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

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.

Investigating Dynamics of COVID-19 Spread and Containment with Agent-Based Modeling

“…Another future research idea is to explore conditional containment strategies currently not used by governments but hypothesized as better candidates, in relation with various vaccine regimes. An implementation of the proposed model in the context of EMD (Evolutionary Model Discovery) which allows for the rule design automation and search in the vast space of possible behavior rules [44] that produce multiple waves of infection is another future research idea. The method used to calculate the basic reproduction number is called generation matrix and is described in [45].…”

Tackling Climate Change with Machine Learning

“…Other applications of ABMs include modeling how behavior under social norms changes with external pressures [715], how the economy and climate may evolve given a diversity of political and economic beliefs [272], and how individuals may migrate in response to environmental changes [787]. While agent and environment models in ABMs are often hand-designed by experts, ML can help integrate data-driven insights into these models [874], for example, by learning rules or models for agents based on observational data [312,875], or by using unsupervised methods such as variational autoencoders or generative adversarial networks to discover salient features useful in modeling a complex environment. While the hope of learning or tuning behavior from data is promising for generalization, many data-driven approaches lose the interpretability for which ABMs are valued; work in interpretable ML methods could potentially help with this.…”

SAGAscape: Simulating Resource Exploitation Strategies in Iron Age to Hellenistic Communities in Southwest Anatolia

“…It provides a formal computational framework that allows a more dynamic approach to complex issues such as the delineation of catchment areas and calculations of carrying capacity. ABM has been successfully applied in archaeological studies on humanenvironment interactions and resource exploitation strategies for topics such as subsistence strategies (Gunaratne & Garibay 2020), risk-decreasing strategies driving social organisation (Shultz & Costopoulos 2019), and resource distribution and exploitation strategies (Janssen & Hill 2016).…”