Modeling Evolving Innovation Networks

TL;DR

This paper introduces a dynamic framework for modeling evolving innovation networks among firms, capturing how their interactions and network structures develop over time based on strategic knowledge exchange decisions.

Contribution

It presents a novel mathematical and simulation-based approach to analyze how firms' strategic link formation influences network structures and reciprocity emergence.

Findings

Different network structures emerge based on firms' information and strategies.

Conditions for the existence of specific network topologies are mathematically derived.

Simulations demonstrate the evolution and equilibrium states of the networks.

Abstract

We develop a new framework for modeling innovation networks which evolve over time. The nodes in the network represent firms, whereas the directed links represent unilateral interactions between the firms. Both nodes and links evolve according to their own dynamics and on different time scales. The model assumes that firms produce knowledge based on the knowledge exchange with other firms, which involves both costs and benefits for the participating firms. In order to increase their knowledge production, firms follow different strategies to create and/or to delete links with other firms. Dependent on the information firms take into account for their decision, we find the emergence of different network structures. We analyze the conditions for the existence of these structures within a mathematical approach and underpin our findings by extensive computer simulations which show the evolution of the networks and their equilibrium state. In the discussion of the results, particular attention is given to the emergence of direct and indirect reciprocity in knowledge exchange, which refers to the emergence of cycles in the network structure. In order to motivate our modeling framework, in the first part of the chapter we give a broad overview of existing literature from economics and physics. This shows that our framework bridges and extends two different lines of research, namely the study of equilibrium networks with simple topologies and the dynamic approach of hypercycle models.