Bibliographic record
Abstract
Many interesting behaviours in the animal and human world involve cooperation among individuals. Yet, cooperating individuals are often susceptible to exploitation by cheaters. Because cheaters do better than the cooperators they exploit, the evolution and persistence of cooperation has been a challenging topic of study in biology, sociology and economics. Studies often abstract from real cooperative interactions, and construct simple games in which players can choose either cooperation with other players, or defection, e.g., the well known prisoner’s dilemma and the snowdrift game. In these games and other social dilemmas, mutual cooperation yields greater payoffs than mutual defection, but individuals are still tempted to defect (because of the possibility that if they cooperate, the other player will defect). Similar dilemmas also arise in situations where multiple individuals may be affected by the actions of one (such as volunteering for community service or evading taxes), and the main theme of this thesis is cooperation in groups. In chapter 2, we analyze pre-emptive vaccination for an outbreak of smallpox (following a bioterrorist attack or accidental release), from the public health (i.e., group) and individual perspectives. Chapters 3 and 4 deal with an extension of the snowdrift game to n interacting players and continuous strategy sets (where individuals decide on their degree of cooperation): in chapter 3, we analyze global evolutionary stability of cooperative strategies in a large class of n-player snowdrift games in infinite populations; chapter 4 analyzes general continuous n-person snowdrift games in finite populations, and compares the evolutionary dynamics with their infinite population analogues. In chapter 5, we present a general framework to model selection processes in finite populations, necessary for the analysis in chapter 4.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".