Alex Taborrak writes:
The prediction markets predicted the election outcome more accurately and more quickly than polls or other forecasting methods, just as expected from decades of research. In this election, however, many people discounted the prediction markets because of large trades on Polymarket.
He quotes Paul Krugman:
Never mind the prediction markets, which are thin and easily manipulated.
But Taborrak points out:
The idea seems to be that whales shifted market odds from 50:50 to 40:60, hoping this would drive more people to vote for Trump. Really? Were voters in Pennsylvania watching Polymarket to decide who to vote for? In a decision market, manipulation might be desirable to a whale (albeit unlikely to succeed), but in prediction markets, this scenario seems dubious: a) people would need to know about these markets, b) they’d need to care about probability shifts on these markets (as opposed to voting say the way their family and neighbors were voting), and c) this would have to be an effective way to spend money to influence votes compared to the myriad other ways of influencing voting. Each step seems dubious.
Very dubious.
But here is the interesting part:
The mystery trader known as the “Trump whale” is set to reap almost $50 million in profit after running the table on a series of bold bets tied to the presidential election. …
Polls failed to account for the “shy Trump voter effect,” Théo said. Either Trump backers were reluctant to tell pollsters that they supported the former president, or they didn’t want to participate in polls, Théo wrote.
To solve this problem, Théo argued that pollsters should use what are known as neighbor polls that ask respondents which candidates they expect their neighbors to support. The idea is that people might not want to reveal their own preferences, but will indirectly reveal them when asked to guess who their neighbors plan to vote for.
…In an email, he told the Journal that he had commissioned his own surveys to measure the neighbor effect, using a major pollster whom he declined to name. The results, he wrote, “were mind blowing to the favor of Trump!”
Théo declined to share those surveys, saying his agreement with the pollster required him to keep the results private. But he argued that U.S. pollsters should use the neighbor method in future surveys to avoid another embarrassing miss.
I’ve had a few people ask me about the value of neighbour surveys. They can hold value if there is a reason to believe people are shy to say whom they actually support. But they have a real flaw.
Let’s say the country is 51.5% Trump and 48.5% Harris. But 30% of the population live in heavily blue areas (80% Harris) and 70% live in purple to red areas (65% Trump).
The 30% in heavily blue areas would almost all say their neighbours are boring Harris (even if they are Trump voters) and the 70% in purple to red areas would say a mixture. Basically neighbour polls don’t take account of how heavily a neighbour leans.
In NZ I doubt they would work because I think we have far less idea how our neighbours would vote. But would be an interesting question to ask at some stage, and compare to the normal results.
The post A whale on a prediction market first appeared on Kiwiblog.
