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Related Experiment Videos

It's a match! Simulating compatibility-based learning in a network of networks.

Michael P Schlaile¹, Johannes Zeman², Matthias Mueller³

¹1Institute of Economics (520i) and Institute of Economic and Business Education (560 D), University of Hohenheim, Wollgrasweg 23, 70593 Stuttgart, Germany.

Journal of Evolutionary Economics

|January 8, 2019

Summary

This study introduces an agent-based model to simulate knowledge diffusion in innovation networks, considering knowledge compatibility and attention. The model enhances understanding of how network structures influence knowledge flow and innovation dynamics.

Keywords:

Agent-based modeling Cognitive distance Exploitation Exploration Innovation Innovation networks Knowledge compatibility Knowledge diffusion Knowledge networks Learning Memetics Network-of-networks

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Area of Science:

Economics of Innovation
Network Science
Computational Social Science

Background:

Previous diffusion models often overlook key knowledge characteristics like its network structure, compatibility, and the attention required for transmission.
Attributes such as absorptive capacity have typically been treated as exogenous parameters in diffusion models.

Purpose of the Study:

To develop a novel agent-based simulation model for capturing knowledge diffusion and assimilation in innovation networks.
To incorporate the network character of knowledge, compatibility, and attention into a diffusion model.
To endogenize attributes like absorptive capacity and analyze the interplay between network structure and knowledge diffusion.

Main Methods:

An agent-based simulation model using a network-of-networks approach.
Agents possess internal networks of knowledge units (KUs).
Knowledge unit exchange and integration are based on compatibility with existing knowledge and agent attention.

Main Results:

The model successfully simulates knowledge diffusion and assimilation, considering knowledge compatibility and attention.
It distinguishes between within-agent and between-agent knowledge diversity.
Simulation results offer new insights into how innovation network structure impacts knowledge diffusion.

Conclusions:

The proposed model offers a novel framework for understanding knowledge diffusion in innovation networks.
This contributes to advancing the economics of innovation and knowledge.
The model's ability to endogenize absorptive capacity provides a more nuanced view of knowledge assimilation.