Competition among memes in a world with limited attention

The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.

Topical interests and the mitigation of search engine bias

Search engines have become key media for our scientific, economic, and social activities by enabling people to access information on the web despite its size and complexity. On the down side, search engines bias the traffic of users according to their page ranking strategies, and it has been argued that they create a vicious cycle that amplifies the dominance of established and already popular sites. This bias could lead to a dangerous monopoly of information. We show that, contrary to intuition, empirical data do not support this conclusion; popular sites receive far less traffic than predicted. We discuss a model that accurately predicts traffic data patterns by taking into consideration the topical interests of users and their searching behavior in addition to the way search engines rank pages. The heterogeneity of user interests explains the observed mitigation of search engines' popularity bias.

Efficient and scalable Pareto optimization by evolutionary local selection algorithms

Local selection is a simple selection scheme in evolutionary computation. Individual fitnesses are accumulated over time and compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. Local selection, coupled with fitness functions stemming from the consumption of finite shared environmental resources, maintains diversity in a way similar to fitness sharing. However, it is more efficient than fitness sharing and lends itself to parallel implementations for distributed tasks. While local selection is not prone to premature convergence, it applies minimal selection pressure to the population. Local selection is, therefore, particularly suited to Pareto optimization or problem classes where diverse solutions must be covered. This paper introduces ELSA, an evolutionary algorithm employing local selection and outlines three experiments in which ELSA is applied to multiobjective problems: a multimodal graph search problem, and two Pareto optimization problems. In all these experiments, ELSA significantly outperforms other well-known evolutionary algorithms. The paper also discusses scalability, parameter dependence, and the potential distributed applications of the algorithm.