Evidence of direct and indirect reciprocity in network-structured economic games

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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Robust Bayesian analysis of animal networks subject to biases in sampling intensity and censoring

Sosa,

Brooke McElreath,

Redhead

et al. 2024

Preprint

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Data collection biases are a persistent issue for studies of social networks. This issue has been particularly important in Animal Social Network Analysis (ASNA), where data are unevenly sampled and such biases may potentially lead to incorrect inferences about animal social behavior. Here, we address the issue by developing a Bayesian generative model, which not only estimates network structure, but also explicitly accounts for sampling and censoring biases. Using a set of simulation experiments designed to reflect various sampling and observational biases encountered in real-world scenarios, we systematically validate our model and evaluate it's performance relative to other common ASNA methodologies. By accounting for differences in node-level censoring (i.e., the probability of missing an individual interaction.), our model permits the recovery of true latent social connections, even under a wide range of conditions where some key individuals are intermittently unobserved. Our model outperformed all other existing approaches and accurately captured network structure, as well as individual-level and dyad-level effects. Antithetically, permutation-based and simple linear regression aprroaches performed the worst across many conditions. These results highlight the advantages of generative network models for ASNA, as they offer greater flexibility, robustness, and adaptability to real-world data complexities. Our findings underscore the importance of generative models that jointly estimate network structure and adjust for measurement biases typical in empirical studies of animal social behaviour.

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Evidence of direct and indirect reciprocity in network-structured economic games

Cited by 3 publications

References 108 publications

Social structure and the evolutionary ecology of inequality

Social structure and the evolutionary ecology of inequality

A causal framework for the drivers of animal social network structure

Robust Bayesian analysis of animal networks subject to biases in sampling intensity and censoring

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