Dynamic Models of Appraisal Networks Explaining Collective Learning
Abstract
This paper proposes models of learning process in teams of individuals who collectively execute a sequence of tasks and whose actions are determined by individual skill levels and networks of interpersonal appraisals and influence. The closely-related proposed models have increasing complexity, starting with a centralized manager-based assignment and learning model, and finishing with a social model of interpersonal appraisal, assignments, learning, and influences. We show how rational optimal behavior arises along the task sequence for each model, and discuss conditions of suboptimality. Our models are grounded in replicator dynamics from evolutionary games, influence networks from mathematical sociology, and transactive memory systems from organization science.
Cite
@article{arxiv.1609.09546,
title = {Dynamic Models of Appraisal Networks Explaining Collective Learning},
author = {Wenjun Mei and Noah E. Friedkin and Kyle Lewis and Francesco Bullo},
journal= {arXiv preprint arXiv:1609.09546},
year = {2016}
}
Comments
A preliminary version has been accepted by the 53rd IEEE Conference on Decision and Control. The journal version has been submitted to IEEE Transactions on Automatic Control