Computational Evidence that Self-regulation of Creativity is Good for Society
Bibliographic record
Abstract
Excess individual creativity can be detrimental to society because creators invest in unproven ideas at the expense of propagating proven ones.Moreover, a proportion of individuals can benefit from creativity without being creative themselves by copying creators.We hypothesized that (1) societies increase their rate of cultural evolution by tempering the novelty-generating effects of creativity with the novelty-preserving effects of imitation, and (2) this is carried out by selectively rewarding and punishing creativity according to the value of the individuals' creative outputs.We tested this using an agent-based model of cultural evolution in which each agent self-regulated its invention-to-imitation ratio as a function of the fitness of its cultural outputs.In self-regulating societies, agents segregated into creators and imitators.The mean fitness of cultural outputs was higher than in non-self-regulating societies, and changes in diversity were rapider and more pronounced.We discuss limitations and possible social implications of our findings.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".