Ranking game on networks: The evolution of hierarchical society

Interacting with each other, individuals in a population form various social network topologies. Models of evolutionary games on networks provide insight into how the collective behaviors of structured populations are influenced by individual decision making and network topologies. In a hierarchical society, many social resources are allocated according to certain social rankings, such as class, social status, and social hierarchy. In this context, to climb the social ladder, individuals will try to improve their social ranking among the population. It is essential to understand the impact that changes in social ranking have on decision making, which very few literature discuss. To capture this social nature, a ranking game model on networks was introduced in this study. Three decision-making strategies - random, follow, and centrality-based - are introduced. Systematic numerical simulations of the different strategies are conducted on three social network topologies: random, small-world, and scale-free networks. The results reveal that the rankings of the whole population evolve differently with various dynamics in network topologies and social liquidities. The centrality strategy leads to relatively larger than average centrality, while the follow strategy tends to form networks with significantly larger edge density, indicating overall improvements for the whole population. Notably, the centrality strategy results in the least similarity, lowest survival rate, and highest liquidity, showing that this strategy allows larger social-structure changes with relatively better social mobility. In contrast, for the random and follow strategies, the social network becomes more rigid. However, in all cases, individuals are observed to appear in different ranking positions. This ranking game model could serve as a basis for further sophisticated ranking-related evolutionary games on social networks, with implications for policy-making in ranked social scenarios. (C) 2019 Elsevier B.V. All rights reserved.