Disentangling influence over group speed and direction reveals multiple patterns of influence in moving meerkat groups

Averly, Baptiste; Sridhar, Vivek Hari; Demartsev, Vlad; Gall, Gabriella E. C.; Manser, Marta B.; Strandburg‐Peshkin, Ariana

doi:10.1038/s41598-022-17259-z

Cited by 7 publications

(11 citation statements)

References 56 publications

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“…Complete graphs are a suitable approximation for small, not too dense groups interacting via vision, where occlusions play a negligible role. Here, all individuals can be assumed to be able to perceive all group members and base their decision on the information gathered from the whole system [22,23]. Whereas small groups (small N) can be incorporated in our analysis of finite-sized systems, this case is at odds with the thermodynamic limit N → ∞.…”

Section: Discussionmentioning

confidence: 99%

“…The first obvious observation in terms of the problem considered here is that any non-zero value of ⟨k⟩ (any bias) leads to a non-vanishing value of x 0 at any finite temperature T. As it follows from equation (23), the value ⟨k⟩ = 0 is achieved either when all individuals are unbiased (ρ 0 = 1) or for equal mean strengths of oppositely biased individuals (ϵ…”

Section: Exact Solution For the Bi-modelmentioning

confidence: 97%

“…Although the models we will consider below can be analyzed for any spatial arrangements of spins, we will concentrate on the case of a complete graph, when each node is connected to all the other nodes. Such a choice may correspond to the situations when agents are able to be in contact independently of their proximity in space [22][23][24]; it will also enable us to obtain exact solutions for thermodynamics.…”

Section: The Model and Its Algorithmic Interpretationmentioning

confidence: 99%

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Individual bias and fluctuations in collective decision making: from algorithms to Hamiltonians

et al. 2023

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In this paper, we reconsider the spin model suggested recently to understand some features of collective decision making among higher organisms [A.T. Hartnett et al., Phys. Rev. Lett. 116 (2016) 038701]. Within the model, the state of an agent i is described by the pair of variables corresponding to its opinion Si = ±1 and a bias ωitowards any of the opposing values of Si. Collective decision making is interpreted as an approach to the equilibrium state within the non-linear voter model subject to a social pressure and a probabilistic algorithm. Here, we push such physical analogy further and give the statistical physics interpretation of the model, describing it in terms of the Hamiltonian of interaction and looking for the equilibrium state via explicit calculation of its partition function. We show that depending on the assumptions about the nature of social interactions two diﬀerent Hamiltonians can be formulated, which can be solved with diﬀerent methods. In such an interpretation the temperature serves as a measure of ﬂuctuations, not considered before in the original model. We ﬁnd exact solutions for the thermodynamics of the model on the complete graph. The general analytical predictions are conﬁrmed using individualbased simulations. The simulations allow us also to study the impact of system size and initial conditions in the collective decision making in ﬁnite-sized systems, in particular with respect to convergence to metastable states.

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Section: Discussionmentioning

confidence: 99%

Section: Exact Solution For the Bi-modelmentioning

confidence: 97%

Section: The Model and Its Algorithmic Interpretationmentioning

confidence: 99%

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Individual bias and fluctuations in collective decision making: from algorithms to Hamiltonians

et al. 2023

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“…For example, while mountain gorillas increase 'grunt' vocalizations to reflect a readiness to move [41], dominant silverbacks always take the lead in a certain direction, after which other group members follow [96]. Similarly, meerkats use 'moving' calls to increase movement speed; however, these calls have not been associated with changes in direction, suggesting that influencing movement direction may require an additional cue (likely visual) to specify the intended direction [39,94].…”

Section: The Role Of Movement Cues In Coordinating Behavioursmentioning

confidence: 99%

The relative contribution of acoustic signals versus movement cues in group coordination and collective decision-making

Liao,

Magrath,

Manser

et al. 2024

Phil. Trans. R. Soc. B

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To benefit from group living, individuals need to maintain cohesion and coordinate their activities. Effective communication thus becomes critical, facilitating rapid coordination of behaviours and reducing consensus costs when group members have differing needs and information. In many bird and mammal species, collective decisions rely on acoustic signals in some contexts but on movement cues in others. Yet, to date, there is no clear conceptual framework that predicts when decisions should evolve to be based on acoustic signals versus movement cues. Here, we first review how acoustic signals and movement cues are used for coordinating activities. We then outline how information masking, discrimination ability (Weber’s Law) and encoding limitations, as well as trade-offs between these, can identify which types of collective behaviours likely rely on acoustic signals or movement cues. Specifically, our framework proposes that behaviours involving the timing of events or expression of specific actions should rely more on acoustic signals, whereas decisions involving complex choices with multiple options (e.g. direction and destination) should generally use movement cues because sounds are more vulnerable to information masking and Weber’s Law effects. We then discuss potential future avenues of enquiry, including multimodal communication and collective decision-making by mixed-species animal groups. This article is part of the theme issue ‘The power of sound: unravelling how acoustic communication shapes group dynamic’.

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“…However, within-group heterogeneity in various traits has been found to give some individuals more influence over the group's movement direction than others. For example, body size [ 14 ], relative speed [ 15 , 16 ], navigational experience [ 17 ], sociability [ 15 , 18 ], motivation [ 18 ], social status [ 19 ], informational status [ 2 ] and spatial position [ 19 – 21 ] within the group have all been found to be correlated with social influence within groups. Driven by such a diverse range of mechanisms, collective behaviour inherently exhibits complex and multiscale temporal dynamics.…”

Section: Introductionmentioning

confidence: 99%

Inferring social influence in animal groups across multiple timescales

Sridhar

Davidson

Twomey

et al. 2023

Phil. Trans. R. Soc. B

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Many animal behaviours exhibit complex temporal dynamics, suggesting there are multiple timescales at which they should be studied. However, researchers often focus on behaviours that occur over relatively restricted temporal scales, typically ones that are more accessible to human observation. The situation becomes even more complex when considering multiple animals interacting, where behavioural coupling can introduce new timescales of importance. Here, we present a technique to study the time-varying nature of social influence in mobile animal groups across multiple temporal scales. As case studies, we analyse golden shiner fish and homing pigeons, which move in different media. By analysing pairwise interactions among individuals, we show that predictive power of the factors affecting social influence depends on the timescale of analysis. Over short timescales the relative position of a neighbour best predicts its influence and the distribution of influence across group members is relatively linear, with a small slope. At longer timescales, however, both relative position and kinematics are found to predict influence, and nonlinearity in the influence distribution increases, with a small number of individuals being disproportionately influential. Our results demonstrate that different interpretations of social influence arise from analysing behaviour at different timescales, highlighting the importance of considering its multiscale nature. This article is part of a discussion meeting issue ‘Collective behaviour through time’.

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Disentangling influence over group speed and direction reveals multiple patterns of influence in moving meerkat groups

Cited by 7 publications

References 56 publications

Individual bias and fluctuations in collective decision making: from algorithms to Hamiltonians

Individual bias and fluctuations in collective decision making: from algorithms to Hamiltonians

The relative contribution of acoustic signals versus movement cues in group coordination and collective decision-making

Inferring social influence in animal groups across multiple timescales

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