Potential disadvantages of using socially acquired information
Bibliographic record
Abstract
The acquisition and use of socially acquired information is commonly assumed to be profitable. We challenge this assumption by exploring hypothetical scenarios where the use of such information either provides no benefit or can actually be costly. First, we show that the level of incompatibility between the acquisition of personal and socially acquired information will directly affect the extent to which the use of socially acquired information can be profitable. When these two sources of information cannot be acquired simultaneously, there may be no benefit to socially acquired information. Second, we assume that a solitary individual's behavioural decisions will be based on cues revealed by its own interactions with the environment. However, in many cases, for social animals the only socially acquired information available to individuals is the behavioural actions of others that expose their decisions, rather than the cues on which these decisions were based. We argue that in such a situation the use of socially acquired information can lead to informational cascades that sometimes result in sub-optimal behaviour. From this theory of informational cascades, we predict that when erroneous cascades are costly, individuals should pay attention only to socially generated cues and not behavioural decisions. We suggest three scenarios that might be examples of informational cascades in nature.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".