicted, but predictions are an inescapable part of knowledge.
Crowd forecasting works. Combining the forecasts of a group of people can produce more accurate predictions than many individual experts. By combining our individual judgments, we can produce more accurate information than any one of us could produce on our own.
Forecasting improves your thinking. There's no better cure for overconfidence than making concrete predictions and seeing how they pan out. Forecasting helps develop good mental habits and improves reasoning skills. That’s not to say that everything about thinking well boils down to accurate forecasting–far from it. But if you want to get better at developing accurate beliefs about the empirical world, forecasting is a good way to put your thinking to the test and improve your reasoning skills over time.
Is there money involved?
There are some prediction “markets” online that allow real money to be traded–to learn more about how this works, try this Bloomberg profile of a new prediction market called Kalshi.
But, generally, you don’t need financial incentives to make a forecasting platform useful. Research suggests that forecasting “tournaments” without real money can still produce accurate forecasts–often quite close in accuracy to those of markets, though there’s plenty of debate about which format is most accurate.
Nonrival isn’t for trading or making money. It’s for people motivated to improve their reasoning. No money is involved.
The newsletter is also completely free! As of now, there are no subscription tiers or anything like that.
Who's behind it?
My name is Walter Frick and I’m the founder and editor of Nonrival. I’m also currently a Contributing Editor at Harvard Business Review and a Pro Forecaster at INFER.
I started Nonrival because I’ve always been interested in using digital media to encourage deeper deliberation and more accurate beliefs about the world. Forecasting is one way to do those things, though far from the only way.
Get in touch with me by sending me an email.
You can read more about my professional background below or on LinkedIn.
What's with the name?
In economics, most goods are considered “rivalrous” meaning that if you consume the good I can’t consume it, too. If you have an apple, either you can eat that apple or I can eat it or we can split it. We can’t both eat the same apple.
But some goods don’t have that property. If you teach me a recipe for apple pie, we can both go home and use that recipe at the same time. Goods like recipes are “non-rivalrous” or “nonrival.”
Information is a nonrival good, and so “Nonrival” seemed like a fitting name for this project. First, it’s all about combining the information of a group of people. Second, the topics that the newsletter covers are mostly about the ways information technology is changing the economy. Hence, Nonrival
How can I help?
Sign up for the newsletter and encourage your friends to sign up, too! Sharing it with one friend is great. If you really want to help, send it to 10 thoughtful friends you think might like it. That’s the biggest thing you can do to support Nonrival right now. It’s free, and the more people who sign up the easier it will be to sustain the project.
For now Nonrival is a passion project, but if you are interested in becoming a sponsor, an investor, an advisor, or in some other way contributing to the project’s success please get in touch!
How’s Nonrival different from other forecasting platforms?
There are lots of great forecasting platforms out there, some of which I have participated in. You should try them! (Recommendations in the next section.)
With Nonrival, I wanted to create something that was incredibly easy to use, and that felt more like a publication than a platform. As an editor and a journalist, I wanted to make forecasting part of a weekly news reading habit. That’s why it’s half email newsletter, half forecasting tournament.
By providing more background information and by only letting readers make a prediction at one point in time, I’m trying to cut down the time and effort it takes for readers to get started with forecasting. That’s also why I ask for predictions about the average reader forecast: The fact that it takes months or even years for a forecast to resolve can be off-putting if you’re just getting started. Scoring those group-average predictions is an easy way to give feedback right away.
There are probably tradeoffs to all these choices. I’m optimizing for ease of use, whereas other platforms may be optimizing more fully for accuracy. Nonrival will be a success if it introduces lots of new people to forecasting. I’m hopeful that it can produce useful, relatively accurate forecasts, too.
How can I learn more about forecasting?
If you want to keep up with all things forecasting, try this newsletter. Here’s a long list of research on the topic of crowd forecasting. The book Superforecasting by Phil Tetlock and Dan Gardner is also a very accessible starting point. A magazine-length version of similar material can be found here.
How do I get better at forecasting?
Are you a “superforecaster”?
No. The idea here is definitely not that I’m personally some great forecaster. If you want to read newsletters by exceptionally accurate forecasters, try this one or this one. You can read more about my forecasting track record here, but I don’t see that as what I’m bringing to this project. I’ve written about forecasting in the past. You can find that coverage here, here, here, here, and here.
How do you pick topics?
I try to focus Nonrival on the economy and especially how digital technology is changing it. The rise of remote work, the fate of platform businesses, the future of venture capital, that sort of thing. Broadly, think anything The Economist would cover. More narrowly, I think of it a bit like if the newsletter Stratechery had a prediction tournament built in.
Why those topics, instead of elections or pop culture or science? Partly I’m trying to play to my strengths: I’ve covered the intersection of economics, business, and tech for a decade. Partly it seems like a gap: There are lots of forecasting platforms focused on politics and geopolitics and on technical progress in areas like AI.
How does the scoring work?
I’ll update this in more detail as the project goes on. For now, all questions are binary: Something either happens or it doesn’t. And scoring is a simple Brier score: The difference between a forecast (example: 0.3) and the outcome (0 or 1), squared. Lower is better. Nonrival translates scores into percentiles wherever possible because they’re easier to interpret.
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