How good are citizen weather stations? Addressing a biased opinion

The UK is home to a dense network of Citizen Weather Stations (CWS) primarily set up by members of the public. The majority of these stations record air temperature, relative humidity and precipitation, amongst other variables, at sub-hourly intervals. This high resolution network could have benefits in many applications, but only if the data quality is well characterised. Here we present results from an intercomparison field study, in which popular CWS models were tested against Met Office standard equipment. The study identifies some common instrumental biases and their dependencies, which will help us to quantify and correct such biases from the CWS network.

Opinion leaders' role in innovation diffusion:a simulation study

This study investigates the critical role that opinion leaders (or influentials) play in the adoption process of new products. Recent existing reseach evidence indicates a limited effect of opinion leaders on diffusion processes, yet these studies take into account merely the network position of opinion leaders without addressing their influential power. Empirical findings of our study show that opinion leaders, in addition to having a more central network position, possess more accurate knowledge about a product and tend to be less susceptible to norms and more innovative. Experiments that address these attributes, using an agent-based model, demonstrate that opinion leaders increase the speed of the information stream and the adoption process itself. Furthermore, they increase the maximum adoption percentage. These results indicate that targeting opinion leaders remains a valuable marketing strategy.

Anisotropic opinion dynamics

We consider the process of opinion formation in a society of interacting agents, where there is a set B of socially accepted rules. In this scenario, we observed that agents, represented by simple feed-forward, adaptive neural networks, may have a conservative attitude (mostly in agreement with B) or liberal attitude (mostly in agreement with neighboring agents) depending on how much their opinions are influenced by their peers. The topology of the network representing the interaction of the society's members is determined by a graph, where the agents' properties are defined over the vertexes and the interagent interactions are defined over the bonds. The adaptability of the agents allows us to model the formation of opinions as an online learning process, where agents learn continuously as new information becomes available to the whole society (online learning). Through the application of statistical mechanics techniques we deduced a set of differential equations describing the dynamics of the system. We observed that by slowly varying the average peer influence in such a way that the agents attitude changes from conservative to liberal and back, the average social opinion develops a hysteresis cycle. Such hysteretic behavior disappears when the variance of the social influence distribution is large enough. In all the cases studied, the change from conservative to liberal behavior is characterized by the emergence of conservative clusters, i.e., a closed knitted set of society members that follow a leader who agrees with the social status quo when the rule B is challenged.