Dynamical organization towards consensus in the Axelrod model on complex networks

Abstract

We analyze the dynamics toward cultural consensus in the Axelrod model on scale-free networks. By looking at the microscopic dynamics of the model, we are able to show how culture traits spread across different cultural features. We compare the diffusion at the level of cultural features to the growth of cultural consensus at the global level, finding important differences between these two processes. In particular, we show that even when most of the cultural features have reached macroscopic consensus, there are still no signals of globalization. Finally, we analyze the topology of consensus clusters both for global culture and at the feature level of representation.

Reconciling long-term cultural diversity and short-term collective social behavior

“…This simulates N individuals giving completely random answers to the questionnaire, and does not depend on the empirically observed answers. This provides a unique random benchmark against which all sampled groups can be compared, and corresponds to the usual initial specification in Axelrod's model (31) and modifications (2,(38)(39)(40)(41)(42)(43)50). A second type of randomization ("shuffled answers") consists in randomly shuffling, for each of the F questions in the questionnaire, the real answers given by the N individuals of the group considered.…”

“…This result inverts the frequent expectation that initial conditions are less relevant than model specifications, and actually makes the latter less important (with the advantage of reducing the arbitrariness that, in principle, underlies the mathematical definition of any model). Quite unexpectedly, a range of more complicated scenarios [such as models with different interaction probabilities (2,31,38,39), different network topologies (2,31,37,42), coevolution of networks and opinions (43-47), cultural drift (40, 41, 48), higher-order interactions (2,38,49), and external sources of information (50)] lead to results that are essentially equivalent (or directly mapped) to what is obtained simply assuming that individuals are subject to dyadic interactions on a complete graph (see SI Text). We will therefore illustrate our results in this simple case, which corresponds to the assumption that social interactions are infinite-ranged, and only limited by the bounded confidence hypothesis.…”

“…In models, the assumption becomes explicit and indeed individuals are always initially assigned uniformly random traits. While many contributions have studied how the process of cultural convergence under social influence changes if different dynamical hypotheses are introduced (2,31,(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50), little is known about the effects of different initial cultural specifications. To fill this gap, here we first perform an empirical analysis of the structure of high-dimensional cultural vectors in a large set of individuals, and then use such vectors as seeds for dynamical models.…”

Setting the agenda: Different strategies of a Mass Media in a model of cultural dissemination

“…The role of the social contact network in the dynamics with a fixed MM was also investigated in [14], where the effects of intra and inter-links of a social network with community structure was analyzed. In [15], the microscopic dynamics toward equilibrium was analyzed when the underlying network is scale-free in its degree distribution. In the same modeling scenario, a model of cross-cultural interaction through Mass Media interactions was investigated in [16], where two (fully adaptive) Mass Media act over two different interconnected populations.…”

Coevolutionary network approach to cultural dynamics controlled by intolerance

“…The (non-equilibrium) phase transition [12] between globalization and multiculturalism was first studied for a square planar geometry [7,13,14], but soon other network structures of social links [15][16][17] were considered, as well as the effects of different types of noise ("cultural drift") [18,19], external fields (modeling e.g. influential media, or information feedback) [20][21][22][23], and global or local non-uniform couplings [24,25].…”