Topics — The twenty-eight kinds of claim this anthology files against — the primary axis of both the practice registry and the reading list, and the same twenty-eight for each (ADR-026). The filing rule is the point: take a domain topic when the claim is ABOUT the domain; a claim merely discovered in one still takes its kind. The axis organizes the record, it does not bound it: content the list cannot place is evidence the list is short (ADR-059).
Deployment and society — what a system does once it is running among people, and how anyone outside it could find out — audits of deployed platforms and recommenders, information ecosystems, moderation and governance, and the access problems and funding pressures of studying any of it from the outside.
5 of 129 THEORY documents. Back to the full index.
| # | Title | Summary | Status |
|---|---|---|---|
| THEORY-047 | Personalizing a feed concentrates what it shows rather than broadening it, and a handful of moderate signals is enough to start | Ye, Luceri and Ferrara (2024), LIT-480 — across four arms of an audit, the accounts following nobody receive the most diverse recommendations and the partisan arms the least. Ten moderate media follows and four political accounts are enough to amplify aligned voices more than 50% above a balanced baseline. | Proposed |
| THEORY-048 | Randomizing individuals estimates an individual quantity, and when the units interact the collective effect is not the sum of it — so a null from such a trial is not evidence of no effect | Bak-Coleman et al. (2025), LIT-482 — four named reasons an individual-level randomized trial fails to estimate a collective effect: non-linearity across scale, hysteresis, feedback in time, and violation of the stable unit treatment value assumption through the network. The power grid shows the first and the last together: treat too few and the system absorbs it, treat enough and the controls fail too. | Active |
| THEORY-051 v2 | A text feature predicts success partly by being uncommon, so a tool that gives it to everyone destroys its predictive value | Corpus et al. (2025), LIT-487 — length, readability and lexical variety predicted petition success, an AI drafting tool moved all three in the successful direction and raised rated quality, and success did not follow. Inter-petition similarity rose 23% in the same window, and the features’ predictive strength weakened or reversed. | Proposed |
| THEORY-055 | A producer’s own rate of return is the price of their time, so making them faster raises the bar on every marginal hour of improvement | Duede, Gross, Crockett and Bergstrom (2026), LIT-492 — in a marginal-value-theorem model of effort allocation, the long-run rate of return is simultaneously the objective and the opportunity cost of the marginal hour. Any acceleration raises it, so the marginal hour of improvement has to clear a higher bar. Whether quality then falls is a race between that bar and the value of the hour, which is why the sign depends on which phase was accelerated rather than on how much time was saved. | Proposed |
| THEORY-091 | Command-to-outcome mutual information scores an interface because it measures how reliably the operator’s input determines what happens at the chosen horizon, which rewards a learnable channel rather than an intuitive one | Reddy, Levine and Dragan (2022), LIT-503. The paper’s reason is that an intuitive interface makes commands less noisy. Its own results fit a narrower account: the score measures how much the operator’s input determines the state Δ steps later. Users adapt, so any consistent mapping scores well, and learned interfaces converge on both no perturbation and exact inversion. The account also explains the one failure: an assistant that overrides commands to prevent crashes lowers one-step influence and raises long-horizon influence. | Proposed |