Published on Sat Feb 09 2019

Network connectivity dynamics affect the evolution of culturally transmitted variants

José Segovia Martín, Bradley Walker, Nicolas Fay, Monica Tamariz

In a fully connected social network, connectivity dynamics, alone and in interaction with different cognitive biases, affect the evolution of cultural variants. We show that connectivity dynamics affect the time-course of variant spread, with lower connectivity slowing down convergence. We also show that, compared to a neutral evolutionary model, content bias accelerates convergence.

0
0
0
Abstract

The distribution of cultural variants in a population is shaped by both neutral evolutionary dynamics and by selection pressures, which include several individual cognitive biases, demographic factors and social network structures. The temporal dynamics of social network connectivity, i.e. the order in which individuals in a population interact with each other, has been largely unexplored. In this paper we investigate how, in a fully connected social network, connectivity dynamics, alone and in interaction with different cognitive biases, affect the evolution of cultural variants. Using agent-based computer simulations, we manipulate population connectivity dynamics (early, middle and late full-population connectivity); content bias, or a preference for high-quality variants; coordination bias, or whether agents tend to use self-produced variants (egocentric bias), or to switch to variants observed in others (allocentric bias); and memory size, or the number of items that agents can store in their memory. We show that connectivity dynamics affect the time-course of variant spread, with lower connectivity slowing down convergence of the population onto a single cultural variant. We also show that, compared to a neutral evolutionary model, content bias accelerates convergence and amplifies the effects of connectivity dynamics, whilst larger memory size and coordination bias, especially egocentric bias, slow down convergence. Furthermore, connectivity dynamics affect the frequency of high quality variants (adaptiveness), with late connectivity populations showing bursts of rapid change in adaptiveness followed by periods of relatively slower change, and early connectivity populations following a single-peak evolutionary dynamic. In this way, we provide for the first time a direct connection between the order of agents' interactions and punctuational evolution.

Sun Jun 29 2014
NLP
Human Communication Systems Evolve by Cultural Selection
A role for selection in cultural evolutionary dynamics is less clear. Alternative models include cultural selection, which assumes variant adoption is biased. Theoretical models of human communication argue that during conversation interlocutors are biased to adopt the same labels and other aspects of linguistic representation.
0
0
0
Thu Feb 27 2014
NLP
Information Evolution in Social Networks
Social networks readily transmit information, albeit with less than perfect fidelity. A meme's mutation rate characterizes the population distribution of its variants. Subpopulations of the social network can preferentially transmit a specific variant of a meme.
0
0
0
Wed Jul 30 2014
Neural Networks
Population fluctuation promotes cooperation in networks
Cooperation flourishes for a wide variety of initial conditions. This model does not require agents to have memory, recognition of other agents or other cognitive abilities.
0
0
0
Thu Mar 30 2017
NLP
Neutral evolution and turnover over centuries of English word popularity
We test Neutral models against the evolution of English word frequency and vocabulary at the population scale. We find that a modified two-stage Neutral model does replicate the static and dynamic properties of the corpus data. This mode -- a smaller neutral model within a larger neutral model -- could represent more broadly those
0
0
0
Tue Oct 15 2013
Artificial Intelligence
A Computational Model of Two Cognitive Transitions Underlying Cultural Evolution
We tested the computational feasibility of the proposal that open-ended cultural evolution was made possible by two cognitive transitions. These transitions were simulated in EVOC, an agent-based model of cultural evolution, in which the fitness of agents' actions increases as they invent ideas for new actions.
0
0
0
Mon Sep 21 2020
Machine Learning
Modeling the Evolution of Networks as Shrinking Structural Diversity
This article reviews and evaluates models of network evolution based on the notion of structural diversity. We show that in all three cases, a dominant trend towards shrinking diversity is apparent.
0
0
0