Catfished! Impacts of Strategic Misrepresentation in Online Dating
Bibliographic record
Abstract
Online dating is a multibillion-dollar global industry, and is increasingly becoming the go-to method for finding partners. Intricate dynamics mark its operation, influenced by varying user preferences, strategies, and traits, as well as by the underlying matchmaking algorithm. This complexity renders it a pertinent subject for multiagent systems research. Despite its relevance, an established simulation framework for online dating is lacking. This paper introduces a multiagent simulation framework for this domain. The framework is extensible and capable of modeling agents with diverse attributes and preferences, either reported or latent. It also supports varied strategies, outcomes, and types of matchmaking logic. Using this framework, we simulate an online dating platform based on real-world demographics to examine the effects of strategic misrepresentation, a notable concern in online dating. Surprisingly, the negative effect of strategic misrepresentation on users is marginal. Moreover, it disproportionately benefits female or honest agents more, enhances the overall welfare of the user population, and benefits attractive users - whether deceitful or not - over less attractive ones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".