A prediction market is a system for people to make predictions about future events. It’s similar to an auction, but instead of selling pieces of property, it’s selling “bets” on the outcome of future events.
A prediction market is most commonly used in finance and investment research, but many industries now have their own prediction markets such as political polls and financial markets.
In this blog post we will explain what a prediction market is, how they work in Web3, and some examples of using them in web applications.
What is a Prediction Market?
A prediction market is a system for people to make predictions about future events. It’s similar to an auction, but instead of selling pieces of property, it’s selling “bets” on the outcome of future events.
Like with any other business venture, you can set a price for your item, and if someone is willing to pay that price, you can sell it to them. The buyer can then decide whether or not to take the bet and make a profit from it.
How Prediction Markets are Being Used in Web3?
The idea of a prediction market is to create a system where you can sell your thoughts and predictions about future events, with the buyers buying these “bets” and placing a wager if they think the outcome will be positive.
The first step is to create the market. After that, teams of data scientists and economists use artificial intelligence to study past events, and make predictions about the future, using the data and data-driven decision-making of humans.
Pros of Prediction Markets
More transparency – With prediction markets, you can place more confidence in your predictions because the data will be open for everyone to see. A market with only your thoughts and no data to analyze can be very dangerous because you may end up being wrong about everything.
More liquid – A market where people put money where their mouths are can be very volatile, causing price speculation and selling before buying again. This is not the case with prediction markets where users put money into the market and then have it automatically exchanged for a certain outcome.
More actionable – With prediction markets, you can get more actionable insight into customer behavior and what they want. With a lot of data and analytics behind it, you can see exactly where a user is willing to bet, and then offer a specific outcome to attract their attention.
More democratized – With prediction markets, you can get a more in-depth look into the issues and solutions that people think about. This can be very helpful when working with communities that might be very polarizing such as in politics or religion.
Cons of Prediction Markets
More work for the data scientists – Prediction markets are very data-driven, and require a lot of analysis and machine learning. This extra work can be very time-consuming and expensive, especially if you are working with a smaller market or only a few events.
More work for the clients – It can be very challenging for businesses to continuously scale up their research and investment based on the results of their prediction markets. At the same time, these markets are very transparent and you can see exactly how much money is changing hands, which can make it hard for clients to get the full value out of their investment.
Rising regulatory concerns – Since most of the trading takes place on decentralized platforms with little control or oversight, there is a lot of risk and volatility with the market. The price of an investment may rise or fall a lot depending on what happens with the project, and therefore there is also the risk that people will lose money.
Wrapping Up
The goal of this blog post was to provide a basic introduction to prediction markets and how they work in the Web3 ecosystem. With prediction markets, you can get more actionable insight into customer behavior and what they want.
With a lot of data and analytics behind it, you can see exactly where a user is willing to bet, and then offer a specific outcome to attract their attention.
We hope that this post provided you a basic understanding of what a prediction market is, how they work in Web3, and some examples of using them in web applications.