On 18 January 2016, Émile Servan-Schreiber shared a link asking how the collective intelligence of the future worked, naming Hypermind and Lumenogic. The short link resolves to a HUB Institute YouTube recording of his presentation. YouTube’s official metadata identifies the publisher as HUB Institute and dates the upload to 15 January; Hypermind’s own media listing describes it as a January 2016 conference.
A lecture and a written primer
The recording is titled L’intelligence collective | Hypermind, Emile Servan-Schreiber and identifies HUB Institute as its publisher. Its subject connects a broad idea about people thinking together with Hypermind’s public forecasting work. A related printed interview offers a concrete explanation of how its market mechanism turns participants’ judgements into prices.
On 4 June 2015, Servan-Schreiber shared an earlier introduction to collective intelligence. Its attached image is a Le Point interview, published in the print issue dated that day and credited to Jérôme Béglé. The newspaper’s official web page dates the online version to 3 June. In the interview, Servan-Schreiber and Mathieu Laine explain the mechanics and interpretation of a prediction market.
From a price to a probability
In the printed interview, Servan-Schreiber described a market in which forecasts are represented by shares that participants buy and sell. A share pays 100 points if its event occurs and nothing if it does not. He stressed that profits and losses were virtual in this case, with prizes allocated in proportion to performance.
Mathieu Laine explained the reading convention: a price on the zero-to-100 scale expresses the crowd’s probability. The interview gives a price of 27 for a question about François Hollande becoming the next president, and 70 for euro–dollar parity before December 2015. These are the interview’s explanatory examples; they are not a separate dated probability series being evaluated here.
The interview also distinguishes a prediction market from an opinion poll: it asks participants to anticipate an outcome rather than describe their present preferences. Our market-probability explainer develops that distinction.
The lecture and the written primer address public and professional audiences interested in collective intelligence. Their common question is how separate judgements become a usable answer. The market explanation makes that process visible: participants trade, prices change and the event determines the share’s payoff. Understanding that convention is a necessary first step before interpreting a market number or comparing it with the eventual outcome.
