Garry Robins

Norm talk is verbal communication that explicitly states or implicitly implies a social norm. To investigate its ability to shape cultural dynamics, 2 types of norm talk were examined: injunction, which explicitly states what should be done, and gossip, which implies a norm by stating an action approved or disapproved of by the communicator. In 2 experiments, participants engaged in norm talk in repeated public goods games. Norm talk was found to help sustain cooperation relative to the control condition; immediately after every norm talk opportunity, cooperation spiked, followed by a gradual decline. Despite the macrolevel uniformity in their effects on cooperation, evidence suggests different microlevel mechanisms for the cooperation-enhancing effects of injunction and gossip. A 3rd study confirmed that both injunction and gossip sustain cooperation by making salient the norm of cooperation, but injunction also effects mutual verification of the communicated norm, whereas gossip emphasizes its reputational implications by linking cooperation to status conferral and noncooperation to reputational damage. A 4th experiment provided additional evidence that norm talk was superior to the promise of conditional cooperation in sustaining cooperation. Implications of the findings for cultural dynamics are discussed in terms of how feelings of shared morality, language-based interpersonal communication, and ritualization of norm communication contribute to social regulation. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

We discuss measuring and detecting influential observations and outliers in the context of exponential family random graph (ERG) models for social networks. We focus on the level of the nodes of the network and consider those nodes whose removal would result in changes to the model as extreme or “central” with respect to the structural features that “matter”. We construe removal in terms of two case-deletion strategies: the tie-variables of an actor are assumed to be unobserved, or the node is removed resulting in the induced subgraph. We define the difference in inferred model resulting from case deletion from the perspective of information theory and difference in estimates, in both the natural and mean-value parameterisation, representing varying degrees of approximation. We arrive at several measures of influence and propose the use of two that do not require refitting of the model and lend themselves to routine application in the ERGM fitting procedure. MCMC p values are obtained for testing how extreme each node is with respect to the network structure. The influence measures are applied to two well-known data sets to illustrate the information they provide. From a network perspective, the proposed statistics offer an indication of which actors are most distinctive in the network structure, in terms of not abiding by the structural norms present across other actors.

A major line of contemporary research on complex networks is based on the development of statistical models that specify the local motifs associated with macro-structural properties observed in actual networks. This statistical approach becomes increasingly problematic as network size increases. In the context of current research on efficient estimation of models for large network data sets, we propose a fast algorithm for maximum likelihood estimation (MLE) that affords a significant increase in the size of networks amenable to direct empirical analysis. The algorithm we propose in this paper relies on properties of Markov chains at equilibrium, and for this reason it is called equilibrium expectation (EE). We demonstrate the performance of the EE algorithm in the context of exponential random graph models (ERGMs) a family of statistical models commonly used in empirical research based on network data observed at a single period in time. Thus far, the lack of efficient computational strategies has limited the empirical scope of ERGMs to relatively small networks with a few thousand nodes. The approach we propose allows a dramatic increase in the size of networks that may be analyzed using ERGMs. This is illustrated in an analysis of several biological networks and one social network with 104,103 nodes.

Barnes, M. L., Ö. Bodin, A. M. Guerrero, R. J. McAllister, S. M. Alexander, and G. Robins. 2017. The social structural foundations of adaptation and transformation in social–ecological systems. Ecology and Society 22(4):16. https://doi.org/10.5751/ES-09769-220416