Data from: Phase synchronization between culture and climate forcing
Data files
Feb 25, 2024 version files 10.17 KB
-
README.md
2.29 KB
-
standard_model_parameters.txt
192 B
-
submission_culture_model_culturaladaptation.m
7.68 KB
Abstract
Over the history of humankind cultural innovations have helped improve survival and adaptation to environmental stress. This has led to an overall increase in human population size, which in turn further contributed to cumulative cultural learning. During the Anthropocene, or arguably even earlier, this positive socio-demographic feedback has caused a strong decline in important resources, that - coupled with projected future transgression of planetary boundaries - may potentially reverse the long-term trend in population growth. Here we present a simple consumer/resource model that captures the coupled dynamics of stochastic cultural learning and transmission, population growth, and resource depletion in a changing environment. The idealized stochastic mathematical model simulates boom/bust cycles between low-population subsistence, high density resource exploitation and subsequent population decline. For slow resource recovery timescales and in the absence of climate forcing, the model predicts a longterm global population collapse. Including a simplified periodic climate forcing, we find that cultural innovation and population growth can couple with the climatic forcing via nonlinear phase-synchronization. We discuss the relevance of this finding in the context of cultural innovation, the anthropological record and longterm future resilience of our own predatory species.
https://doi.org/10.5061/dryad.8sf7m0cwb
The main data provided with this submission includes the code used to generate the key figures in “Phase synchronization between culture and climate forcing, Proceedings of the Royal Society, B, 2024”
Code/Software
This Matlab code performs numerical integrations of the stochastic ordinary differential equations (1)-(3) in Phase synchronization between culture and climate forcing, Proceedings of the Royal Society B, 2024 and generates the main figures. We use an Euler discretization of these equations (code lines 67, 69, 74) and apply a Weibull-distributed stochastic forcing with distribution parameters 1 and 0.1 (code line 74). Since there is no random seed for the stochastic term, every call of this code will generate a different sequence of random numbers and slightly different overall trajectories.
For kappa>0 (code lines 143 and below), the model equations turn into a non-autonomous (external forced) system, which is used to study the phase synchronization of culture and population density relative to the external climatic forcing. The phase synchronization is quantified by studying the histogram of the phase differences (code lines 216-229), obtained from the Analytical Signal (code lines 176-189) of dynamical variable and forcing. A uniform phase difference histogram corresponds to the absence of phase synchronization and deviations from this null hypothesis will be characterized as phase-synchronization.
The code in this submission is included as submission_culture_model_culturaladaptation.m
Standard Model parameters:
To obtain the standard model solution in Figure 2 a of the main manuscript, the following parameters in the Matlab code have to be selected: lambda=0.01,alpha=0.01,mu=0.001,eta=0.1,sigma=0.00000001. All other figures can be reproduced in a statistical sense, by adjusting these model parameters to the corresponding values indicated in the respective figure captions in the main manuscript.
The standard model parameters are included in this submission as standard_model_parameters.txt
Funding:
This research was supported by the Institute for Basic Science, South Korea, under IBS-R028-D1.
This dataset contains the Matlab code used to solve the ordinary differential equations (ODE) (1-3) of the paper "Phase synchronization between culture and climate forcing" in Proceedings of the Royal Society, B. The prognostic equation describe the dynamics of population density, resources/carrying capacity and culture, respectively.
The Matlab code represents an Euler discretization of the stochastic ODEs and a Weibull-distributed noise distribution is assumed for the cultural innovation term. All images in the paper can be reproduced - at least in a statistical sense - by changing the parameters in the code by the parameters indicated in the figure caption of the manuscript.