EVOC: A Computer Model of the Evolution of Culture
This work provides an incremental computational model for understanding cultural evolution, relevant to researchers in anthropology and social sciences.
The authors tackled the problem of modeling cultural evolution by developing EVOC, a neural network-based agent model that replicates earlier findings on fitness and diversity dynamics, showing that slowly eroding borders maximize diversity and square worlds increase it, while broadcasting leaders boost fitness but reduce diversity.
EVOC is a computer model of the EVOlution of Culture. It consists of neural network based agents that invent ideas for actions, and imitate neighbors' actions. EVOC replicates using a different fitness function the results obtained with an earlier model (MAV), including (1) an increase in mean fitness of actions, and (2) an increase and then decrease in the diversity of actions. Diversity of actions is positively correlated with number of needs, population size and density, and with the erosion of borders between populations. Slowly eroding borders maximize diversity, fostering specialization followed by sharing of fit actions. Square (as opposed to toroidal) worlds also exhibit higher diversity. Introducing a leader that broadcasts its actions throughout the population increases the fitness of actions but reduces diversity; these effects diminish the more leaders there are. Low density populations have less fit ideas but broadcasting diminishes this effect.