Scandinavian Working Papers in Economics

Working Paper Series in Economics and Institutions of Innovation,
Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies

No 357: Policy Simulation of Firms Cooperation in Innovation

Almas Heshmati () and Flávio Lenz-Cesar ()
Additional contact information
Almas Heshmati: Centre of Excellence for Science and Innovation Studies (CESIS), & Department of Economics, Sogang University, Postal: Room K526, 35 , Baekbeom-ro, (Sinsu-dong #1) , Mapo-gu, , Seoul 121-742 Korea
Flávio Lenz-Cesar: Ministry of Communications, Esplanada dos Ministério, Postal: Bloco R #702, , Brasília, DF, , Brazil

Abstract: This study utilizes results from an agent-based simulation model to conduct public policy simulation of firms’ networking and cooperation in innovation. The simulation game investigates the differences in sector responses to internal and external changes, including cross-sector spillovers, when applying three different policy strategies to promote cooperation in innovation. The public policy strategies include clustering to develop certain industries, incentives to encourage cooperative R&D and spin-off policies to foster entrepreneurship among R&D personnel. These policies are compared with the no-policy alternative evolving from the initial state serving as a benchmark to verify the gains (or loses) in the number of firms cooperating and networking. Firms’ behavior is defined according to empirical findings from analysis of determinants of firms’ participation in cooperation in innovation with other organizations using the Korean Innovation Survey. The analysis based on manufacturing sector data shows that firms’ decision to cooperate with partners is primarily affected positively by firm’s size and the share of employees involved in R&D activities. Then, each cooperative partnership is affected by a different set of determinants. The agent-based models are found to have a great potential to be used in decision support systems for policy makers. The findings indicate possible appropriate policy strategies to be applied depending on the target industries. We have applied few examples and showed how the results may be interpreted. Guidelines are provided on how to generalize the model to include a number of extensions that can serve as an optimal direction for future research in this area.

Keywords: agent-based simulation; collaborative R&D; innovation networks; simulation game; policy strategy

JEL-codes: C15; C71; D21; D85; L20; O31

25 pages, March 27, 2014

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