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Report NEP-BIG-2026-03-23
This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé (Tom Coupe) issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-BIG
The following items were announced in this report:
- De Munck, Thomas & Tancrez, Jean-Sébastien & Chevalier, Philippe, 2025, "Transfer Reinforcement Learning for Pricing, Driver Repositioning and Customer Admission in Ride-Hailing Networks," LIDAM Discussion Papers CORE, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE), number 2025004, Feb.
- Luca Macedoni & Ariel Weinberger, 2026, "Lobbying for Regulations: When Big Business Says Yes," CESifo Working Paper Series, CESifo, number 12536.
- Simon Greenhill & Brant J. Walker & Joseph S. Shapiro, 2026, "Deep Learning Projects Jurisdiction of New and Proposed Clean Water Act Regulation," NBER Working Papers, National Bureau of Economic Research, Inc, number 34947, Mar.
- Marina Azzimonti & David Wiczer & Yang Xuan, 2026, "Estimating Demand Shocks from Foot Traffic: A Big-Data Approach," Working Paper, Federal Reserve Bank of Richmond, number 26-05, Mar.
- Rudy Marhastari & Cicilia Anggadewi Harun & Retno Muhardini & Agatha Silalahi & Annes Nisrina Khoirunnisa & Rheznandya Arkaputra Azis & Sintia Aurida & Rahardian Luthfan Ihtifazhuddin & Citra Ayu Ross, 2025, "Echoes Of Policy: Leveraging Ai/Ml To Support Central Bank Communication Strategies," Working Papers, Bank Indonesia, number WP/19/2025.
- Yizhi Liu & Balaji Padmanabhan & Siva Viswanathan, 2026, "Estimating Visual Attribute Effects in Advertising from Observational Data: A Deepfake-Informed Double Machine Learning Approach," Papers, arXiv.org, number 2603.02359, Mar.
- Mohammad Al Ridhawi & Mahtab Haj Ali & Hussein Al Osman, 2026, "Stock Market Prediction Using Node Transformer Architecture Integrated with BERT Sentiment Analysis," Papers, arXiv.org, number 2603.05917, Mar, revised Apr 2026.
- Fuchs, Anna & Haensch, Anna-Carolina & Weber, Wiebke, 2026, "AI for Survey Design: Generating and Evaluating Survey Questions with Large Language Models," SocArXiv, Center for Open Science, number fzn7t_v1, Mar, DOI: 10.31219/osf.io/fzn7t_v1.