In economic systems, the mix of products that countries make or export has been shown to be a strong leading indicator of economic growth. Hence, methods to characterize and predict the structure of the network connecting countries to the products that they export are relevant for understanding the dynamics of economic development. Here we study the presence and absence of industries in international and domestic economies and show that these networks are significantly nested. This means that the less filled rows and columns of these networks' adjacency matrices tend to be subsets of the fuller rows and columns. Moreover, we show that their nestedness remains constant over time and that it is sustained by both, a bias for industries that deviate from the networks' nestedness to disappear, and a bias for the industries that are missing according to nestedness to appear. This makes the appearance and disappearance of individual industries in each location predictable. We interpret the high level of nestedness observed in these networks in the context of the neutral model of development introduced by Hidalgo and Hausmann (2009). We show that the model can reproduce the high level of nestedness observed in these networks only when we assume a high level of heterogeneity in the distribution of capabilities available in countries and required by products. In the context of the neutral model, this implies that the high level of nestedness observed in these economic networks emerges as a combination of both, the complementarity of inputs and heterogeneity in the number of capabilities available in countries and required by products. The stability of nestedness in industrial ecosystems, and the predictability implied by it, demonstrates the importance of the study of network properties in the evolution of economic networks.

Multimedia learning research has established several principles for the effective design of audiovisual instruction. The image principle suggests that showing the instructor’s face in multimedia instruction does not promote learning, because the potential benefits from inducing social responses are outweighed by the cost of additional cognitive processing. In an 8-week observational field study (N = 2,951), online learners chose to watch video lectures either with or without the instructor’s face. Although learners who saw the face reported having a better lecture experience than those who chose not to see the face, 35% watched videos without the face for self-reported reasons including avoiding distraction. Building on these insights, the authors developed a video presentation style that strategically shows the face to reduce distraction while preserving occasional social cues. A 10-week field experiment (N = 12,468) compared the constant with the strategic presentation of the face and provided evidence consistent with the image principle. Cognitive load and perceived social presence were higher in the strategic than in the constant condition, but learning outcomes and attrition did not differ. Learners who expressed a verbal learning preference experienced substantially lower attrition and cognitive load with the constant than the strategic presentation. The findings highlight the value of social cues for motivation and caution against one-size-fits-all approaches to instructional design that fail to account for individual differences in multimedia instruction.

The growing presence of research shared on social media, coupled with the increase in freely available research, invites us to ask whether scientific articles shared on platforms like Twitter diffuse beyond the academic community. We explore a new method for answering this question by identifying 11 articles from two open access biology journals that were shared on Twitter at least 50 times and by analyzing the follower network of users who tweeted each article. We find that diffusion patterns of scientific articles can take very different forms, even when the number of times they are tweeted is similar. Our small case study suggests that most articles are shared within single-connected communities with limited diffusion to the public. The proposed approach and indicators can serve those interested in the public understanding of science, science communication, or research evaluation to identify when research diffuses beyond insular communities.

Citations and text analysis are both used to study the distribution and flow of ideas between researchers, fields and countries, but the resulting flows are rarely equal. We argue that the differences in these two flows capture a growing global inequality in the production of scientific knowledge. We offer a framework called ‘citational lensing’ to identify where citations should appear between countries but are absent given that what is embedded in their published abstract texts is highly similar. This framework also identifies where citations are overabundant given lower similarity. Our data come from nearly 20 million papers across nearly 35 years and 150 fields from the Microsoft Academic Graph. We find that scientific communities increasingly centre research from highly active countries while overlooking work from peripheral countries. This inequality is likely to pose substantial challenges to the growth of novel ideas.

Diversity tends to generate more and better ideas in social settings, ranging in scale from small-deliberative groups to tech-clusters and cities. Implicit in this research is that there are knowledge-generating benefits from diversity that comes from mixing different individuals, ideas, and perspectives. Here, we utilize agent-based modeling to examine the emergent outcomes resulting from the manipulation of how diversity is distributed and how knowledge is generated within communicative social structures. In the context of problem solving, we focus on cognitive diversity and its two forms: ability and knowledge. For diversity of ability, we find that local diversity (intermixing of different agents) performs best at all time scales. However, for diversity of knowledge, we find that local homogeneity performs best in the long-run, because it maintains global diversity, and thus the knowledge-generating ability of the group, for a longer period.