Coalitions in theory and reality: a review of pertinent variables and processes
Bibliographic record
Abstract
Coalitions and alliances are ubiquitous in humans and many other mammals, being part of the fabric of complex social systems. Field biologists and ethologists have accumulated a vast amount of data on coalition and alliance formation, while theoretical biologists have developed modelling approaches. With the accumulation of empirical data and sophisticated theory, we are now potentially able to answer a host of questions about how coalitions emerge and are maintained in a population over time, and how the psychology of this type of cooperation evolved. Progress can only be achieved, however, by effectively bridging the communication gap that currently exists between empiricists and theoreticians. In this paper, we aim to do so by asking three questions: (1) What are the primary questions addressed by theoreticians interested in coalition formation, and what are the main building blocks of their models? (2) Do empirical observations support the assumptions of current models, and if not, how can we improve this situation? (3) Has theoretical work led to a better understanding of coalition formation, and what are the most profitable lines of inquiry for the future? Our overarching goal is to promote the integration of theoretical and field biology by motivating empirical scientists to collect data on aspects of coalition formation that are currently poorly quantified and to encourage theoreticians to develop a comprehensive theory of coalition formation that is testable under real-world conditions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".