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Register a new userThe Effects of Communication Burstiness on Consensus Formation and Tipping Points in Social Dynamics
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Current models for opinion dynamics typically utilize a Poisson process for speaker selection, making the waiting time between events exponentially distributed. Human interaction tends to be bursty, though, having higher probabilities of either extremely short waiting times or long periods of silence. To quantify the burstiness effects on the dynamics of social models, we place in competition two groups exhibiting different speakers waiting-time distributions. These competitions are implemented in the binary Naming Game, and show that the relevant aspect of the waiting-time distribution is the density of the head rather than that of the tail. We show that even with identical mean waiting times, a group with a higher density of short waiting times is favored in competition over the other group. This effect remains in the presence of nodes holding a single opinion that never changes, as the fraction of such committed individuals necessary for achieving consensus decreases dramatically when they have a higher head density than the holders of the competing opinion. Finally, to quantify differences in burstiness, we introduce the expected number of small-time activations and use it to characterize the early-time regime of the system.
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How can minorities of regular individuals overturn social conventions? Theoretical and empirical studies have proposed that when a committed minority reaches a critical group size-ranging from 10% of the population up to 40%-a cascade of behaviour change rapidly increases the acceptance of the minority view and apparently stable social norms can be overturned. However, several observations suggest that much smaller groups may be sufficient to bring the system to a tipping point. Here, we generalise a model previously used for both theoretical and empirical investigations of tipping points in social convention and find that the critical mass necessary to trigger behaviour change is dramatically reduced if individuals are less prone to change their views, i.e., are more resistant to social influence. We show that groups smaller than 3% of the population are effective on different kinds of social networks, both when pairwise or group interactions are considered, and in a broad region of the parameter space. In some cases, even groups as small as 0.3% may overturn the current social norm. Our findings reconcile the numerous observational accounts of rapid change in social convention triggered by committed minorities with the apparent difficulty of establishing such large minorities in the first place. We anticipate that they will be of interest for both researchers and practitioners interested in understanding the phenomenon of norm change, and in designing interventions aimed at contrasting such global challenges as climate change and vaccine hesitancy.
The social network maintained by a focal individual, or ego, is intrinsically dynamic and typically exhibits some turnover in membership over time as personal circumstances change. However, the consequences of such changes on the distribution of an egos network ties are not well understood. Here we use a unique 18-month data set that combines mobile phone calls and survey data to track changes in the ego networks and communication patterns of students making the transition from school to university or work. Our analysis reveals that individuals display a distinctive and robust social signature, captured by how interactions are distributed across different alters. Notably, for a given ego, these social signatures tend to persist over time, despite considerable turnover in the identity of alters in the ego network. Thus as new network members are added, some old network members are either replaced or receive fewer calls, preserving the overall distribution of calls across network members. This is likely to reflect the consequences of finite resources such as the time available for communication, the cognitive and emotional effort required to sustain close relationships, and the ability to make emotional investments.
The basic interaction unit of many dynamical systems involves more than two nodes. In such situations where networks are not an appropriate modelling framework, it has recently become increasingly popular to turn to higher-order models, including hypergraphs. In this paper, we explore the non-linear dynamics of consensus on hypergraphs, allowing for interactions within hyperedges of any cardinality. After discussing the different ways in which non-linearities can be incorporated in the dynamical model, building on different sociological theories, we explore its mathematical properties and perform simulations to investigate them numerically. After focussing on synthetic hypergraphs, namely on block hypergraphs, we investigate the dynamics on real-world structures, and explore in detail the role of involvement and stubbornness on polarisation.
A number of human activities exhibit a bursty pattern, namely periods of very high activity that are followed by rest periods. Records of this process generate time series of events whose inter-event times follow a probability distribution that displays a fat tail. The grounds for such phenomenon are not yet clearly understood. In the present work we use the freely available Wikipedias editing records to tackle this question by measuring the level of burstiness, as well as the memory effect of the editing tasks performed by different editors in different pages. Our main finding is that, even though the editing activity is conditioned by the circadian 24 hour cycle, the conditional probability of an activity of a given duration at a given time of the day is independent from the latter. This suggests that the human activity seems to be related to the high cost of starting an action as opposed to the much lower cost of continuing that action.
Opinion formation is an important element of social dynamics. It has been widely studied in the last years with tools from physics, mathematics and computer science. Here, a continuous model of opinion dynamics for multiple possible choices is analysed. Its main features are the inclusion of disagreement and possibility of modulating information, both from one and multiple sources. The interest is in identifying the effect of the initial cohesion of the population, the interplay between cohesion and information extremism, and the effect of using multiple sources of information that can influence the system. Final consensus, especially with external information, depends highly on these factors, as numerical simulations show. When no information is present, consensus or segregation is determined by the initial cohesion of the population. Interestingly, when only one source of information is present, consensus can be obtained, in general, only when this is extremely mild, i.e. there is not a single opinion strongly promoted, or in the special case of a large initial cohesion and low information exposure. On the contrary, when multiple information sources are allowed, consensus can emerge with an information source even when this is not extremely mild, i.e. it carries a strong message, for a large range of initial conditions.
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