  EVOC (for EVOlution of Culture) is a computer model of culture that enables
us to investigate how various factors such as barriers to cultural diffusion,
the presence and choice of leaders, or changes in the ratio of innovation to
imitation affect the diversity and effectiveness of ideas. It consists of
neural network based agents that invent ideas for actions, and imitate
neighbors' actions. The model is based on a theory of culture according to
which what evolves through culture is not memes or artifacts, but the internal
models of the world that give rise to them, and they evolve not through a
Darwinian process of competitive exclusion but a Lamarckian process involving
exchange of innovation protocols. EVOC shows an increase in mean fitness of
actions over time, and an increase and then decrease in the diversity of
actions. Diversity of actions is positively correlated with population size and
density, and with barriers between populations. Slowly eroding borders increase
fitness without sacrificing diversity by fostering specialization followed by
sharing of fit actions. Introducing a leader that broadcasts its actions
throughout the population increases the fitness of actions but reduces
diversity of actions. Increasing the number of leaders reduces this effect.
Efforts are underway to simulate the conditions under which an agent
immigrating from one culture to another contributes new ideas while still
fitting in.
