INSIGHTS

IDEAS, METHODS, AND TECHNOLOGY SHAPING BETTER FORECASTING AND STRATEGIC DECISIONS.

2025-11-06The Forecasting Machine

LLM narratives and macroeconomic data: the Murex research proposal Émile shared

In November 2025, a proposed Murex doctorate combined narrative monitoring with market and macroeconomic data. Émile shared the invitation, not a finished forecasting result.

2025-09-30The Forecasting Machine

Superforecasting at ten, and the “proven forecasters” NewsFutures spotted in 2006

Ten years after Tetlock and Gardner’s Superforecasting, Émile Servan-Schreiber dug out a 2006 NewsFutures market in which the previous quarter’s best forecasters beat the crowd. Why the idea took a decade to be recognised, and where AI fits now.

2025-09-27The Forecasting Machine

Who controls collective intelligence? Servan-Schreiber’s case for public forecasting

Two posts in 2025 connect a criticism of concentrated platform power with a proposal to make evidence-based forecasts available to anyone.

2025-05-20The Forecasting Machine

AI gathers, crowds guess: Servan-Schreiber’s hybrid forecasting model in Prague

At the Europe as a Task conference in Prague in May 2025, Émile Servan-Schreiber split forecasting into information gathering, where AI now leads, and guessing, where crowds still take better-calculated risks.

2025-03-19The Forecasting Machine

How humans and an early forecasting AI saw 2030: the OECD's Hypermind pilot

An OECD foresight exercise compared aggregate human forecasts with an early LLM-based system across 25 possible AI benefits and risks to 2030—and emphasized the limits of both.

2025-02-22The Forecasting Machine

Human diversity and AI model collapse: a question from UM6P’s 2025 debate

A February 2025 essay turned a debate about the future of human intelligence around: could AI sustain its performance without fresh, diverse human observations? Research on recursive training gives the question a concrete basis.

2024-10-19The Forecasting Machine

Red bars and blue bars: how betting markets and forecaster panels split over Harris and Trump

In the final weeks of the 2024 US election, Émile Servan-Schreiber tracked eleven forecasting venues. Real-money markets leaned Trump, forecaster panels leaned Harris, and the result reopened an old argument about what money adds to a forecast.

2024-09-18The Forecasting Machine

AI versus the crowd: Hypermind’s hybrid forecast map of the 2024 US presidential election

In September 2024 Hypermind set its new retrieval-augmented AI Forecasting Machine against its human prediction market on a state-by-state election map. How the experiment worked, what it forecast, and what the result can and cannot tell us.

2024-04-15The Forecasting Machine

Supercollectif readers: organising independent judgement

Readers of Supercollectif describe independent judgement, diverse information and practical team habits, from robotics to workshops and business strategy.

2024-03-02The Forecasting Machine

Gender diversity and group intelligence: from laboratory studies to workplace practice

Social sensitivity, shared speaking time and gender diversity appear in research on group performance. Servan-Schreiber connects those findings to workplace practice.

2023-09-20The Forecasting Machine

“Governing is predicting”: crowd forecasting at the Brussels Democracy Lab

At a Democracy Lab on smarter crowdsourcing in Brussels on 19 September 2023, Émile Servan-Schreiber gave EU policymakers five reasons to use crowd forecasting, starting with the idea that governing is predicting.

2023-07-06The Forecasting Machine

How independent errors become a crowd forecast: Hypermind’s four aggregation steps

A July 2023 preview highlighted the value of combining different judgements. Its technical companion explains weighting, culling, averaging and extremizing.