Alice D. Bridges, Amanda Royka, Tara Wilson, Charlotte Lockwood, Jasmin Richter, Mikko Juusola & Lars Chittka
Nature (2024)

Culture refers to behaviours that are socially learned and persist within a population over time. Increasing evidence suggests that animal culture can, like human culture, be cumulative: characterized by sequential innovations that build on previous ones1. However, human cumulative culture involves behaviours so complex that they lie beyond the capacity of any individual to independently discover during their lifetime1,2,3. To our knowledge, no study has so far demonstrated this phenomenon in an invertebrate. Here we show that bumblebees can learn from trained demonstrator bees to open a novel two-step puzzle box to obtain food rewards, even though they fail to do so independently. Experimenters were unable to train demonstrator bees to perform the unrewarded first step without providing a temporary reward linked to this action, which was removed during later stages of training. However, a third of naive observer bees learned to open the two-step box from these demonstrators, without ever being rewarded after the first step. This suggests that social learning might permit the acquisition of behaviours too complex to ‘re-innovate’ through individual learning. Furthermore, naive bees failed to open the box despite extended exposure for up to 24 days. This finding challenges a common opinion in the field: that the capacity to socially learn behaviours that cannot be innovated through individual trial and error is unique to humans.

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Heather Z. Brooks, Mason A. Porter

We study the spreading dynamics of content on networks. To do this, we use a model in which content spreads through a bounded-confidence mechanism. In a bounded-confidence model (BCM) of opinion dynamics, the agents of a network have continuous-valued opinions, which they adjust when they interact with agents whose opinions are sufficiently close to theirs. The employed content-spread model introduces a twist into BCMs by using bounded confidence for the content spread itself. To study the spread of content, we define an analogue of the basic reproduction number from disease dynamics that we call an \emph{opinion reproduction number}. A critical value of the opinion reproduction number indicates whether or not there is an “infodemic” (i.e., a large content-spreading cascade) of content that reflects a particular opinion. By determining this critical value, one can determine whether or not an opinion will die off or propagate widely as a cascade in a population of agents. Using configuration-model networks, we quantify the size and shape of content dissemination using a variety of summary statistics, and we illustrate how network structure and spreading model parameters affect these statistics. We find that content spreads most widely when the agents have large expected mean degree or large receptiveness to content. When the amount of content spread only slightly exceeds the critical opinion reproduction number (i.e., the infodemic threshold), there can be longer dissemination trees than when the expected mean degree or receptiveness is larger, even though the total number of content shares is smaller.

Read the full article at: arxiv.org

Pablo Gallarta-Sáenz, Hugo Pérez-Martínez,  Jesús Gómez-Gardeñes

Chaos 34, 033106 (2024)

In this work, we analyze how reputation-based interactions influence the emergence of innovations. To do so, we make use of a dynamic model that mimics the discovery process by which, at each time step, a pair of individuals meet and merge their knowledge to eventually result in a novel technology of higher value. The way in which these pairs are brought together is found to be crucial for achieving the highest technological level. Our results show that when the influence of reputation is weak or moderate, it induces an acceleration of the discovery process with respect to the neutral case (purely random coupling). However, an excess of reputation is clearly detrimental, because it leads to an excessive concentration of knowledge in a small set of people, which prevents a diversification of the technologies discovered and, in addition, leads to societies in which a majority of individuals lack technical capabilities.

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Xiaofan Liang, César A. Hidalgo, Pierre-Alexandre Balland, Siqi Zheng, Jianghao Wang

Computers, Environment and Urban Systems Volume 109, April 2024, 102092

Urban outputs, from economy to innovation, are known to grow as a power of a city’s population. But, since large cities tend to be central in transportation and communication networks, the effects attributed to city size may be confounded with those of intercity connectivity. Here, we map intercity networks for the world’s two largest economies (the United States and China) to explore whether a city’s position in the networks of communication, human mobility, and scientific collaboration explains variance in a city’s patenting activity that is unaccounted for by its population. We find evidence that models incorporating intercity connectivity outperform population-based models and exhibit stronger predictive power for patenting activity, particularly for technologies of more recent vintage (which we expect to be more complex or sophisticated). The effects of intercity connectivity are more robust in China, even after controlling for population, GDP, and education, but not in the United States once adjusted for GDP and education. This divergence suggests distinct urban network dynamics driving innovation in these regions. In China, models with social media and mobility networks explain more heterogeneity in the scaling of innovation, whereas in the United States, scientific collaboration plays a more significant role. These findings support the significance of a city’s position within the intercity network in shaping its success in innovative activities.

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Georgios Tsekenis, Giulio Cimini, Marinos Kalafatis, Achille Giacometti, Tommaso Gili & Guido Caldarelli
Scientific Reports volume 14, Article number: 5266 (2024)

We define bipartite and monopartite relational networks of chemical elements and compounds using two different datasets of inorganic chemical and material compounds, as well as study their topology. We discover that the connectivity between elements and compounds is distributed exponentially for materials, and with a fat tail for chemicals. Compounds networks show similar distribution of degrees, and feature a highly-connected club due to oxygen . Chemical compounds networks appear more modular than material ones, while the communities detected reveal different dominant elements specific to the topology. We successfully reproduce the connectivity of the empirical chemicals and materials networks by using a family of fitness models, where the fitness values are derived from the abundances of the elements in the aggregate compound data. Our results pave the way towards a relational network-based understanding of the inherent complexity of the vast chemical knowledge atlas, and our methodology can be applied to other systems with the ingredient-composite structure.

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