Earlier this summer, I sat barefoot in Berkeley with a dozen aspiring “geopolitical traders,” learning how to make money from war. We were at a conference billed as a “festival for predictions, and markets thereof” – that is, a celebration of prediction markets. The venue, a hippie-ish compound, mostly forbade shoes indoors. Our instructor, Mike, claimed to have grown his money eightfold on Polymarket, mostly by trading on politics. Now he would share his wisdom.

Prediction markets were legalized in the United States in 2020 on the premise that they would “allow us, more often than not, to predict the future.” That, at least, is what Kalshi’s marketing copy claims. Reportedly valued at $40 billion, the firm runs the first exchange of its kind to win US regulatory approval. But Mike soon made clear that our predictive abilities had little to do with our ability to succeed on Kalshi or Polymarket.

Mike had a theory. “It would be in the interest of the US government to actively interfere in the information space to distort market prices,” he told us. Donald Trump, he noted, had repeatedly – and baselessly –promised an imminent deal to end the war in Iran, a claim prediction markets had largely taken at face value. The volatility now rippling through these markets, he suggested, might well be “actively planned and executed by the US government.” When the President is “abusing his position of trust,” what should a prediction-market trader do? “We can adapt,” Mike said, if we “embrace the slop.”

The markets are helping officials to quantify how effective they are at polluting the information space

The markets are helping officials to quantify how effective they are at polluting the information space

Our forecasts are getting worse, he explained, “because the input we put into the forecast is also lower quality.” Which is to say: we should become “swing traders,” putting money on Trump’s every word no matter what we actually believe the outcome will be. I asked Mike whether “embracing the slop” defeated the social purpose of prediction markets – namely, generating reliable forecasts. He suggested they might soon find a new use: quantifying, for officials, how effectively their statements have polluted the information space.

Early last month, when moderate liberal David Crowley upset Democratic Socialist Francesca Hong in Wisconsin’s Democratic gubernatorial primary – an outcome Kalshi had priced at just 4.6 percent – I thought back to my exchange with Mike. That night, the markets had clearly gone haywire. After polls closed, Hong’s odds on Kalshi plummeted to roughly one in five, then spiked back to about 50 percent, briefly retaking the lead. Over the course of the evening, Hong’s and Crowley’s odds flipped nine times, five of them in a single 20-minute burst. Why might that be? Well, to borrow Mike’s phrase, users were trading the slop.

Traders “did not have the full read of what was going on when they were making those trades,” Lakshya Jain, the head of political........

© The Spectator