Nicolas Perony

Bats are able to maintain long-term social relationships despite the high fission–fusion dynamics of their groups

Elephants, dolphins, as well as some carnivores and primates maintain social links despite their frequent splitting and merging in groups of variable composition, a phenomenon known as fission–fusion. Information on the dynamics of social links and interactions among individuals is of high importance to the understanding of the evolution of animal sociality, including that of humans. However, detailed long-term data on such dynamics in wild mammals with fully known demography and kin structures are scarce. Applying a weighted network analysis on 20 500 individual roosting observations over 5 years, we show that in two wild Bechsteins bat colonies with high fission–fusion dynamics, individuals of different age, size, reproductive status and relatedness maintain long-term social relationships. In the larger colony, we detected two stable subunits, each comprising bats from several family lineages. Links between these subunits were mainly maintained by older bats and persisted over all years. Moreover, we show that the full details of the social structure become apparent only when large datasets are used. The stable multi-level social structures in Bechsteins bat colonies resemble that of elephants, dolphins and some primates. Our findings thus may shed new light on the link between social complexity and social cognition in mammals.

The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy

What is the role of social interactions in the creation of price bubbles? Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. Here, we focus on Bitcoin, the most popular cryptocurrency. Bitcoin has experienced periods of rapid increase in exchange rates (price) followed by sharp decline; we hypothesize that these fluctuations are largely driven by the interplay between different social phenomena. We thus quantify four socio-economic signals about Bitcoin from large datasets: price on online exchanges, volume of word-of-mouth communication in online social media, volume of information search and user base growth. By using vector autoregression, we identify two positive feedback loops that lead to price bubbles in the absence of exogenous stimuli: one driven by word of mouth, and the other by new Bitcoin adopters. We also observe that spikes in information search, presumably linked to external events, precede drastic price declines. Understanding the interplay between the socio-economic signals we measured can lead to applications beyond cryptocurrencies to other phenomena that leave digital footprints, such as online social network usage.

How Random Is Social Behaviour? Disentangling Social Complexity through the Study of a Wild House Mouse Population

Out of all the complex phenomena displayed in the behaviour of animal groups, many are thought to be emergent properties of rather simple decisions at the individual level. Some of these phenomena may also be explained by random processes only. Here we investigate to what extent the interaction dynamics of a population of wild house mice (Mus domesticus) in their natural environment can be explained by a simple stochastic model. We first introduce the notion of perceptual landscape, a novel tool used here to describe the utilisation of space by the mouse colony based on the sampling of individuals in discrete locations. We then implement the behavioural assumptions of the perceptual landscape in a multi-agent simulation to verify their accuracy in the reproduction of observed social patterns. We find that many high-level features – with the exception of territoriality – of our behavioural dataset can be accounted for at the population level through the use of this simplified representation. Our findings underline the potential importance of random factors in the apparent complexity of the mices social structure. These results resonate in the general context of adaptive behaviour versus elementary environmental interactions.

Why Did the Meerkat Cross the Road? Flexible Adaptation of Phylogenetically-Old Behavioural Strategies to Modern-Day Threats

Risk-sensitive adaptive spatial organisation during group movement has been shown to efficiently minimise the risks associated with external ecological threats. Whether animals can draw on such behaviours when confronted with man-made threats is generally less clear. We studied road-crossing in a wild, but habituated, population of meerkats living in the Kalahari Desert, South Africa. We found that dominant females, the core member in meerkat social systems, led groups to the road significantly more often than subordinates, yet were consistently less likely to cross first. Our results suggest that a reshuffling occurs in progression order when meerkat groups reach the road. By employing a simple model of collective movement, we have shown that risk aversion alone may be sufficient to explain this reshuffling, but that the risk aversion of dominant females toward road crossing is significantly higher than that of subordinates. It seems that by not crossing first, dominant females avoid occupying the most risky, exposed locations, such as at the front of the group – a potential selfish strategy that also promotes the long-term stability and hence reproductive output of their family groups. We argue that our findings support the idea that animals can flexibly apply phylogenetically-old behavioural strategies to deal with emerging modern-day problems.

ENHANCING CONSENSUS UNDER OPINION BIAS BY MEANS OF HIERARCHICAL DECISION MAKING

We study the role of hierarchical structures in a simple model of collective consensus formation based on the bounded confidence model with continuous individual opinions. For the particular variation of this model considered in this paper, we assume that a bias toward an extreme opinion is introduced whenever two individuals interact and form a common decision. As a simple proxy for hierarchical social structures, we introduce a two-step decision making process in which in the second step groups of like-minded individuals are replaced by representatives once they have reached local consensus, and the representatives in turn form a collective decision in a downstream process. We find that the introduction of such a hierarchical decision making structure can improve consensus formation, in the sense that the eventual collective opinion is closer to the true average of individual opinions than without it. In particular, we numerically study how the size of groups of like-minded individuals being represented by delegate individuals affects the impact of the bias on the final population-wide consensus. These results are of interest for the design of organizational policies and the optimization of hierarchical structures in the context of group decision making.

Use of a Four-Tiered Graph to Parse the Factors Leading to Phenotypic Clustering in Bacteria: A Case Study Based on Samples from the Aletsch Glacier

An understanding of bacterial diversity and evolution in any environment requires knowledge of phenotypic diversity. In this study, the underlying factors leading to phenotypic clustering were analyzed and interpreted using a novel approach based on a four-tiered graph. Bacterial isolates were organized into equivalence classes based on their phenotypic profile. Likewise, phenotypes were organized in equivalence classes based on the bacteria that manifest them. The linking of these equivalence classes in a four-tiered graph allowed for a quick visual identification of the phenotypic measurements leading to the clustering patterns deduced from principal component analyses. For evaluation of the method, we investigated phenotypic variation in enzyme production and carbon assimilation of members of the genera Pseudomonas and Serratia, isolated from the Aletsch Glacier in Switzerland. The analysis indicates that the genera isolated produce at least six common enzymes and can exploit a wide range of carbon resources, though some specialist species within the pseudomonads were also observed. We further found that pairwise distances between enzyme profiles strongly correlate with distances based on carbon profiles. However, phenotypic distances weakly correlate with phylogenetic distances. The method developed in this study facilitates a more comprehensive understanding of phenotypic clustering than what would be deduced from principal component analysis alone.

Hierarchical Consensus Formation Reduces The Influence Of Opinion Bias.

We study the role of hierarchical structures in a simple model of collective consensus formation based on the bounded confidence model with continuous individual opinions. For the particular variation of this model considered in this paper, we assume that a bias towards an extreme opinion is introduced whenever two individuals interact and form a common decision. As a simple proxy for hierarchical social structures, we introduce a two-step decision making process in which in the second step groups of like-minded individuals are replaced by representatives once they have reached local consensus, and the representatives in turn form a collective decision in a downstream process. We find that the introduction of such a hierarchical decision making structure can improve consensus formation, in the sense that the eventual collective opinion is closer to the true average of individual opinions than without it. In particular, we numerically study how the size of groups of like-minded individuals being represented by delegate individuals affects the impact of the bias on the final population-wide consensus. These results are of interest for the design of organisational policies and the optimisation of hierarchical structures in the context of group decision making.