Bibliographic record
Abstract
Animal conflict typically manifests itself as an aggressive interaction between two individuals, but the nature of this interaction can sometimes be influenced by third parties. For example, in some systems, individuals can observe fights between others and use the information they gain to help shape their own fighting strategies. Fighters themselves can modify their own behaviour dependent on whether an audience is present, and winners of fights sometimes display their victory to bystanders. In other systems, fights are genuinely polyadic and coalitions can form, with two or more individuals forming an alliance to protect or obtain a valuable resource. These coalitions often involve two individuals fighting one individual but sometimes contests more akin to warfare can take place between two large groups of individuals. Ecological interactions are shaped by natural selection and frequently involve cases in which the payoff from adopting any given behavioural strategy is dependent on the strategies adopted by other members of the population. So games, mathematical models of strategic interaction, are potentially a powerful tool to represent and analyse many of the multi-party contests described above. In this chapter we briefly review examples of multi-party contests in nature before going on to describe how and why such contests have been represented mathematically and the types of insight these models have delivered. Conflicts that take place within large networks are, almost by definition, complex affairs, but here we show that the representation of multi-party contests as triadic interactions can go some way to explaining a variety of phenomena ranging from victory displays to neighbour intervention and that these models can provide benchmarks for the exploration of more complex systems. Finally, we briefly review where this modelling work may lead, and identify some challenges that lie ahead.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".