Evolutionary model discovery of causal factors behind the socio-agricultural behavior of the Ancestral Pueblo

Chathika Gunaratne, Ivan Garibay

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Agent-based modeling of artificial societies allows for the validation and analysis of human-interpretable, causal explanations of human behavior that generate society-scale phenomena. However, parameter calibration is insufficient to conduct data-driven explorations that are adequate in evaluating the importance of causal factors that constitute agent rules that match real-world individual-scale generative behaviors. We introduce evolutionary model discovery, a framework that combines genetic programming and random forest regression to evaluate the importance of a set of causal factors hypothesized to affect the individual’s decision-making process. With evolutionary model discovery, we investigated the farm plot seeking behavior of the Ancestral Pueblo of the Long House Valley simulated in the Artificial Anasazi model. We evaluated the importance of causal factors unconsidered in the original model, which we hypothesized to have affected the decision-making process. Our findings, concur with other archaeological studies on the Ancestral Pueblo communities during the Pueblo II period, which indicate the existence of cross-village polities, hierarchical organization, and dependence on the viability of the agricultural niche. Contrary to the original Artificial Anasazi model, where closeness was the sole factor driving farm plot selection, selection of higher quality land, distancing from failed farm plots, and desire for social presence are found to be more important. Finally, models updated with farm selection strategies designed by incorporating these insights showed significant improvements in accuracy and robustness over the original Artificial Anasazi model.

Original languageEnglish
Article numbere0239922
JournalPLoS ONE
Volume15
Issue number12 December
DOIs
StatePublished - Dec 2020
Externally publishedYes

Funding

DARPA SocialSim program: Awarded to IG, DARPA program HR001117S0018, Defense Advanced Research Projects Agency https://www. darpa.mil/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. AWS Research Grant Awarded to IG, N/A, Amazon Web Services https://aws.amazon.com/grants/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

FundersFunder number
Defense Advanced Research Projects AgencyHR001117S0018

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