Novel Multidimensional Models of Opinion Dynamics in Social Networks

Unlike many complex networks studied in the literature, social networks rarely exhibit unanimous behavior, or consensus. This requires a development of mathematical models that are sufficiently simple to be examined and capture, at the same time, the complex behavior of real social groups, where opinions and actions related to them may form clusters of different size. One such model, proposed by Friedkin and Johnsen, extends the idea of conventional consensus algorithm (also referred to as the iterative opinion pooling) to take into account the actors' prejudices, caused by some exogenous factors and leading to disagreement in the final opinions. In this paper, we offer a novel multidimensional extension, describing the evolution of the agents' opinions on several topics. Unlike the existing models, these topics are interdependent, and hence the opinions being formed on these topics are also mutually dependent. We rigorous examine stability properties of the proposed model, in particular, convergence of the agents' opinions. Although our model assumes synchronous communication among the agents, we show that the same final opinions may be reached "on average" via asynchronous gossip-based protocols.Comment: Accepted by IEEE Transaction on Automatic Control (to be published in May 2017

Modulating interaction times in an artificial society of robots

In a collaborative society, sharing information is advantageous for each individual as well as for the whole community. Maximizing the number of agent-to-agent interactions per time becomes an appealing behavior due to fast information spreading that maximizes the overall amount of shared information. However, if malicious agents are part of society, then the risk of interacting with one of them increases with an increasing number of interactions. In this paper, we investigate the roles of interaction rates and times (aka edge life) in artificial societies of simulated robot swarms. We adapt their social networks to form proper trust sub-networks and to contain attackers. Instead of sophisticated algorithms to build and administrate trust networks, we focus on simple control algorithms that locally adapt interaction times by changing only the robots' motion patterns. We successfully validate these algorithms in collective decision-making showing improved time to convergence and energy-efficient motion patterns, besides impeding the spread of undesired opinions

Policing cyberspace understanding online repression in Thailand and the Philippines

Social networking sites have become increasingly relevant in the study of democracy and culture in recent years. This study explores the interconnectedness of social networks, the imposition of state control, and management of social behavior by comparing various literature on the operation of repression in Thai and Philippine cyberspaces. It examines the overt and covert policing of daily interactions in digital environments and unpacks governmental technologies’ disciplinary mechanisms following Michel Foucault’s notion of government and biopolitical power. Subjugation in the context of social networks merits analysis for it sheds light on the practice of active and passive self-censorship - the former driven by the pursuit of a moral self-image and the latter by state-sponsored fear. In tracing various points of convergence and divergence in the practice of cyber control in Thailand and the Philippines, the study found newer domains of regulation of social behavior applicable to today’s democracies

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

An outstanding open problem is whether collective social phenomena occurring over short timescales can systematically reduce cultural heterogeneity in the long run, and whether offline and online human interactions contribute differently to the process. Theoretical models suggest that short-term collective behavior and long-term cultural diversity are mutually excluding, since they require very different levels of social influence. The latter jointly depends on two factors: the topology of the underlying social network and the overlap between individuals in multidimensional cultural space. However, while the empirical properties of social networks are well understood, little is known about the large-scale organization of real societies in cultural space, so that random input specifications are necessarily used in models. Here we use a large dataset to perform a high-dimensional analysis of the scientific beliefs of thousands of Europeans. We find that inter-opinion correlations determine a nontrivial ultrametric hierarchy of individuals in cultural space, a result unaccessible to one-dimensional analyses and in striking contrast with random assumptions. When empirical data are used as inputs in models, we find that ultrametricity has strong and counterintuitive effects, especially in the extreme case of long-range online-like interactions bypassing social ties. On short time-scales, it strongly facilitates a symmetry-breaking phase transition triggering coordinated social behavior. On long time-scales, it severely suppresses cultural convergence by restricting it within disjoint groups. We therefore find that, remarkably, the empirical distribution of individuals in cultural space appears to optimize the coexistence of short-term collective behavior and long-term cultural diversity, which can be realized simultaneously for the same moderate level of mutual influence

Superstars need social benefits : an experiment on network formation

We investigate contributions to the provision of public goods on a network when efficient provision requires the formation of a star network. We provide a theoretical analysis and study behavior is a controlled laboratory experiment. In a 2x2 design, we examine the effects of group size and the presence of (social) benefits for incoming links. We find that social benefits are highly important. They facilitate convergence to equilibrium networks and enhance the stability and efficiency of the outcome. Moreover, in large groups social benefits encourage the formation of superstars: star networks in which the core contributes more than expected in the stage-game equilibrium. We show that this result is predicted by a repeated game equilibrium