# The Base Rate Times, news through prediction markets

**Introduction**
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**I made a news site based on prediction markets**
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The Base Rate Times is a nascent news site that incorporates prediction markets prominently into its coverage.

Please see current iteration: [www.baseratetimes.com](http://www.baseratetimes.com/)

Twitter: [www.twitter.com/base\_rate\_times](http://www.twitter.com/base_rate_times)

**What problem does it solve?**
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**Forecasts are underutilized by the media**
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Prediction markets are more accurate than pundits, yet the media has made limited use of their forecasts. This is a big problem: one of the most rigorous information sources is being omitted from public discourse!

The Base Rate Times creates prediction markets content, substituting for inferior news sources. This improves the epistemics of its audience.

**Forecasts are dispersed, generally inconvenient to consume**
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Prediction markets are dispersed among many different platforms, fragmenting the information forecasters provide. For example, different platforms ask similar questions in different ways. Furthermore, platforms’ UX is orientated towards forecasters, not information consumers. Overall, trying to use prediction markets as ‘news replacement’ is cumbersome.

There is value in aggregating and curating forecasts from various platforms. We need engaging ways of sharing prediction markets’ insights. The Base Rate Times aims to make prediction markets easily digestible to the general public.

**How does it work?**
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**News media (emotive narrative) vs Base Rate Times (actionable odds)**
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For example, this is a real headline from a reputable newspaper: ***“Taiwan braces for China's fury over Pelosi visit”**.* Emotive and incendiary, it does not help you form an accurate model of the situation.

By contrast, The Base Rate Times: ***“China-Taiwan conflict risk 14%, up 2x from 7% after Pelosi visit”**.* That's an actionable insight. It can inform your decision on whether to stay in Taiwan or to flee, for example.

**News aggregation, summarizing prediction markets**
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Naturally, the probabilities in the example above come from prediction markets. The Base Rate Times presents what prediction markets are telling us about news in an engaging way.

Stories that shift market odds are highlighted. And if a seemingly important story doesn’t shift market odds, that also tells you something.

On The Base Rate Times, right now you can see the latest odds on:

*   Putin staying in power
*   Russian territorial gains in Ukraine
*   Escalation risk of NATO involvement
*   and more...

By glancing at a few charts, you can form a more accurate model (in less time) of Russia-Ukraine than reading countless narrative-based news stories.

**Inspiration**
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A key inspiration was Scott Alexander’s [Prediction Market FAQ](https://astralcodexten.substack.com/p/prediction-market-faq):

> *I recently had to read many articles on Elon Musk’s takeover of Twitter, which all repeated that “rumors said” Twitter was about to go down because of his mass firing. Meanwhile, there were several prediction markets on whether this would happen, and they were all around 40%. If some journalist had thought to check the prediction markets and cite them in their article, they could have not only provided more value (a clear percent chance instead of just “there are some rumors saying this”), but also been right when everyone else was wrong.*

Also Scott’s 'Mantic Monday' posts and Zvi’s [blog](https://thezvi.substack.com/).

This [simple chart](https://twitter.com/ClayGraubard/status/1496699988801433602) by @ClayGraubard was another inspiration. Wanted something like this, but for all major news stories. Couldn't find it, so making it myself. (Clay is making geopolitics videos and podcasts now, [check it out](https://twitter.com/GeovaneVideo).)

**Goals**
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**Like 538, but for prediction markets**
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The Base Rate Times is a bet that forecasts can be popularized, as opinion polls have been, and improve society’s models of the world.

Goal: Longshot probability of going mainstream, e.g. like 538.

If highly successful in scaling, we’d be effectively running an experiment on whether prediction can markets serve as early warning systems. For example, if there was a major newspaper consistently reporting a 1 in 3 risk of a global pandemic before COVID-19, would it have made a difference?

**Future**
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**Next topic the site will cover is Artificial Intelligence**
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Launched with coverage of Russia-Ukraine and Nuclear War (with a small sideline on US debt). The plan is to expand into a new ‘vertical’ every month, the topic being decided via [Twitter polls](https://twitter.com/base_rate_times/status/1659188337855578113) (I am a man of the people). Coverage of AI will be launched in June.

**AI-generated ‘enhanced’ article summaries**
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Currently news links are summarized by AI into 3-4 bullet points. Testing ways of enhancing these summaries, can AI…:

*   find historical data to contextualize the story?
*   advise forecast updates based on the story?
*   make counter-arguments?
*   translate articles from e.g. Chinese?
*   detect bias & cross-reference claims?

**LessWrong-style reacts**
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As soon as I saw that ‘Key Insight’ react, I knew I had to steal it! Using LessWrong as an inspiration, the plan is to come up with ~5 reacts that are directly relevant to The Base Rate Times.

**Community Notes-style overlay**
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Eventually I would like a ‘Community Notes’-style overlay on top of all of The Base Rate Times. For example, there might be a more liquid or better suited prediction market that I’ve missed. Or I might’ve grouped together markets in a way that requires more context. Readers could also correct AI-summaries and any headlines I might write.

**Driving traffic to prediction platforms**
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Right now it’s not that convenient to go from The Base Rate Times to one of the referenced prediction markets. Plan is ‘instant check-out’ for placing a trade and easier clickthrough to the platforms.

**Tagging tweets with prediction markets**
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Ever read a tweet (perhaps with a news link) and thought, ‘I bet that just isn’t true?’. Idea is to match dodgy viral tweets to relevant prediction markets.

Community Notes are well suited to adding missing info or making factual corrections. But prediction markets are better at fighting vague verbiage and contextualizing stories with concrete odds.

**Helping top forecasters monetize their skillset**
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Prediction market traders are making a contribution to the public good by helping create accurate odds for current events. Unlike financial market traders, they are not richly compensated for ‘price discovery’ as many of the top platforms are ‘play money’. I have some ideas on how to get e.g. top Metaculus users paid, more on this later…

**Feedback**
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**Still in the MVP stage**
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This is an MVP -- I welcome any and every bit of feedback, big or small. Please feel free to be critical.

What's one thing you would change about the website? 

Some specific areas you might like to comment on:

*   general website layout (should I change to 2 columns?)
*   quality of headlines
*   chart design (should I replace data labels with a Y-axis?)
*   link selection and presentation
*   quality of the AI-generated article summaries

**Grant suggestions please!**
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I would also appreciate any suggestions on grants (or other funding) to apply for.

Thank you for reading! If you made it this far, then damn u is gangsta af.