The decentralized betting market platform Polymarket left political commentators around the world stunned by its accuracy in sourcing information about the US presidential election race between President-elect Donald Trump and Vice President Kamala Harris.
Now several commentators are backing the idea that prediction markets and the concept of “information finance” could be the next leap forward in humanity’s ability to make more efficient and educated guesses about the outcome of global events.
Vitalik Buterin outlines the future of “Information Fi”
In a Nov. 9 blog post, Ethereum co-founder Vitalik Buterin outlined the concept of information finance, or “Info Fi” for short, betting on the fact that betting markets are actually much more than just speculation engines.
“Predicting the election is just the first app. The broader concept is that you can use finance as a way to align incentives in order to provide viewers with valuable information.”
Additionally, Buterin says increasingly powerful artificial intelligence tools will “turbocharge” the realm of info finance within the next ten years.
Prediction markets make information sourcing more efficient. Source: Vitalik Buterin.
He says most of the interesting applications of info finance are concerned with “micro questions” — millions of tiny markets that own their own, have low real-world consequences but when combined together en masse, have massive repercussions on the world.
One of the big problems with the efficiency of human betting markets is that they require high volumes to function, as sophisticated human actors will only spend their time undertaking detailed analyses if it’s a potentially profitable endeavor.
“AI changes that equation completely and means that we could potentially get reasonably high-quality info elicited even on markets with $10 of volume. Even if subsidies are required, the size of the subsidy per question becomes extremely affordable,” he wrote.
Betting markets have outperformed for almost 40 years
While betting markers may have witnessed a high degree of success in predicting the outcome of the 2024 election, this is far from the first time that betting markets have captured the attention of pollsters and political pundits for their accuracy.
In the 1988 presidential election, the Iowa Electronic Market out of the University of Iowa, startled pollsters when it began outperforming polling and sizing in predictions of the election race between George Bush and Mike Dukakis. It continued to have an edge on polls and traditional reporting for several elections following.
Some theorize that these markets’ superiority in predicting the outcome of significant political and world events comes back to a mix of two key ideas: the “wisdom of crowds” theory and the efficient market hypothesis (EMH).
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The wisdom of crowds theory posits that groups of people are much smarter than any one particular member of the group. Meanwhile, EMH says the market is a giant information processing tool, which vacuums up the facts, opinions and emotions of millions of investors, in the form of buy and sell orders — and condenses it into a single output: price.
When these two principles are combined, larger (smarter) groups of people engage in sophisticated market behavior that incentivizes selecting the correct scenario in a given set of possible outcomes, the theory goes that prediction markets could make future unknowns a bit more clear.
Wisdom of crowds and efficient markets
Nimrod Cohen, head of product and research at eOracle, told Cointelegraph that the real power of prediction markets isn’t just about predicting outcomes, it’s their ability to serve as a “high-speed reporting mechanism.”
“When you have substantial market volume, you get two crucial benefits: extensive coverage and instant reaction times,” he said.
“Traditional reporting faces an inherent challenge: you typically face a trade-off between coverage and 'finality' of reaction. A quick Twitter post might be fast but unreliable, while comprehensive analysis from major news outlets takes time.”
“Prediction markets uniquely solve this by providing both — immediate price movements reflect breaking news, while the financial stakes ensure accuracy.”
“While Polymarket’s election outcome might seem ‘obvious’ in hindsight, what’s more valuable is how the market responded to each development throughout the campaign — prices adjusted in real-time to new information, providing a continuous, financially-backed assessment of the situation.
“That’s an efficiency you don’t get from traditional polling or news coverage.”
To Cohen, the major stumbling block for prediction markets has less to do with their accuracy and more to do with what’s known as “resolution ambiguity.”
Cohen says anyone who’s ever made a bet among their close friends knows how tricky resolving the outcome of a wager can be, with even seemingly straightforward bets leading to heated disputes over the exact terms and conditions.
So solving for this, and defining “precise resolution conditions” with 100% certainty is the most important step forward when it comes to the efficacy of prediction markets.
Cohen puts forward a number of potential dispute scenarios: “In sports events, how should weather cancellations be handled? In markets about company acquisitions, does a letter of intent count or is it only a closed deal?”
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Answering these questions is at the forefront of solving for prediction market reliability in a longer time frame says Cohen.
“We need a system that can handle nuanced disputes while maintaining the market’s integrity and user confidence.”
“This is exactly why we’re seeing prediction market platforms invest heavily in developing these resolution frameworks. The technology for trading is mature but it’s the dispute resolution layer that remains the critical missing piece.”
