Structure, learning, and the speed of innovating: a two-phase model of collective innovation using agent based modeling

Authored by Xing Zhong, Salih Zeki Ozdemir

Date Published: 2010-10

DOI: 10.1093/icc/dtq020

Sponsors: Early Career Research Grant of Australian School of Business, UNSW

Platforms: No platforms listed

Model Documentation: Other Narrative Pseudocode Mathematical description

Model Code URLs: Model code not found

Abstract

Continual increase in the complexity of technologies and innovations has resulted in actors, i.e. individuals or organizations, becoming more dependent on other actor's knowledge and skills to complement their own skills in the innovation process. As a result, networks have become more and more prominent in affecting the innovation process. Conceptualizing innovation as a search and learning process that results in a successful product, we introduce a two-phase model of collective innovation that accounts for the deliberateness of the innovation process. Using agent-based modeling, we explore how interaction structure among a group of actors affects the speed at which the group can collectively innovate. In addition, we present results from counterfactual simulations to highlight the importance of deliberate search and changing realized connections in modeling collective innovation process. Moreover, we discuss the effects of the structure under different mechanisms of learning and different levels of actor's learning capabilities.

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