{"title":"Evolutionary Game of Multiple Subjects Collaborative Innovation Under Double Uncertainty","authors":"Xumei Yuan, Ming Zhang, Fuli Wei, Xu Zhang","doi":"10.1155/cplx/3743541","DOIUrl":null,"url":null,"abstract":"<p>Multisubject technology collaborative innovation serves as an essential driving force to improve technology transfer efficiency as well as implement the innovation-driven strategy. Particularly, collaborative innovation in accordance with technological orientation and market demand enables the efficient resource allocation that stimulates innovation willingness and improves innovation performance. This study explores the strategies and conditions for multiagent collaborative innovation under technological and market uncertainties, employing a government–enterprise–university evolutionary game model. Numerical simulations demonstrate that both technology and market factors are critical drivers of collaborative innovation systems, with market influences outweighing technological ones. Notably, technological universality proves more impactful than novelty. When technology and market drivers are weak, government willingness becomes the decisive factor in shaping other participants’ strategic choices. Punitive measures have a stronger effect on system evolution than government incentives. The study’s key contribution lies in emphasizing technological sophistication, technological universality, market price, participant willingness, and policy impacts. The findings underscore the dynamic interplay of technology, market, and government in collaborative innovation, offering novel theoretical and practical insights into its driving mechanisms and implementation pathways.</p>","PeriodicalId":50653,"journal":{"name":"Complexity","volume":"2026 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cplx/3743541","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Complexity","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/cplx/3743541","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Multisubject technology collaborative innovation serves as an essential driving force to improve technology transfer efficiency as well as implement the innovation-driven strategy. Particularly, collaborative innovation in accordance with technological orientation and market demand enables the efficient resource allocation that stimulates innovation willingness and improves innovation performance. This study explores the strategies and conditions for multiagent collaborative innovation under technological and market uncertainties, employing a government–enterprise–university evolutionary game model. Numerical simulations demonstrate that both technology and market factors are critical drivers of collaborative innovation systems, with market influences outweighing technological ones. Notably, technological universality proves more impactful than novelty. When technology and market drivers are weak, government willingness becomes the decisive factor in shaping other participants’ strategic choices. Punitive measures have a stronger effect on system evolution than government incentives. The study’s key contribution lies in emphasizing technological sophistication, technological universality, market price, participant willingness, and policy impacts. The findings underscore the dynamic interplay of technology, market, and government in collaborative innovation, offering novel theoretical and practical insights into its driving mechanisms and implementation pathways.
期刊介绍:
Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.