A huge amount of information, created and forwarded by millions of people with various characteristics, propagates through online social networks every day. Understanding the mechanisms of information diffusion over social networks is critical to various applications, including online advertisements and website management. Differently from most existing works in this area, we investigate information diffusion from an evolutionary game-theoretic perspective and try to reveal the underlying principles dominating the complex information diffusion process over heterogeneous social networks. Modeling the interactions among the heterogeneous users as a graphical evolutionary game, we derive the evolutionary dynamics and the evolutionarily stable states (ESSs) of the diffusion. The different payoffs of the heterogeneous users lead to different diffusion dynamics and ESSs among them, in accordance with the heterogeneity observed in real-world data sets. The theoretical results are confirmed by simulations. We also test the theory on the Twitter hashtag data set. We observe that the evolutionary dynamics fit the data well and can predict future diffusion data.

How information diffuses over social networks has attracted much attention from both industry and academics. Most of the existing works in this area are based on machine learning methods focusing on social network structure analysis and empirical data mining. However, the network users’ decisions, actions, and socioeconomic interactions are generally ignored in most existing works. In this chapter, we discuss an evolutionary game-theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we derive the information diffusion dynamics in complete networks and uniform-degree and nonuniform-degree networks. We find that the dynamics of information diffusion over these three kinds of networks are scale-free and the same as each other when the network scale is sufficiently large. To verify the theoretical analysis, we perform simulations of the information diffusion over synthetic networks and real-world Facebook networks. Moreover, we conduct an experiment on the Twitter hashtag data set, which shows that the game-theoretic model well fits and predicts information diffusion over real social networks.

Chapter four empirically assess the veracity of the argument on why leaders nationalize their oil sectors and establish operational national oil companies (NOCs). To test hypotheses derived from the theory’s implications, this chapter presents the findings from a method of statistical analysis that combines the cross-national NOC dataset with information elicited from structured interviews with oil experts. This technique -- Bayesian statistics -- allows for a holistic analysis of the determinants of nationalization that incorporates both quantitative and qualitative evidence on NOC formation. The results show the importance of information diffusion and perceptions of leader survival in the decision to nationalize the oil sector. The chapter then offers a case comparison of Iran and Saudi Arabia in the 1940s to show how information diffusion to a strong regime led to maintaining private ownership in Saudi Arabia in 1949, while knowledge about revenue sharing diffused to a weak regime led to nationalization in Iran in 1951.

Understanding network formation is essential to building a cohesive theory of network connectivity in the social relations that form historical regimes. Using diagrams of network structure in which nodes represent components and lines represent their interactions, we can recognize essential features of the interactive configurations that lead to patterns (institutions) and behaviors (regimes) and emergent properties. When we capture how agents interact and self-organize, we can infer structure; and knowing structure we can infer patterns of information transmission and thus collective behavior, including why system growth or breakdown follows a critical event. Theoretical network models – random, scale-free, small-world, and hub-and-spoke – capture these regularities and allow us to infer the principles underlying their construction and the trade-offs of stability and resilience. Knowing the patterns of structure and interaction, we gain a deeper grasp of two critically important and strongly correlated phenomena of contemporary political economy: the Great Divergence of East and West, and the global impact of China’s contemporary and unprecedented economic transformation.

Europe’s network structure had evolutionary advantages in its adaptability in innovation and resistance to collapse. While China’s imperial dynasties bequeathed political, social, and ideological foundations for national unity that endured largely intact for two millennia, behind that legacy resides a source of enduring structural weakness. Its system stability comes at a loss of flexibility. Fear of emerging chaos is memorialized in the narrative by which the Chinese Communist Party justifies its grip on power. An awareness of how vulnerability has led to failures predisposes China’s leaders to take insulating measures, e.g., censoring the Internet, constraining academic course content, imposing party oversight on enterprise, and hindering the acquisition of power and prestige that is independent of the regime. But bolstering system stability by strengthening centralized control mechanisms may undermine system resilience, reproducing the very weaknesses its designers seek to avoid and causing a massive disruption in the future. As these two great cultural systems begin to impinge on one another, network analysis has much to reveal about the choice of separation or integration that is before us.