Agentic Prediction Markets: Design a Research Agent You Can Audit
Design a research agent that preserves evidence, separates observations from inference, and lets a reviewer reconstruct every published forecast.
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AI & agents
What should a model do with a market question—and what should remain a human decision? These articles cover evidence gathering, language-model forecasts, calibration, paper-trading experiments, and research agents whose work can be inspected.
Start with the AI research brief, move to forecasting evaluation, then examine permissions and agent design. Each article treats a fluent answer as a starting point for checking evidence. Fictional examples illustrate the method without claiming real predictive performance. The collection is useful for readers designing an editorial workflow, comparing model outputs, or deciding what information a responsible research system should preserve.
AI Prediction Bots: Paper Trading, Permissions, and Risk Controls
A practical framework for evaluating prediction bots without confusing an impressive simulation with a reliable trading system.
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LLM Forecasting: Calibration, Scoring, and Honest Evaluation
A convincing explanation is only the beginning. Learn how to record forecasts, calculate Brier scores, and test calibration without hindsight.
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AI for Prediction Markets: From Evidence to a Research Brief
Give AI a defined research task, a dated evidence set, and a reviewable output before asking it for a probability.
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