Expertise and confidence explain how social influence evolves along intellective tasks

• URL: http://arxiv.org/abs/2011.07168v1
• Date: Fri, 13 Nov 2020 23:48:25 GMT
• Title: Expertise and confidence explain how social influence evolves along intellective tasks
• Authors: Omid Askarisichani, Elizabeth Y. Huang, Kekoa S. Sato, Noah E. Friedkin, Francesco Bullo, Ambuj K. Singh
• Abstract summary: We study interpersonal influence in small groups of individuals who collectively execute a sequence of intellective tasks. We report empirical evidence on theories of transactive memory systems, social comparison, and confidences on the origins of social influence. We propose a cognitive dynamical model inspired by these theories to describe the process by which individuals adjust interpersonal influences over time.
• Score: 10.525352489242396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Discovering the antecedents of individuals' influence in collaborative environments is an important, practical, and challenging problem. In this paper, we study interpersonal influence in small groups of individuals who collectively execute a sequence of intellective tasks. We observe that along an issue sequence with feedback, individuals with higher expertise and social confidence are accorded higher interpersonal influence. We also observe that low-performing individuals tend to underestimate their high-performing teammate's expertise. Based on these observations, we introduce three hypotheses and present empirical and theoretical support for their validity. We report empirical evidence on longstanding theories of transactive memory systems, social comparison, and confidence heuristics on the origins of social influence. We propose a cognitive dynamical model inspired by these theories to describe the process by which individuals adjust interpersonal influences over time. We demonstrate the model's accuracy in predicting individuals' influence and provide analytical results on its asymptotic behavior for the case with identically performing individuals. Lastly, we propose a novel approach using deep neural networks on a pre-trained text embedding model for predicting the influence of individuals. Using message contents, message times, and individual correctness collected during tasks, we are able to accurately predict individuals' self-reported influence over time. Extensive experiments verify the accuracy of the proposed models compared to baselines such as structural balance and reflected appraisal model. While the neural networks model is the most accurate, the dynamical model is the most interpretable for influence prediction.

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