Humans have always tried to predict the future. From ancient Greek Oracles to modern weather predictions, people are desperate for a glimpse into what’s coming next to help them make better decisions and reduce risk.
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Out of this psychological desire, something called prediction markets have been created over the last 50 years. These are marketplaces where people can buy or sell contracts which bet on the outcome of future events. The price of a contract is correlated with the market’s collective belief about the likelihood of an event or result taking place.
While the ancient Greeks might have scored their predictions on stone tablets, blockchain and crypto technology are now inscribing decentralized prediction markets in digital ledgers.
For markets to run accurately, they need Oracles. These are systems that bring real-world data onto the blockchain. This occurs by bridging this external data into smart contracts, often requiring verification or consensus mechanisms to ensure accuracy.
In these markets, there is something known as “truth,” which is the ultimately recognized outcome of an event. Then there’s “uncertainty,” which is what the market price represents.
Prediction markets aren’t just about gambling. They’re living experiments to understand how societies agree on information to predict the future. They’re using collective knowledge to test the success and failure of solving uncertainty in real time.
Prediction markets, also known as information markets, first sprung up in the 1980s. An early example was the Iowa Electronic Market. It lets users bet on political outcomes and often outperform polls. Although not run using blockchain, these pioneering markets are the precursor to what has now been built in a decentralized digital format.
Prediction markets work by enabling participants to buy shares in an outcome. For instance, an individual can buy a share of an outcome on a specific candidate to win an election or a particular sports team to win a tournament.
If the event happens as you predict, the share pays out. If not, it becomes worthless. You win nothing. The prices of these shares move dynamically based on the buying and selling action of the market.
The beauty of decentralized blockchain technology prediction markets is that anyone, anywhere in the world, can participate. It opens up the doors much wider for people to bet on anything they want, rather than just what a bookmaker might decide.
While there are lower barriers to entry, some jurisdictions still have regulatory requirements like know your customer (KYC) to access the required cryptocurrencies. Legislatory burden created issues in some countries, including the U.S where Polymarket has restricted access for US users at times.
Examples of blockchain prediction platforms include:
Naturally, participation is motivated by potential profits, which encourages predictors to seek out the best information and work to make the most accurate predictions possible. The result is something called the ‘wisdom of the crowd’ effect.
Hundreds, thousands, or millions of people seeking out the most up-to-date, accurate information helps to provide a better prediction of outcomes than a single highly qualified expert.
Over the years, studies have found that prediction markets can outperform 74% of professional economic forecasters. Plus, they’ve correctly predicted outcomes such as the 2024 US presidential election. While many media outlets and experts had it as a 50-50 race, prediction markets weighed heavily in favor of Donald Trump, which was the correct outcome.
In blockchain, an Oracle is a third-party service that feeds external data and information into smart contracts.
Without the help of Oracles, a blockchain can’t know what happens in the real world and therefore wouldn’t be able to execute smart contracts based on these real-world events. So an Oracle is crucial in determining who wins and who loses in a prediction market.
This presents the issue that if data is wrong or delayed, payouts can become incorrect, and trust in a platform can be completely lost.
Within the industry, there are two main types of Oracles:
Over the years, Oracles and prediction markets have been heavily stress-tested.
In 2020, an attacker manipulated the BZX protocol price feeds, leading to a loss of 8 million dollars during the Oracle attack.
Disputed results can hinder Oracle’s truth, which causes delays in confirming events. For example, the 2020 US election was disputed and it raised a case for how blockchain prediction markets deal with this scenario.
Polymarkets reliance on data supplied to Oracles from media outlets leads to discussions about how the truth is ultimately determined. Different media outlets use projections to call a race and deliver a certain outcome. When elections are disputed, this can cause uncertainty and unresolved markets for traders, even dragging on for months until a resolution is finally confirmed on inauguration day.
Ambiguity is also a big problem. If an event is hard to clearly define or a result is unclear in data points. For example, what counts as the ‘end of the COVID-19 pandemic’, it’s not possible for Oracles to resolve cleanly.
Overall, Oracles are integral to prediction markets, but it’s critical that they continue to be stress-tested and adapted. Their importance can also be seen as a weak link in decentralized systems. Without trust, speed, and pinpoint accurate data feeds, prediction markets won’t be able to function reliably.
Agreeing on the truth exposes the complexities of defining and agreeing on truth in a world where facts can be disputed, delayed, distorted, or manipulated.
In prediction markets, the truth is whatever the Oracle or confirmation mechanism declares as the outcome. Sports scores are straightforward and hard to dispute. However, political events with deeply contested results are less straightforward to establish in a timely manner.
This raises the question: When is the truth officially determined?
For these markets to work, they have to rely on hard data. A subjective truth is hard to arrive at with differences in interpretation and consensus.
Decentralization can help with this through community governance and resolution. It can allow markets and users to vote on outcomes and decide the truth. But again, this can lead to subjective outcomes or manipulation in a community. Platforms like Augur use a staking and dispute resolution system using their REP governance token. This allows users to report outcomes and challenge incorrect reports with dispute voting rounds.
To combat this problem, prediction markets can declare a market invalid. This results in all bets being refunded, but it’s a frustrating experience for users. You can imagine being particularly annoyed if, in your subjective view, your prediction was right. But because there’s no hard data confirming your perceived view of a market win, your initial bet is just refunded.
Predicting the future revolves around uncertainty. While the outcome is based on truth, the prediction is equally reliant on uncertainty. It’s the only reason prediction markets exist.
Their core function is to price uncertainty; to give a literal financial price on the perceived likelihood of a real-world event occurring. It turns things that are unknown into a tradable asset.
To aggregate beliefs around uncertainty, every single participant arrives with their own interpretation of information and incentives. Then the market price reflects the overall crowd’s probability forecast.
When it comes to uncertainty, its accuracy does have its limits:
US elections have always been a hot topic in prediction markets. In 2016, prediction and betting markets like Ladbrokes and Betfair put Hillary Clinton at a 70-85% chance of winning but she didn’t win. This is clear proof that even crowds can be massively wrong when uncertainty levels are high or information is unclear or incomplete.
This shows that while prediction markets can help us put a price and clearer prediction on uncertainty, they do have limits. Plus, it highlights the requirement for robust systems and stress testing to handle things like manipulation, attacks, and unforeseen events.
Watching how prediction markets handle stress delivers insight into improving them. It delivers insights into wider challenges faced in the digital economy: how the world agrees on facts, manages risk, and deals with ambiguity.
Creating robust, trustworthy Oracles is a massive step in this process.
Projects like Chainlink, Witnex and UMA are exploring ways to use AI and machine learning to aggregate and verify data from multiple sources. These efforts could work towards reducing manipulation risk and increase the reliability of data being fed into decentralized systems.
The beauty of decentralization also allows us to layer in community voting and governance. This way, crowd consensus can be used for arbitration or third-party verification rather than small panels or single experts to arrive at conclusions for disputed outcomes.
Integration with the Internet of Things (IoT) could be helpful to feed hard data into Oracles to determine the outcomes of smart contracts. It offers the potential to include sensor-driven data for weather. And now with the inclusion of much more automated technology in sports, data from sensors can provide instant data for predicting the outcome of sports events.
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