Bibliographic record
Abstract
This dissertation explores whether and when people act strategically: that is, how people's actions are influenced by others' actions (Chapters 1 and 2) and preferences (Chapter 3). Chapter 1, titled "Paternalistic Persuasion", studies whether paternalistic experts ("Advisors'') can make decision-makers ("Choosers'') better off by persuading them to change their behaviour. In a setting where Choosers are wary of Advisors' incentives, I experimentally investigate whether Advisors send recommendations that account for this wariness, and why they may fail to do so. I find that nearly 80% of Advisors send sub-optimal recommendations, but prompting them to think about Choosers' likely response is an effective way to correct this mistake. This suggests that the mistake stems from a failure to focus on recommending actions that are both welfare-improving and appealing to Choosers. Chapter 2, titled "Gender Differences in Job Application Strategies: An Experimental Investigation", is co-authored with Annabel Thornton. We experimentally investigate how gender differences in beliefs about a job's "competitiveness'' - the quantity and quality of its applicants - may make women less likely to apply for high-paying yet competitive jobs. We design a game where, without knowing one's rank, members of a group select one of three "jobs'': lotteries that either yield a large payoff to the best-ranked person who selects them, or guarantee a small payoff. We find that willingness to apply to a job decreases in the believed competitiveness of its applicant pool, and that gender differences in these beliefs create gaps in willingness to apply to the highest-paying competitive job. Chapter 3, titled "Preference Aggregation in Social Choice Under Risk", studies decision-makers' willingness to accommodate others' preferences when making "social choices": choices that influence both one's own and others' welfare. I design an experimental task that can answer these questions in two-person social choice problems, and use it to study social choice under risk. I find that over one-third of decision-makers are willing to accommodate a recipient's risk preferences. These decision-makers accommodate wide ranges of preferences, but tend to favour preferences that are similar to their own. Recipients' preferences carry the greatest weight when the decision-maker's own preferences are incomplete.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".