DOI: 10.5061/dryad.8sf7m0cwb
title: Data from: Phase synchronization between culture and climate forcing

description: 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.
description: 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.
description: # Data from: Phase synchronization between culture and climate forcing
 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&gt;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.