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“Prediction Markets in a Polarized Society”

Rajiv Sethi writes about some weird things in election prediction markets, such as Donald Trump being given a one-in-eight chance of being the election winner . . . weeks after he’d lost the election.

Sethi writes:

There’s a position size limit of $850 dollars per contract in this market, which also happens to have hit a limit on the total number of participants. But dozens of other contracts are available that reference essentially the same outcome, and offer about the same prices. . . .

What prediction markets are revealing to us today is the extent of the chasm in beliefs held by Americans. . . . People actively seek information that largely confirms their existing beliefs, and social media platforms accommodate and intensify this demand. Prediction markets play an interesting and usual role in this environment. They encourage people with opposing worldviews to interact with each other in anonymous, credible, and non-violent ways. In a sense, they are the opposite of echo chambers. A market with homogeneous beliefs would have no trading volume, or would attract those with different opinions who are drawn by what they perceive to be mispriced contracts.

While most online platforms facilitate and deepen ideological segregation, prediction markets do exactly the opposite. They provide monetary reinforcement to those who get it right, and force others to question their assumptions and predispositions. While best known as mechanisms for generating forecasts through the wisdom of crowds, they also bring opposing worldviews into direct and consequential contact with each other. This is a useful function in an increasingly segregated digital ecosystem.

I dunno, seems kind of optimistic to me! We discuss prediction markets in section 2.6 of this article but come to no firm conclusions.


  1. Russell says:

    I made a lot of money doing these bets this year. There were a lot of highly correlated bets, such as whether Trump would be president, rather Trump’s cabinet would have their jobs, etc. It really felt like stealing money from partisans. I learned some of the tricks to make even more money next time (exploiting quirks in the system), so I hope it continues to be a trend.

  2. Dylan O'Connell says:

    For the curious, I’d highly recommend Vitalik Buterin’s write-up of his experience betting against Trump in crypto prediction markets (post-election). . It’s the perspective of a guy who *did* agree this was an inefficiency , and made quite a bit of money off it. But it does a good job of wading into some of the messy details, that help explain why it’s not *quite* as large of an inefficiency as you might think. (TLDR: while PredictIt has such severe limits, it can’t really be called anything like an efficient market, people cite the high volume crypto markets with similar price. But even for those, while there’s a lot of money to be made, the technical details make this all a bit messier than you’d think from the outside).

    • Ben says:

      That was a wild read.

      > A CDP is how all DAI is generated: users deposit their ETH into a smart contract, and are allowed to withdraw an amount of newly-generated DAI up to 2/3 of the value of ETH that they put in. They can get their ETH back by sending back the same amount of DAI that…

      What even

  3. Ben says:

    > They provide monetary reinforcement to those who get it right, and force others to question their assumptions and predispositions.

    Alternate post title, “Puts and Refutations”, by Imre Lacashtos

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