# What Are Prediction Markets? Prediction markets are platforms where people trade on the probability of a real-world event happening. Each market asks a yes-or-no question, and the price reflects what traders believe the odds are. ### Origin Prediction markets began with election betting in the United States. In the late 1980s, the Iowa Electronic Markets let people trade on presidential races. Over time, the idea expanded beyond politics into areas like sports, finance, and culture. ### In the Media News outlets such as *The Wall Street Journal*, *Bloomberg*, and *CNBC* regularly cite prediction market odds in their coverage. Polymarket called the presidential election hours before traditional media. Kalshi and Polymarket are the leading platforms, with millions of users and billions of dollars traded. ### Why Use Prediction Markets? Prediction markets offer benefits that traditional polls or news sources cannot: * **As a News Source:** Markets provide real-time odds, turning vague headlines into clear probabilities. * **Forecasting Ahead:** Prices can signal outcomes before mainstream media reports on them. * **Collective Intelligence:** Market prices reflect the combined knowledge of many people. * **Targeted Events:** Traders can focus on specific outcomes without being exposed to unrelated risks. ### Predictify Everything Prediction markets started with election betting and expanded into sports, climate, culture, and finance. They are a new asset class: contracts that let people trade directly on real-world events. The end goal is to let people bet on anything. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://polymarketguide.gitbook.io/polymarketguide/basics/what-are-prediction-markets.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.