Dynamic Cognitive Resource Allocation in Platform-Based Manufacturing Companies Innovation Ecosystems: Game-Theoretic Modeling of Governance Tradeoffs

Yongquan Guo, Mengyao Zhang and Deyu He

Complexity, 2026, vol. 2026, 1-28

Abstract: Addressing the conflict between “centralized control and decentralized coordination†in the allocation of cognitive resources within the innovation ecosystem of platform-based manufacturing enterprises, as well as the governance dilemma of balancing short-term efficiency with long-term innovation, this paper constructs a multiagent interaction model encompassing internal “leadership–team–employees†and external “government–platform–partner enterprises–consumers.†Through MATLAB numerical simulations, the paper compares resource allocation efficiency under centralized and decentralized decision-making and reveals the characteristics of a double-threshold bifurcation and the optimal governance range. The results indicate that (1) excessive internal centralization inhibits innovation vitality, whilst decentralized decision-making, although enhancing innovation output, tends to lead to increased coordination costs; (2) external centralized governance is more conducive to the integration of cognitive resources, whereas the decentralized model suffers from significant fragmentation; and (3) the system exhibits an internal bifurcation threshold γI∗ = −0.403 and an external bifurcation threshold γE∗ = −0.374, which together form a three-segment bifurcation structure; (4) the optimal governance model is “internal decentralization + external centralization,†which simultaneously maximizes both grassroots innovation dynamism and external integration efficiency. This paper expands the application boundaries of distributed cognition and differential games in platform governance, providing a theoretical basis and quantitative decision-making tools for platform-based manufacturing enterprises to dynamically optimize the allocation of cognitive resources, and for governments to refine innovation incentive policies.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:6823250

DOI: 10.1155/cplx/6823250

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