Bibliographic record
Abstract
In this dissertation, I provide a compelling explanation about why the World Trade Organization (WTO) permits retaliation only after a lengthy delay. I then explain why it usually rejects requests for retaliation (or a reciprocal withdrawal of concessions) in other related inter- national agreements. Next, I consider a more general problem about agents negotiating over an allocation of some surplus. This multilateral bargaining model could be applied to international trade or many real-world negotiations. I begin by taking a dynamic mechanism design approach and analyze the welfare effects among same-sector retaliation with and without delay as well as cross-sector retaliation with and without delay. I show that a retaliation with delay mechanism generates higher welfare and supports a higher self-enforcing level of cooperation than does a retaliation without delay mechanism. I demonstrate that under certain conditions, a same-sector retaliation mechanism generates higher welfare and supports a higher self-enforcing level of cooperation than does a cross-sector retaliation mechanism. All the above results are showing to hold for several different stochastic process of how a state of the world evolves. I then consider a more general case of bargaining where the size of the surplus is endogenized. In my model of the first two chapters after the introduction, although the size of the surplus varies across time, it still evolves in a stochastic manner. In many real-world negotiations, however, a surplus is usually created by players and each player may have certain power to influence a recognition process. Hence, my main innovation in the last chapter is to allow a surplus as well as recognition probabilities to be endogenously determined by players' actions. I assume that players' actions can have either persistent or transitory effects on a bargaining process. I compare the equilibrium outcomes under different voting rules and show that when a competition becomes less intensive (i.e., a proposal needs the consents of more players), it raises social welfare while it makes a free-ride problem more severe.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".