ARTIFICIAL INTELLIGENCE

“Kalshi is the first regulated U.S. exchange dedicated to trading event contracts, allowing users to buy and sell positions on the outcome of real-world events such as economic indicators, political, weather, and sports outcomes. Regulated by the CFTC, it operates as an exchange rather than a sportsbook, offering, for example ‘Yes’ or ‘No’ contracts.” – Kalshi – Prediction market

Kalshi represents the first fully regulated U.S. exchange dedicated to trading event contracts, enabling users to buy and sell positions on the outcomes of real-world events including economic indicators, political developments, weather patterns, and sports results. Regulated by the Commodity Futures Trading Commission (CFTC), it functions as a true exchange rather than a sportsbook, offering binary ‘Yes’ or ‘No’ contracts priced between 1 cent and 99 cents, where the price mirrors the market’s collective probability assessment of the event occurring.^3,5,7

Unlike traditional sportsbooks where users bet against the house with bookmaker-set odds incorporating a ‘vig’ margin, Kalshi employs a peer-to-peer central limit order book (CLOB) model akin to stock exchanges. Traders place limit or market orders that match based on price and time priority, with supply and demand driving real-time prices; for instance, a ‘Yes’ contract at 30 cents implies a 30% perceived likelihood, paying $1 upon resolution if correct.^2,3,4,5

The platform’s event contracts demand objectively verifiable outcomes, with predefined resolution criteria and data sources to mitigate manipulation. Categories span economics (e.g., Federal Reserve rates, inflation, GDP), finance (e.g., S&P 500 movements), politics, climate, sports, and entertainment, featuring combo markets and leaderboards for enhanced engagement.^4,5,6

Kalshi requires collateral akin to a brokerage, employing portfolio margining to optimise requirements across positions, and pays interest on idle cash. Customer funds reside in segregated, FDIC-insured accounts with futures-style protections, distinguishing it from offshore platforms like Polymarket by providing legal recourse and no need for VPNs or tokens.³

Studies indicate prediction markets like Kalshi often surpass traditional polls in forecasting accuracy, as seen in the 2024 election where its institutional markets tracked macro outcomes closely.³

Key Theorist: Robin Hanson and the Intellectual Foundations of Prediction Markets

Robin Hanson, an economist and futurist, stands as the preeminent theorist behind prediction markets, having formalised their efficacy as superior information aggregation mechanisms. Born in 1959, Hanson earned a PhD in social science from the California Institute of Technology in 1998 after prior degrees in physics and philosophy, blending interdisciplinary insights into his work.

A research associate at the Future of Humanity Institute and professor of economics at George Mason University, Hanson’s seminal contributions include his 1990s advocacy for ‘logarithmic market scoring rules’ (LMSR), a market maker algorithm ensuring liquidity and truthful revelation of beliefs. He popularised the notion of prediction markets as ‘truth serums’ in his 2002 paper ‘Combinatorial Information Market Design’ and book The Age of Em (2016), arguing they harness collective intelligence better than polls or experts by incentivising accurate forecasting through financial stakes.

Hanson’s relationship to platforms like Kalshi stems from his long-standing push for regulated, government-approved prediction markets. In the early 2000s, he proposed the ‘Policy Analysis Market’ (PAM) for the Pentagon to trade on geopolitical events, highlighting their predictive power despite controversy leading to its cancellation. He testified before U.S. Congress on legalising event markets, critiquing bans under the Commodity Futures Modernization Act. Kalshi’s CFTC-regulated model directly realises Hanson’s vision, transforming his theoretical frameworks from academic grey zones into practical, compliant exchanges that democratise forecasting on real-world events.^3,5