Most models of product adoption predict S-shaped adoption curves. Here we report results from two country-scale experiments in which we find linear adoption curves. We show evidence that the observed linear pattern is the result of active information-seeking behaviour: individuals actively pulling information from several central sources facilitated by modern Internet searches. Thus, a constant baseline rate of interest sustains product diffusion, resulting in a linear diffusion process instead of the S-shaped curve of adoption predicted by many diffusion models. The main experiment seeded 70 000 (48 000 in Experiment 2) unique voucher codes for the same product with randomly sampled nodes in a social network of approximately 43 million individuals with about 567 million ties. We find that the experiment reached over 800 000 individuals with 80% of adopters adopting the same product—a winner-take-all dynamic consistent with search engine driven rankings that would not have emerged had the products spread only through a network of social contacts. We provide evidence for (and characterization of) this diffusion process driven by active information-seeking behaviour through analyses investigating (a) patterns of geographical spreading; (b) the branching process; and (c) diffusion heterogeneity. Using data on adopters’ geolocation we show that social spreading is highly localized, while on-demand diffusion is geographically independent. We also show that cascades started by individuals who actively pull information from central sources are more effective at spreading the product among their peers.

 

Product diffusion through on-demand information-seeking behaviour
Christoph Riedl, Johannes Bjelland, Geoffrey Canright, Asif Iqbal, Kenth Engø-Monsen, Taimur Qureshi, Pål Roe Sundsøy, David Lazer

JRS Inteface
Published 21 February 2018.DOI: 10.1098/rsif.2017.0751

Source: rsif.royalsocietypublishing.org

The outbreak of epidemics, the rise of religious radicalization or the motivational influence of fellow students in classrooms are some of the issues that can be described as diffusion processes in heterogeneous groups. Understanding the role that interaction patterns between groups (e.g. homophily or segregation) play in the diffusion of certain traits or behaviours is a major challenge for contemporary societies. Here, we study the impact on diffusion processes of mixing (or, alternatively, segregating) two groups that present different sensitivities or propensities to contagion. We find non-monotonic effects of mixing and inefficient segregation levels, i.e. situations where a change in the mixing level can benefit both groups, e.g. where an increase in the mixing level can reduce the expected contagion levels in both groups. These findings can have fundamental consequences for the design of inclusion policies.

 

Mixing and diffusion in a two-type population
Segismundo S. Izquierdo, Luis R. Izquierdo, Dunia López-Pintado
Royal Society Open Science

Published 21 February 2018.DOI: 10.1098/rsos.172102

Source: rsos.royalsocietypublishing.org

The recent dramatic increase in online data availability has allowed researchers to explore human culture with unprecedented detail, such as the growth and diversification of language. In particular, it provides statistical tools to explore whether word use is similar across languages, and if so, whether these generic features appear at different scales of language structure. Here we use the Google Books N-grams dataset to analyze the temporal evolution of word usage in several languages. We apply measures proposed recently to study rank dynamics, such as the diversity of N-grams in a given rank, the probability that an N-gram changes rank between successive time intervals, the rank entropy, and the rank complexity. Using different methods, results show that there are generic properties for different languages at different scales, such as a core of words necessary to minimally understand a language. We also propose a null model to explore the relevance of linguistic structure across multiple scales, concluding that N-gram statistics cannot be reduced to word statistics. We expect our results to be useful in improving text prediction algorithms, as well as in shedding light on the large-scale features of language use, beyond linguistic and cultural differences across human populations.

 

Rank dynamics of word usage at multiple scales
José A. Morales, Ewan Colman, Sergio Sánchez, Fernanda Sánchez-Puig, Carlos Pineda, Gerardo Iñiguez, Germinal Cocho, Jorge Flores, Carlos Gershenson

Source: arxiv.org

Understanding the importance of links in transmitting information in a network can provide ways to hinder or postpone ongoing dynamical phenomena like the spreading of epidemic or the diffusion of information. In this work, we propose a new measure based on stochastic diffusion processes, the transmission centrality, that captures the importance of links by estimating the average number of nodes to whom they transfer information during a global spreading diffusion process. We propose a simple algorithmic solution to compute transmission centrality and to approximate it in very large networks at low computational cost. Finally we apply transmission centrality in the identification of weak ties in three large empirical social networks, showing that this metric outperforms other centrality measures in identifying links that drive spreading processes in a social network.

 

Link transmission centrality in large-scale social networks
Qian Zhang, Márton Karsai, Alessandro Vespignani

Source: arxiv.org

In both social systems and ecosystems there is a need to resolve potential conflicts between the interests of individuals and the collective interest of the community. The collective interests need to survive the turbulent dynamics of social and ecological interactions. To see how different systems with different sets of interactions have different degrees of robustness, we need to look at their different contingent histories. We analyze abstract artificial life models of such systems, and note that some prominent examples rely on explicitly ahistorical frameworks; we point out where analyses that ignore a contingent historical context can be fatally flawed. The mathematical foundations of Gaia theory are presented in a form whose very basic and general assumptions point to wide applicability across complex dynamical systems. This highlights surprising connections between robustness and accumulated contingent happenstance, regardless of whether Darwinian evolution is or is not implicated. Real-life studies highlight the role of history, and artificial life studies should do likewise.

 

Robustness and Contingent History: From Prisoner’s Dilemma to Gaia Theory

Inman Harvey

Artificial Life
Volume 24 | Issue 1 | Winter 2018
p.29-48

Source: www.mitpressjournals.org