Notes on the Future of Quantitative Social Science
Preface
AI is re-shaping society and the quantitative social sciences are no exception. All of us now have to grapple with how it is changing both research and teaching.
Some of us in the quantitative social sciences are brand new to AI - our first experience was, say, Winter 2026 when Claude Code became especially popular. Some of us got excited when ChatGPT was released in November 2022 or when GPT-3 was released in the middle of 2020. Still others of us in the quantitative social sciences were relatively early to what is now AI in earlier guises such as natural language processing or text-as-data. Regardless of experience, very few people saw what it would become.
On the research side, review comments, albeit from machines not peers, can be obtained in seconds; entire research papers can be generated in a matter of minutes; and data cleaning that used to take weeks can be coded in hours. What to make of our hard-won coding and analytical skills?
Quantitative social science is a nebulous term that often encompasses the more applied researchers from economics, political science, sociology.
This edited volume came about after having multiple similar conversations with colleagues from various departments we decided to host a conference on 1 May 2026. Papers were required to be submitted before the conference, and were quickly brought together in an informal volume for discussion. Over the next few months additional contributions were received from those that were unable to join the conference, and updates were made.
The contributions in this edited volume are a marker of how academics from a diverse range of disiplines are thinking about AI in the middle of 2026.
Chapter 1 Evaluation rules everything around me by Rohan Alexander.
Chapter 2 AI All The Way Down: Infinite Regress and the Future of Quantitative Social Science by Lisa P. Argyle, Ethan C. Busby, and Joshua R. Gubler.
Chapter 3 Teaching data analysis with agentic AI for social science students by Gabor Békés.
Chapter 4 As AI Lowers the Cost of Research, Adjudication and Attention Will Become the Bottlenecks by Ryan Briggs.
Chapter 5 The Shifting Production Function: AI, Reproducibility, and the Future of Quantitative Social Science by Abel Brodeur and Bruno Barbarioli.
Chapter 6 All Models Are Wrong, But Some Are Trending by Mike Burnham.
Chapter 7 Research Transparency and Collaboration in the Era of Generative AI Requires Open, Clean Code by Chris Cochrane and Michael Cowan.
Chapter 8 Failing Faster, Learning Better: Agentic AI and Empirical Social Science Research by Charles Crabtree, Valentina Gonzalez-Rostani, and Jae Yeon Kim.
Chapter 9 A relational approach to agentic AI in social science research: Projects, roles, and tasks by Thomas Davidson.
Chapter 10 Fault Lines: Inequality and the Future of Quantitative Social Science in the Age of Artificial Intelligence by Alicia Eads and Fedor Dokshin.
Chapter 11 My Title by Josh Goldstein.
Chapter 12 AI for Me, Not (Yet) for Thee? Desirable Difficulties and Deliberate Friction with LLMs by Andrew Heiss.
Chapter 13 Artificial Intelligence and the Measurement Imperative by Rehan Mirza and Sandra González-Bailón.
Chapter 14 AI Allows More Diversity in the Forms of Social Science by Kevin Munger.
Chapter 15 Epistemic Standards and the Next Generation of Scholars by Andreea Musulan and Jean-François Godbout.
Chapter 16 Quantifying Culture from Embeddings to Generation by Laura K. Nelson and Tom Einhorn.
Chapter 17 With Great Powers: A Practical Guide to Agentic AI for Social Science Research by Simone Paci.
Chapter 18 The Human Element in Social Science Research by Alexis Palmer.
Chapter 19 Agentic AI and the Thawing of Frozen Data in Social Science by Pratik Sachdeva and Dennis Feehan.
Chapter 20 Optimal Editorial Screening Under Cheap Testing by Gabriel Sekeres.
Chapter 21 Intelligent Machines Are Social Actors by Milena Tsvetkova.
Chapter 22 Curation and Reproducibility in an Artificial Intelligence World: Challenges and Solutions for Scientific Research by Lars Vilhuber.
Chapter 23 On Benchmarks by Eva Vivalt.
Chapter 24 Computational Social Scientists Need to Care About the Competitiveness of the AI Market by Patrick Wu.
I would like to thank David Grubbs.
It has been a pleasure to edit this collection, and I hope that you find it as informative and interesting as I did.
Rohan Alexander
University of Toronto
rohan.alexander@utoronto.ca
Toronto