Frequency-dependent payoffs and sequential decision-making favour consistent tactic use
Bibliographic record
Abstract
Although natural selection should have favoured individuals capable of adjusting the weight they give to personal and social information according to circumstances, individuals generally differ consistently in their individual weighting of both types of information. Such individual differences are correlated with personality traits, suggesting that personality could directly affect individuals' ability to collect personal or social information. Alternatively, the link between personality and information use could simply emerge as a by-product of the sequential decision-making process in a frequency-dependent context. Indeed, when the gains associated with behavioural options depend on the choices of others, an individual's sequence of arrival could constrain its choice of options leading to the emergence of correlated behaviours. Any factor such as personality that affects decision order could thus be correlated with information use. To test this new explanation, we developed an individual-based model that simulates a group of animals engaged in a game of sequential frequency-dependent decision: a producer-scrounger game. Our results confirm that the sequence of decision, in this case enforced by the order in which animals enter a foraging area, consistently influences their mean tactic use and their individual plasticity, an outcome reminiscent of the correlation reported between personality and social information use.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".