Artificial intelligence could transform France’s job market - but it’s still early days - RFI
Artificial intelligence could transform France’s job market - but it’s still early days RFI
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Digital Project Abandonment Crisis: Deadweight Loss in Plain Sight
The most cited figure in startup failure research comes from the U.S. Bureau of Labor Statistics: roughly 20% of businesses fail in their first year, and about 65% within ten years. For technology companies specifically, CB Insights' analysis of over 110 startup post-mortems found that 42% failed because there was no genuine market need for what they built. Running out of cash was second at 29% — but as the report noted, cash problems typically trail the market need problem by months. The technology sector fails at higher rates than the broader business population. Approximately 63% of tech businesses fail within five years. For software-as-a-service companies specifically, the dynamics are similar: roughly 90% of SaaS startups fail to reach sustainable scale. None of these numbers are enc
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Learn then Decide: A Learning Approach for Designing Data Marketplaces
arXiv:2503.10773v2 Announce Type: replace Abstract: As data marketplaces become increasingly central to the digital economy, it is crucial to design efficient pricing mechanisms that optimize revenue while ensuring fair and adaptive pricing. We introduce the Maximum Auction-to-Posted Price (MAPP) mechanism, a novel two-stage approach that first estimates the bidders' value distribution through auctions and then determines the optimal posted price based on the learned distribution. We establish that MAPP is individually rational and incentive-compatible, ensuring truthful bidding while balancing revenue maximization with minimal price discrimination. On the theoretical side, we establish a statistical viewpoint that recasts revenue optimization as a valuation density estimation problem: we
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Learn then Decide: A Learning Approach for Designing Data Marketplaces
arXiv:2503.10773v2 Announce Type: replace Abstract: As data marketplaces become increasingly central to the digital economy, it is crucial to design efficient pricing mechanisms that optimize revenue while ensuring fair and adaptive pricing. We introduce the Maximum Auction-to-Posted Price (MAPP) mechanism, a novel two-stage approach that first estimates the bidders' value distribution through auctions and then determines the optimal posted price based on the learned distribution. We establish that MAPP is individually rational and incentive-compatible, ensuring truthful bidding while balancing revenue maximization with minimal price discrimination. On the theoretical side, we establish a statistical viewpoint that recasts revenue optimization as a valuation density estimation problem: we
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