Welcome to Web 3 with A6 and Z. I'm Sonal Choxi, and today's episode is about a hot topic right now, prediction markets. What are they specifically? How do they work? How to design them? And much more. This is your definitive explainer because we go deep with two top experts. Alex Tfarik, professor of economics at George Mason University and the first voice you'll hear, and Scott Commoner's A6NCryptor Research Partner and Harvard Business School professor. While we cover how prediction markets came back into the conversation with last year's election, which is when we first recorded this, the discussion is very relevant today as we go into what's hype versus reality on claims people make about prediction

markets and what they're good for. How do they fit in with other trends like AI, futarchy,

the crisis in scientific publishing, making business decisions, and more. And then about halfway

through, we talk about desired futures for prediction markets and underlying technologies,

including where blockchains and crypto come in. As a reminder,

none of the following should be taken as business investment legal or tax advice. Please see a6.com

slash disclosures for more important information. So I think a prediction market is a very simple idea.

The bottom line is that we're interested in forecasting. Lots of people are interested in forecasting things.

And prediction markets are some of the best methods of forecasting, which we have yet created.

They tend to be better than complicated statistical models. They tend to be better than polls, or at least as good.

And there's one reason for that.

And making bets on the prediction market, I push the prediction market closer to the truth.

And actually, you know, sort of that is an illustration of why we think of these things as information aggregation mechanisms, right?

What are they really doing?

They're aggregating information from all of the people in the market, right? And so if many

different people are out there doing their own private forecasts and like calibrating their own

models, they all have their own estimates, which they trust with some degree of confidence,

they come together, right? They're all buying or selling the prediction asset based on what their model

leads them to believe. And so as a result, the asset is sort of like aggregating all of this

information. It's price discovery, just like we think about in financial markets and commodities

markets. Like everybody's demand together discovers the price at which the market clears.

And here, because what the value of the asset is depends on probability, right?

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