Discordant benevolence: How and why people help others in the face of conflicting values

By: Sarah K. Cowan, Tricia C. Bruce, Brea L. Perry, Bridget Ritz*, Stuart Perrett* & Elizabeth M. Anderson*

Published in: Science Advances 8 (7), 2022

What happens when a request for help from friends or family members invokes conflicting values? In answering this question, we integrate and extend two literatures: support provision within social networks and moral decision-making. We examine the willingness of Americans who deem abortion immoral to help a close friend or family member seeking one. Using data from the General Social Survey and 74 in-depth interviews from the National Abortion Attitudes Study, we find that a substantial minority of Americans morally opposed to abortion would enact what we call discordant benevolence: providing help when doing so conflicts with personal values. People negotiate discordant benevolence by discriminating among types of help and by exercising commiseration, exemption, or discretion. This endeavor reveals both how personal values affect social support processes and how the nature of interaction shapes outcomes of moral decision-making.

Secrets and Social Networks

By: Sarah K. Cowan

Published in: Current Opinion in Psychology 31 (2020)

Secrets are information kept from others; they are relational. They shape the intimacy of our relationships, what we know of others and what we infer about the world. Recent research has promoted two models of voluntary secret disclosure. The first highlights deliberate and strategic disclosure to garner support and to avoid judgment. The second maintains strategic action but foregrounds that disclosures are made in contexts which shape who is in one’s social network and who may be the recipient of a disclosure. Work outside of this main vein examines the mechanisms and motivations to share others’ secrets as well as the potential consequences of doing so. The final avenue of inquiry in this review considers how keeping secrets can change (or avoid changing) the size and composition of the secret-keeper’s social network and what information is shared within it. Understanding how secrets spread within and form social networks informs work from public health to criminology to organizational management.

Estimating Personal Network Size with Non-random Mixing via Latent Kernels

By: Swupnil Sahai, Timothy Jones*, Sarah K. Cowan & Tian Zheng

Published in: Aiello L., Cherifi C., Cherifi H., Lambiotte R., Lió P., Rocha L. (eds) Complex Networks and Their Applications VII. Complex Networks 2018. Studies in Computational Intelligence, vol 812. Springer.

A major problem in the study of social networks is estimating the number of people an individual knows. However, there is no general method to account for barrier effects, a major source of bias in common estimation procedures. The literature describes approaches that model barrier effects, or non-random mixing, but they suffer from unstable estimates and fail to give results that agree with specialists’ knowledge. In this paper we introduce a model that builds off existing methods, imposes more structure, requires significantly fewer parameters, and yet allows for greater interpretability. We apply our model on responses gathered from a survey we designed and show that our conclusions better match what sociologists find in practice. We expect that this approach will provide more accurate estimates of personal network sizes and hence remove a significant hurdle in sociological research.