Does Environmental Knowledge Inhibit Hominin Dispersal?
Why this work is in the frame
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Bibliographic record
Abstract
We investigated the relationship between the dispersal potential of a hominin population, its local-scale foraging strategies, and the characteristics of the resource environment using an agent-based modeling approach. In previous work we demonstrated that natural selection can favor a relatively low capacity for assessing and predicting the quality of the resource environment, especially when the distribution of resources is highly clustered. That work also suggested that the more knowledge foraging populations had about their environment, the less likely they were to abandon the landscape they know and disperse into novel territory. The present study gives agents new individual and social strategies for learning about their environment. For both individual and social learning, natural selection favors decreased levels of environmental knowledge, particularly in low-heterogeneity environments. Social acquisition of detailed environmental knowledge results in crowding of agents, which reduces available reproductive space and relative fitness. Agents with less environmental knowledge move away from resource clusters and into areas with more space available for reproduction. These results suggest that, rather than being a requirement for successful dispersal, environmental knowledge strengthens the ties to particular locations and significantly reduces the dispersal potential as a result. The evolved level of environmental knowledge in a population depends on the characteristics of the resource environment and affects the dispersal capacity of the population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it