CBBS-MS05

Data-driven approaches to understanding collective behavior

Tuesday, June 15 at 02:15am (PDT)
Tuesday, June 15 at 10:15am (BST)
Tuesday, June 15 06:15pm (KST)

SMB2021 Follow Monday (Tuesday) during the "MS05" time block.

Maria Bruna (University of Cambridge, United Kingdom), Ulrich Dobramysl (University of Cambridge, United Kingdom), Simon Garnier (New Jersey Institute of Technology, USA)

Description:

Mathematical models of collective motion have a wide range of applications including cell motility, animal swarms and pedestrian traffic. A common goal is to use the models to gain insight into how some given individual-based interactions give rise to the observed collective. These models typically include complex interactions between individuals and individual motility rules that may in fact be very hard to parameterize given experimental observations. In addition, experimental data can be very noisy due to the large number of individuals common in real systems and the difficulty extracting individual trajectories. This mini-symposium aims to bring together mathematical modelers and experimental researchers to explore the state of the art of parameterizing individual-based models and model selection.

Meg Crofoot

(Max Planck Institute of Animal Behavior & University of Konstanz, Germany)

"Locomotor compromise underlies coordination in heterogeneous groups on the move"

When members of a group differ in locomotor capacity, coordinating collective movement poses a challenge: some individuals may have to move faster (or slower) than their preferred speed to remain together. Such compromises have energetic repercussions yet research in collective behavior has largely neglected locomotor consensus costs. Here we integrate high-resolution tracking of wild baboon locomotion and movement with simulations to demonstrate that size-based variation in locomotor capacity poses an obstacle to collective movement. While all baboons modulate their gait and move-pause dynamics during collective movement, the costs of maintaining cohesion are disproportionately borne by smaller group members. Although consensus costs are not distributed equally, all group-mates do make locomotor compromises, suggesting a shared decision-making process drives the pace of collective movement in this highly despotic species. These results highlight the importance of considering how social dynamics and locomotor capacity interact to shape the movement ecology of group-living species.

Colin Torney

(School of Mathematics & Statistics, University of Glasgow, United Kingdom)

"Inferring microscale properties of interacting systems from macroscale observations"

Emergent dynamics of complex systems are observed throughout nature and society. The coordinated motion of bird flocks, the spread of opinions, fashions and fads, or the dynamics of an epidemic, are all examples of complex macroscale phenomena that arise from fine-scale interactions at the individual level. In many scenarios, observations of the system can only be made at the macroscale, while we are interested in creating and fitting models of the microscale dynamics. This creates a challenge for inference as a formal mathematical link between the micro and macro scale is rarely available. In this talk, I will describe an inferential framework that bypasses the need for a formal link between scales and instead uses sparse Gaussian process regression to learn the drift and diffusion terms of an empirical Fokker-Planck equation which describes the time evolution of the probability density of a macroscale variable. This gives access to the likelihood of the microscale properties of the system and a second Gaussian process can then be used to emulate the log-likelihood surface, allowing the implementation of a fast, adaptive MCMC sampler which iteratively refines the emulator when needed. The performance of the method can be illustrated by applying it to simple models of collective motion.

Yuko Ulrich

(Institute of Integrative Biology, ETH Zurich, Switzerland)

"Behavioral organization in heterogeneous groups of a social insect"

The composition of social groups has profound effects on their function, from collective decision-making to foraging efficiency. But few social systems afford sufficient control over group composition to precisely quantify its effects on individual and collective behavior. Here we combine experimental and theoretical approaches to study the effect of group composition on individual behavior and division of labor (DOL) in a social insect. Experimentally, we use automated behavioral tracking to monitor 120 colonies of clonal raider ants, with controlled variation in three key correlates of social insect behavior: genotype, age, and morphology. We find that each of these sources of heterogeneity generates a distinct pattern of behavioral organization, including the amplification or dampening of inherent behavioral differences in mixed colonies. Theoretically, we use a well-studied model of DOL to explore potential mechanisms underlying the experimental findings. We find that the simplest implementation of this model, which assumes that heterogeneous individuals differ only in response thresholds, could only partially recapitulate the empirically observed patterns of behavior. However, the full spectrum of observed phenomena was recapitulated by extending the model to incorporate two factors that are biologically meaningful but theoretically rarely considered: variation among workers in task performance efficiency and among larvae in task demand. Our results thus show that different sources of heterogeneity within social groups can generate different, sometimes non-intuitive, behavioral effects, but that relatively simple models can capture these dynamics and thereby begin to elucidate the basic organizational principles of DOL in social insects.

Adrien Blanchet

(Toulouse School of Economics, France)

"Mathematical model of disinformation"

For a couple of decades, the social network revolution has dramatically changed the way in which people access or share information. Information appears now to be decentralised, spreads faster and faster and seems difficult to control, predict or even understand. However the understanding of the spreading of information is absolutely crucial as it shapes the modern society: the opinion of citizens, the consumption of consumers, the behaviour of agents, or the political decisions. The problem of disinformation is fundamental and has been identified by the World Economic Forum as one of the threats to the economy. In this talk we will present a model of such phenomenon based on a game theory framework and using optimal transport and we will present an ongoing project. Co-authors: G. Carlier, F. Santambroggio, P. Mossay

Hosted by SMB2021 Follow

Virtual conference of the Society for Mathematical Biology, 2021.