(De)composing sociality: disentangling individual-specific from dyad-specific propensities to interact

Neumann, Christof; Fischer, Julia

doi:10.1101/2023.08.15.549768

Cited by 2 publications

(4 citation statements)

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“…Nevertheless, we investigated convergent validity by looking at the correlation between dyadic contact sit and grooming and provided evidence that they are highly positively correlated. Finally, we recommend disentangling the effect of individual personalities from dyad-specific measures on cooperation in future studies [ 101 ].…”

Section: Discussionmentioning

confidence: 99%

Personality heterophily and friendship as drivers for successful cooperation

Bhattacharjee,

Waasdorp,

Middelburg

et al. 2024

Proc. R. Soc. B.

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Cooperation is widespread and arguably a pivotal evolutionary force in maintaining animal societies. Yet, proximately, what underlying motivators drive individuals to cooperate remains relatively unclear. Since ‘free-riders’ can exploit the benefits by cheating, selecting the right partner is paramount. Such decision rules need not be based on complex calculations and can be driven by cognitively less-demanding mechanisms, like social relationships (e.g. kinship, non-kin friendships, dyadic tolerance), social status (e.g. dominance hierarchies) and personalities (social and non-social traits); however, holistic evidence related to those mechanisms is scarce. Using the classical ‘loose-string paradigm’, we tested cooperative tendencies of a hierarchical primate, the long-tailed macaque ( Macaca fascicularis ). We studied three groups ( n = 21) in their social settings, allowing partner choice. We supplemented cooperation with observational and experimental data on social relationships, dominance hierarchies and personality. Friendship and dissimilarities in non-social ‘exploration’ and ‘activity–sociability’ personality traits predicted the likelihood of cooperative dyad formation. Furthermore, the magnitude of cooperative success was positively associated with friendship, low rank-distance and dissimilarity in the activity–sociability trait. Kinship did not affect cooperation. While some findings align with prior studies, the evidence of (non-social) personality heterophily promoting cooperation may deepen our understanding of the proximate mechanisms and, broadly, the evolution of cooperation.

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Section: Discussionmentioning

confidence: 99%

Personality heterophily and friendship as drivers for successful cooperation

Bhattacharjee,

Waasdorp,

Middelburg

et al. 2024

Proc. R. Soc. B.

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“…In the empirical study, genetic relatedness could for instance be estimated alongside the other components of the model (Koller & Friedman, 2009). Similarly, dominance relationships and affiliation may be modelled as latent variables in multiplex network models (De Bacco et al, 2023; Neumann & Fischer, 2023a, 2023b; Redhead et al, 2024; Redhead, McElreath, et al, 2023; Young et al, 2020). In this regard, statistical model 4 can be considered an intermediate step towards more realistic estimators.…”

Section: Discussionmentioning

confidence: 99%

“…However, the utility of permutation methods for social network analysis has been challenged, as they carry several, fundamental, flaws (see Hart et al, 2022; McElreath, 2020; Ross et al, 2023; Weiss et al, 2021). Recent methodological work proposes to solve the issues of noise and biases introduced by the sampling process, by defining the true interaction rates as parameters in a Bayesian Model (Duboscq et al, 2023; Hart et al, 2023; Neumann & Fischer, 2023a; Redhead, McElreath, et al, 2023; Ross et al, 2023; Young et al, 2020). We will return to this idea in detail, further below.…”

Section: Introductionmentioning

confidence: 99%

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A causal framework for the drivers of animal social network structure

Kawam,

Ostner,

McElreath

et al. 2024

Preprint

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A major goal of behavioural ecology is to explain how phenotypic and ecological factors shape the social relationships that animals form with one another. This inferential task is notoriously challenging. The social networks of interest are generally not observed, but must be approximated from behavioural samples. Moreover, these data are highly dependent: the observed network edges correlate with one another, due to biological and sampling processes. Failing to account for the resulting uncertainty and biases can lead to dysfunctional statistical procedures, and thus to incorrect results. Here, we argue that these problems should be understood— and addressed—as problems of causal inference. For this purpose, we introduce a Bayesian causal modelling framework that explicitly defines the links between the target interaction network, its causes, and the data. We illustrate the mechanics of our framework with simulation studies and an empirical example. First, we encode causal effects of individual-, dyad-, and group-level features on social interactions using Directed Acyclic Graphs and Structural Causal Models. These quantities are the objects of inquiry, ourestimands. Second, we developestimatorsfor these effects— namely, Bayesian multilevel extensions of the Social Relations Model. Third, we recover the structural parameters of interest, map statistical estimates to the underlying causal structures, and compute causalestimatesfrom the joint posterior distribution. Throughout the manuscript, we develop models layer by layer, thereby illustrating an iterative workflow for causal inference in social networks. We conclude by summarising this workflow as a set of seven steps, and provide practical recommendations.

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(De)composing sociality: disentangling individual-specific from dyad-specific propensities to interact

Cited by 2 publications

References 47 publications

Personality heterophily and friendship as drivers for successful cooperation

Personality heterophily and friendship as drivers for successful cooperation

A causal framework for the drivers of animal social network structure

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