Predictions for economics, given AI
From Ingar Haaland: With math essentially being delegated to OpenAI, here’s what I predict for economics and the social sciences more generally: The top tier of research will just become better and it will be normal human-led research where AI is used for scale (e.g. conducting qualitative interviews with relevant populations, running behavioral interventions in […] The post Predictions for…
With the increasing reliance on AI in mathematics, the future of economics and social sciences looks promising. While the top tier of research will see enhanced quality, most human-led research will continue to utilize AI for efficiency (e.g., conducting interviews, running field experiments, and analyzing vast amounts of text data). Field experiments are expected to gain more value, and researchers who can successfully collaborate with companies and execute AI-driven tests will be in high demand.
Administrative data research is set to become even more critical, though this advantage may be limited to a select few who secure government agency collaborations. The fields of economic history and data mining are poised for significant advancements as digitization of archives gains momentum, uncovering new research methods and descriptive facts.
AI assistance will revolutionize the review and verification process, making it standard practice to conduct 360-degree reviews of papers, code, and data during the submission stage.
However, researchers who heavily rely on AI for "bread and butter" research might face challenges as much of this work could be outsourced to public agencies equipped to address their inquiries without the need for peer-reviewed papers. While this could be detrimental to certain researchers, it may lead to more efficient utilization of resources by agencies directly addressing the questions at hand.
Written by urgent.news from Marginal Revolution's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
