Prediction markets are financial markets where people can bet on the outcomes of future events. They harness the “wisdom of crowds” to generate probabilistic forecasts about everything from elections to economic indicators.

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History of Prediction Markets

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Academic Origins

The concept of prediction markets emerged in the 1980s, but it wasn’t until the University of Iowa launched the Iowa Electronic Markets (IEM) in 1988 that real-money trading began. IEM was designed as a research tool to test the hypothesis that market prices could outpace traditional polls in forecasting election results. This early foray proved remarkably accurate, paving the way for deeper academic interest.

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From Research to Regulation

In the 2000s, economists like Robin Hanson popularized the idea of “ideas futures,” proposing formal market scoring rules to guarantee liquidity. Hanson’s Logarithmic Market Scoring Rule (LMSR) became the de facto mechanism for many academic and hobbyist markets. Over the same period, organizations such as PredictIt in the U.S. navigated regulatory no-action letters from the Commodity Futures Trading Commission, demonstrating that small-scale prediction markets could operate legally.

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Key Milestones

  • 2000s: Launch of PredictIt, IEM expansion, and the first corporate internal markets (e.g., Microsoft’s use of internal predictive markets for project forecasting)
  • 2014-2016: Ethereum’s emergence enabled decentralized markets like Augur and Gnosis’s Omen, democratizing access via smart contracts
  • 2021-2024: The rise of Base-chain platforms brought 0DTE (zero-days-to-expiry) mechanics and seamless USDC settlement, exemplified by Limitless Exchange

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How They Work

  1. Market Creation: A question is posed (e.g., “Will it rain tomorrow?”)
  2. Trading: People buy and sell shares representing different outcomes
  3. Price Discovery: Share prices reflect the crowd’s collective belief about probability
  4. Resolution: When the event occurs, winning shares pay out
  • Polymarket: The largest crypto-based prediction market
  • Kalshi: CFTC-regulated prediction market for US users

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Why They Matter

Prediction markets often outperform traditional forecasting methods because:
  • Financial Incentives: Real money encourages careful analysis
  • Aggregated Wisdom: Many perspectives combined
  • Real-Time Updates: Prices adjust as new information emerges
  • Track Record: Historical accuracy can be measured and verified

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The Challenge

While prediction markets are powerful, making good predictions requires:
  • Deep research into relevant factors
  • Understanding of market dynamics
  • Analysis of multiple data sources
  • Synthesis of complex information
This is where BetterAI comes in - automating the research and analysis process with AI.