Social information use may lead to maladaptive decisions: a game theoretic model
Bibliographic record
Abstract
Because animals rely on the actions of others to make behavioral decisions in various contexts and social information use has important evolutionary implications, numerous theoretical studies have addressed the question of when it should occur. Despite several predictions of these models are supported by experimental findings, they have focused mainly on animals that can copy others’ decisions, without paying a cost. Yet, the acquisition or exploitation of social information is likely to be costly in many cases, notably when animals compete for depleting resources: social learners then cannot directly copy the decision of others but instead acquire generalized preferences through observation and hence suffer a risk of being unable to use the information previously collected. To explore the conditions that should favor this form of copying (i.e., acquisition of generalized preferences), we developed a mate-choice model with 2 strategies: selective females assess potential partners until they have found an acceptable mate, whereas copier females observe their mating decisions and then search for a male similar in appearance to the accepted mates. Our results indicate that the extent to which animals should rely on personal information logically increases with the costs entailed by social information use, and the proportion of asocial learners can even reach fixation. Furthermore, as the costs of using both personal and social information are frequency dependent on the proportion of social and asocial learners, there are conditions where both strategies coexist within the population, although social information use may lead to maladaptive decisions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".