The effect of kinship cooperation learning strategy and culture on the resilience of social systems in the village multi-agent simulation
Bibliographic record
Abstract
The multi-agent village simulation was initially developed to examine the settlement and farming practices of prehispanic Pueblo Indians of the Central Mesa Verde region of Southwest Colorado (Kohler, 2000; Kohler et al.). The original model of Kohler was used to examine whether drought alone was responsible for the departure of the prehispanic Puebloan people from the Four Corners region after 700 years of occupation. The results suggested that other factors besides precipitation were important. We then proceeded to add economic factors into the simulation, first allowing agents to engage in reciprocal exchanges between kin. This resulted in larger populations, more complex social networks, and more resilient systems. However, the exchange was done randomly and individuals did not remember the transactions. In This work we explicitly embed the reciprocal exchange process within a cultural algorithm, where individual agents can remember individuals that they have cooperated with. Also, in the cultural space the group can learn generalizations about what kind of relative is likely to successfully respond to a request. These generalizations are used to drive changes in requestor behavior. The results of this approach produced an even larger and more complex system exhibiting greater dependence on hub nodes that are sensitive to precipitation.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".