An exploration for knowledge evolution affected by task assignment in a research and development team: perspectives of learning obtained through practice and communication

Authored by Bin Hu, Ni Xia, Fengzhen Jiang

Date Published: 2016

DOI: 10.1177/0037549716655609

Sponsors: Chinese National Natural Science Foundation

Platforms: AnyLogic

Model Documentation: Other Narrative Flow charts Pseudocode

Model Code URLs: http://journals.sagepub.com.ezproxy1.lib.asu.edu/doi/pdf/10.1177/0037549716655609

Abstract

Traditional learning theory seldom emphasizes the knowledge evolution process learnt through practices and communication (knowledge transfer), and also neglects the strategy of task assignment. This paper aims to integrate learning through practice and communication in the same system and explores how knowledge evolves under several different strategies of task assignment in a software research and development team. We model the relationship between knowledge and task with system dynamics, and establish communication between members using agent-based modeling techniques. Through simulation experiments, we find that communication greatly affects learning only in the early stage and this effect will gradually subside later. The simulation results of the strategy of task assignment indicate that random assignment performs better than any other single strategy. This finding may enlighten managers in integrating various strategies of task assignment in order to achieve better performance.

Tags

agent-based simulation Model Science Organization