Collaborative innovation and policy support: the emergence of trilateral networks

ABSTRACT

The present article models the critical factors for a successful and evolutionarily stable National System of Innovation. We simulate a model, against the background of increasingly complex technologies, in a national process of agents’ interactions with social-dilemma characteristics. In particular, the articleinvestigates the emergence of a trilateral collaborative innovation alliance among ‘enterprise’, ‘university’ and ‘government’. We apply a tripartite evolutionary game with a replication process and explore the role and options of the public policy agent to support collaboration on innovation. We find that some policy mix, in particular, a combination of (1) public rewards for cooperation, (2) public punishment for non-cooperation and (3) settings of public cost controls and tax income from innovation, can promote broad and sustainable innovation alliances. For instance, threats of strong punishment, even with low public rewards for cooperation, may promote the formation of a collaborative innovation alliance. We run some sensitivity analyses of the results through parametric variation of two critical factors of the model, knowledge spillover and output elasticity of knowledge input. We find some qualifications for the velocity of the process.

KEYWORDS:

• Cooperation
• Evolutionary Games
• Innovation Networks
• Knowledge Spillovers
• Technology Transfer

JEL CLASSIFICATION:

• C63
• C73
• D85

Acknowledgments

This article is supported by the National Natural Science Foundation of China (No. 71874068), Ministry of Education of China (No. 16JJD790017; No.17YJC790129).

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ Model Variables and Parameters are presented in Appendix 1.

² The initial value of our parameters were set according to previous evolutionary game study (Amann and Possajennikov 2009; Marco and Goetz 2017).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [71874068]; Ministry of Education of the People’s Republic of China [17YJC790129]; Ministry of Education of the People’s Republic of China [16JJD790017].