One-Way Mirrors in Online Dating: A Randomized Field Experiment
Why this work is in the frame
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Bibliographic record
Abstract
The growing popularity of online dating websites is altering one of the most fundamental human activities: finding a date or a marriage partner. Online dating platforms offer new capabilities, such as extensive search, big-data based mate recommendations and varying levels of anonymity, whose parallels do not exist in the physical world. Yet, little is known about the causal effects of these new features. In this study we examine the impact of a particular anonymity feature, which is unique to online environments, on matching outcomes. This feature allows users to browse profiles of other users anonymously, by being able to check out a potential mate’s profile while not leaving any visible online record of the visit. While this feature may decrease search costs and allow users to search without inhibition, it also eliminates “weak signals” of interest for their potential mates that may play an important role in establishing successful communication. We run a randomized field experiment on a major North American online dating website, where 50,000 of 100,000 randomly selected new users are gifted the ability to anonymously view profiles of other users. Compared to the control group, the users treated with anonymity become disinhibited, in that they view more profiles, and are more likely to view same-sex and inter-racial mates. However, based on our analysis, we demonstrate causally that weak signaling is a key mechanism in achieving higher levels of matching outcomes. Anonymous users, who lose the ability to leave a weak signal, end up having fewer matches as compared to their non-anonymous counterparts. This effect of anonymity is particularly strong for women who tend not to make the first move and instead rely on the counter-party to initiate the communication. Further, the reduction in quantity of matches by anonymous users is not compensated by a corresponding increase in quality of matches.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it