Multi-Agent Collaborative R&D Strategies of General-Purpose Technologies: Commonality and Synergy Perspective

• Published in: Tehnički vjesnik Vol. 30; no. 2; p. 597
• Main Authors: Zhu, Junda, Liu, Guoxin, Zhang, Feng
• Format: Journal Article Paper
• Language: English
• Published: Slavonski Baod University of Osijek 01.04.2023; Josipa Jurja Strossmayer University of Osijek; Strojarski fakultet u Slavonskom Brodu; Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek; Građevinski i arhitektonski fakultet Osijek
• Subjects: China; Collaboration; Commonality; commonality degree; evolutionary game; general-purpose technologies; Government industry relations; Industrial research; Mathematical models; multi-agent collaborative R&D; Multi-agent systems; Multiagent systems; R&D; Research & development; Research facilities; Strategic planning (Business); synergy degree; China
• DOI: 10.17559/TV-20220613041011
• ISSN: 1330-3651

New round of emerging scientific and technological revolution mingled with industrial revolution has intensified the competition among countries for general-purpose technologies (GPTs), as they attempt to strengthen their strategic layout and seize decisive development opportunities. However, in reality, the collaborative R&D of GPTs by multiple subjects often fails, thereby leading to the low success rate of GPTs. To identify the influencing factors of multi-agent collaborative R&D GPTs, an evolutionary game model of multi-agent collaborative R&D based on types of GPTs and the degree of synergy among multiple agents was constructed in this study. The multi-agent collaborative R&D strategies under the two scenarios with and without government participation were then explored, and MATLAB numerical simulation was performed to compare the influence of different types of GPTs and the degree of synergy under government strategies and measures on multi-agent collaborative R&D strategies. Results show that: (1) in the R&D process of basic, pre-competitive and applied GPTs, the degree of synergy required for multi-agent cooperation gradually decreases, and the focus of synergy also changes along with types of technologies. (2) In the R&D process of basic GPTs, when the degree of synergy is higher than the critical value, the willingness of enterprises to conduct R&D decreases and then increases in a "U-shaped" trend as the willingness of universities and research institutes (UR) to conduct R&D increases. In the R&D process of pre-competitive GPTs, when the degree of synergy is lower than the critical value, the willingness of UR to conduct R&D increases and then decreases in an "inverted U" trend as the willingness of enterprises to conduct R&D decreases. In the R&D process of all three types of GPTs, government participation is particularly effective, and when the degree of synergy is much higher than the critical value, the willingness of enterprises to engage in R&D increases. (3) In the case of government participation, the proportion of subsidies, the strength of knowledge protection and the degree of synergy among multiple subjects are adjusted by the government in line with the importance of GPTs to act on precise policies. To some extent, conclusions provide theoretical basis and policy suggestions for industry-university-research collaborative R&Ds of GPTs and government measures.