AI Allows More Diversity in the Forms of Social Science

Source: Kevin Munger, Never Met a Science · 2026-05-01

So that was fun. But the intellectual agenda is no longer mine to set, not even on my own blog; it’s currently impossible to think about media theory or metascience without grappling with AI. AI is shaping up to be a central political issue, in terms of electoral, geo-, and cultural politics. But it’s also immediately important for my day job as a professor of political science, so I’ve been focusing my serious writing on AI and metascience.

Today, I present my contribution to an internet-native “edited volume” organized by Rohan Alexander about the future of quantitative social science in the age of AI. This is brilliant meta-scientific institutional work — he emailed prospective authors in late March, organized a workshop that’s taking place today, and otherwise simply set up a lightweight online publishing platform (a “website”) that I’m sure is using an LLM workflow to harmonize and store all of the contributions.1 This is a methododological contribution, in the sense in which some social scientists (including me) specialize as methodologists. We have to begin to expand the scope of what counts as an epistemic contribution beyond the pdf.

And that’s the theme of my contribution: AI Allows More Diversity in the Forms of Social Science. The whole essay follows — don’t worry, this is written more like a classic Never Met A Science blog post than an academic article. But in even more classic Never Met A Science form, I first want to take a step back and emphasize the AI usage statement, an intensely contested new methodological step in the process of social science, discussed here.

AI usage statement: I dictated a first draft of this manuscript to Claude Opus 4.7 while walking my infant son. I told Claude to clean up the transcript of the conversation and to structure some of the disparate threads while using my exact words as much as possible and demarcating any significant interpolations or transitions. I then went back and edited the manuscript, added citations (I also use Claude to manage my citations in bibtex), and then rewrote or deleted the Claude-labeled Claude-generated text. Insofar as it followed my instructions, all of this text is my own – but there’s no way for me to verify this, nor do I think it especially important.

The successful transition to the age of AI must entail greater diversity in the forms that quantitative social science takes. Of course, the same was true of the age of the internet; quantitative social science did not have a successful transition to the internet age. We should have had a revolution in the modality in which we store and communicate scientific knowledge when the internet became standard across universities. It is an embarrassment to our field, and a significant impediment to the pace of social scientific progress over the past thirty years, that we essentially just took old paper journals and put them online with no innovation in form. But AI will soon make it impossible for us to ignore the absurdity of the status quo–especially if enterprising social scientists collectively think hard about how to change social science. This collection of articles is encouraging in this regard, and makes me more optimistic that social science will muster the energy to reject the entrenched interests preserving the status quo.

The present metascientific framework is an extension of the one used in Munger (2026), coauthored with eight other editors of social science journals. We consider how peer review is going to have to change as AI becomes more widespread. The framework requires us to consider different possible equilibria; it’s not sufficient to think about how one subcomponent of the overall academic ecosystem might change. If you only change one thing, there will be adaptation by other components of the system that might dilute the desired effects of the reform, or have other kinds of negative consequences. To do this kind of metascience rigorously, it’s essential to be aware of all of the different functions that peer review serves in order to think about how we might incorporate AI in a way that satisfies all of the relevant desiderata while also producing a stable equilibrium which provides the right incentives for academics.

But in addition to theory, we need trial-and-error. It’s just as absurd to believe that we can perfectly predict how AI will be incorporated into social science as it is to predict that the status quo will persist. The essential first step is to try things out; I discuss three ways in which AI allows for innovation in knowledge production (meta-ontology, time, and rapid empirical iteration). But to give practicing scientists the appropriate incentives to try out these new forms, we must overcome an age-old tyranny.

The biggest bottleneck to the exploration of AI-enabled formal diversity is the uniformity of the outputs for which social scientists can get credit. I refer, of course, to the tyranny of the peer-reviewed PDF. There are many different types of epistemically relevant, valorous, valuable actions that social scientists take, but the only one for which we are officially recognized and rewarded is the peer-reviewed PDF.1 The fact that all social science has to be routed into this modality of communication causes us to flatten the types of epistemic contributions that we make, and many of them are really very poorly fit for being stored and communicated in this modality.

Before getting to the ways the PDF is insufficient, it’s worth delineating the bundle of functions the pdf serves. The static PDF functions as both an archive of what actions the scientist has taken and as a reference to previous research products: the literature review, the theoretical arguments, the methods section summarizing different statistical tools taken, the statistical results, the conclusions, the references. All this made sense to bundle together, because the implicit arrangement at the point when the academic paper became the standard was that there were many humans who were going to read these PDFs.

But with the internet and especially with AI, there’s no need for every epistemic contribution to always include every one of these elements. By unbundling them and allowing for epistemic contributions to take different forms, social science can become radically more efficient.

I propose three dimensions on which those new forms can transcend the PDF. The first is the introduction of a better-defined, more explicit meta-ontology of social science. The second is time: the fact that knowledge can be kept up to date rather than stored as a single static PDF. The third is aggressive empirical exploration within a well-defined space of experimentation or quantitative study.

The first problem with the PDF is that it presents each paper as a single contained narrative. Sure, the authors draw from previous studies for the purpose of making a theoretical argument connected to their specific empirical case. It’s not meant to be completely unique, but ideally, both the theoretical arguments and the empirical cases are understood to be in some way novel through the production of a narrative in the academic paper. Social scientists understand that they need to have a story. This is especially true for the most important paper they will ever write: their job market paper.

The bundle-narrative form treats the task of writing these papers as something which involves craft, for which taste is necessary. I think this is a valuable art form, one which I very much enjoy – but we’re seeing it devalued and commoditized because it is the only permissible form of epistemic contribution. We might collectively write far fewer of these narrative PDFs, and then focus our attention on these more careful, artisanally constructed arguments. The diversification of the forms of quantitative social science will in fact enable us to appreciate the narrative PDF for what it actually sets out to accomplish.

The narrative PDF requires authors to play a kind of trick: to convince readers that all of the components of the bundle hang together correctly. This particular RCT allows us to learn about the “effects of education on support for the far right”; this survey experiment on an online convenience sample does in fact generalize to a population from which it’s not randomly drawn; this other game theoretic model in which one of the actors is labeled a “voter” does in fact relate some way to what happens to people in the voting booth.

Each pdf weaves these components together into as coherent a narrative as it can manage. But contemporary methodology pays very little attention to the individual linkages. We know how to evaluate each component in the abstract; the question of whether they hang together is more of an art form.

We need a more well-defined meta-ontology of social science. We need a database that tracks each of these components separately, as well as how they have been combined.