PAPER PLAINE

Prediction markets are increasingly used for real-world forecasting on politics, business, and science, but they only work if prices reflect genuine information rather than insider knowledge or manipulation. The framework here—demonstrated against a real DOJ indictment of a military officer who traded on nonpublic Venezuela intelligence—gives regulators and platform operators a practical toolkit to detect and stop informed traders before they undermine market integrity.

Businesses currently wait weeks to settle payments because trade credit sits outside official clearing systems. By tapping this hidden liquidity, companies could access cash faster and cut the working capital they need to tie up. This could be especially powerful for small suppliers and developing economies where informal credit chains are most common and access to capital is most constrained.

Most AI forecasting systems today are evaluated on static datasets or by their trading profits, both of which hide whether an AI actually has predictive skill or just got lucky with market timing and position sizing. This benchmark lets anyone trustlessly evaluate AI forecasting agents on real prediction markets with proper statistical incentives, cutting through the noise to identify which systems genuinely see the future more clearly than crowds do. For AI companies and traders, it's a way to separate signal from noise; for the broader AI safety community, it's a model for building evaluations resistant to overfitting and centralized gaming.

Banks must set aside capital reserves for potential losses, and stress-testing requirements force them to model worst-case scenarios. A better prediction method could help regulators and banks estimate required reserves more accurately, avoiding either dangerously low buffers or wasteful overprovision. This affects lending capacity and ultimately how much credit flows to the real economy.

Proof-of-Stake cryptocurrencies like Ethereum were designed to be more democratic than older mining-based systems, but this research suggests the opposite happens at scale: wealth and control concentrate in fewer hands. If true, it undermines a core promise of these networks—that ordinary people can participate meaningfully in securing and governing them.

Options traders and risk managers calculate implied volatility thousands of times per day—it's how they price contracts and manage portfolios. Replacing slow iterative methods with a direct calculation could speed up trading systems, reduce computational costs, and lower latency in high-frequency markets where milliseconds matter. The breakthrough also settles a mathematical question that has persisted since the Black-Scholes model became standard in 1973.

Prediction markets are growing as a tool for forecasting everything from elections to climate outcomes, but we know almost nothing about how they actually work. This research documents Polymarket's plumbing in detail—revealing where the standard playbook from stock markets fails and where it holds. For anyone building a competing platform, trading on these markets, or relying on their price signals for real decisions, knowing what data you can actually trust matters enormously.

Traders and risk managers currently juggle two different clocks—one for actual trades and one for theoretical pricing—and this mismatch can hide real risks, especially during fast trading or market stress. Recognizing that market time is fundamentally non-unique doesn't break existing tools, but it explains why they sometimes fail at high frequencies and suggests when simpler, lower-frequency models might be more reliable for managing money and hedging positions.