Bibliographic record
Abstract
This thesis groups three papers in applied microeconomic theory that focus on political economy and the economics of organisations.\n\nThe first chapter studies the equilibrium outcomes of a dynamic game of electoral competition between two policy-motivated parties. I model incumbent policy persistence: parties commit to implement a policy for their full tenure in office, and hence in any election only the opposition party is free to choose a new platform. The model gives rise to novel equilibrium policy dynamics: governments alternate in power; parties compromise, that is, starting from differentiated ideological positions, they gradually move towards proposing platforms which resemble one another; however, they never capitulate, that is, party labels matter and parties maintain distinct policy goals.\n\nThe second chapter studies a directed search model of competition between sellers that control the quality of buyers' private information about goods. As better informed buyers extract more informational rents from trade, sellers may try to attract buyers by offering better information. First, I establish how the characteristics of exogenously fixed sale mechanisms determine equilibrium information provision. Information provision is higher under competition than under monopoly, yet partial information is provided for many sale mechanisms. Second, when sellers commit to both information provision and mechanisms, I identify simple conditions under which every equilibrium has full information. In these equilibria, sellers capture the efficiency gains of information provision and compete only over non-distortionary rents offered to buyers.\n\nRetaining the option to develop a currently inactive project often requires maintaining specialised stocks of knowledge. However, standard models of experimentation treat the choice of one project over another as entailing only an implicit opportunity cost. In the third chapter, I characterise the optimal experimentation policy in a model in which undeveloped projects have explicit maintenance costs and can be irreversibly discarded. Projects which in the absence of maintenance costs would be developed only after more promising projects fail are sometimes developed first and then discarded early. Maintenance costs alter optimal project development by providing incentives to bring the option value of less promising projects forward.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".