On 6 November 2019, Émile Servan-Schreiber described an in-depth prediction-markets class at Morocco’s 1337 coding school. He thanked Pavel Atanasov for a special guest contribution and Camille Larmanou for helping turn the class into an interactive exercise, with live dice-roll markets and real-time jelly-bean experiments.
Camille Larmanou’s account said the Moroccan coding school had hosted them the previous week. She identified the teaching as a new course from the UM6P School of Collective Intelligence using the Hypermind prediction market. The exercises brought a topic often discussed through distant political or economic events into a setting where students could participate directly.
Learning through a market and an estimate
The reported exercises gave the class two points of contact with uncertainty: markets around dice rolls and estimates involving jelly beans. As a teaching distinction, an uncertain future outcome and an unknown existing quantity call for different kinds of judgement. Students can discuss what they know, how confident they should be and how their answers should be combined. This explains the exercises’ pedagogical relevance without reconstructing their specific rules or results.
Larmanou’s recap identified diversity, specialisation and independence as ingredients of collective intelligence. It also described prediction markets as useful for complex or unfamiliar situations with unstructured information. These are the course takeaways she reported; the post was not an experiment comparing the class’s performance against another forecasting method.
She stressed calibration: forecasts expressed as probabilities should be assessed against the frequency with which outcomes actually occur. The distinction matters in a classroom because a correct answer to one question cannot show that a student’s confidence levels are reliable. The journal’s review of Hypermind’s calibration record examines a much longer collection of forecasts.
A collaborative teaching setting
1337’s official concept page describes an educational approach based on peer learning. That context makes an interactive class on collective judgement a relevant fit, although the general school description is not independent documentation of this particular session.
According to Larmanou, Atanasov contributed a discussion of how predictions inform bets. That connects a belief about an outcome with a decision made under uncertainty. Servan-Schreiber said he learned from the guest contribution himself, recording an exchange between teachers as well as students.
Larmanou anticipated meeting again in December to identify strong forecasters. Her account links participation with assessment: students first encounter collective judgement through an exercise, then can examine how their estimates compare with outcomes. The specific exercise rules and follow-up results remain unspecified. This teaching emphasis complements the journal’s broader account of forecasting courses at IE University and UM6P.
