Beyond “Failure Tolerance”

 In recent years, many discussions of innovation policy have emphasized the need to “tolerate failure.” While I strongly agree with this principle, I worry that the slogan risks diverting attention from a more fundamental issue: the structure of research evaluation and the design of public R&D investment. Encouraging risk-taking alone does not explain why the descendants of successful entrepreneurs in countries such as Japan and Korea often become effective long-term managers and R&D investors, nor why governments that rely heavily on expert committees frequently struggle to achieve comparable innovation outcomes.

The repeated call to “accept failure” can therefore oversimplify the problem. Many researchers are not avoiding risk because they fear failure. Rather, the structure of academic incentives often encourages work that is theoretically elegant and readily publishable rather than work that addresses long-term, system-oriented technological challenges. As a result, researchers may develop deep expertise within narrow domains while having limited exposure to the full innovation process—from modeling and theoretical development to engineering implementation and eventual commercial deployment.

The deeper issue is not simply the cultural acceptance of failure, but the willingness and institutional support required to pursue the repetitive and often tedious cycles of trial and error needed to transform ideas into real technologies. Translating a novel concept into practical applications frequently requires far more effort than generating the initial idea or demonstrating success in a laboratory environment. When evaluation systems prioritize short-term academic outputs, researchers rationally focus on projects that align with those incentives, leaving the broader innovation pipeline less understood.

True reform should focus on the incentives embedded within research governance. Evaluation frameworks must recognize contributions that extend beyond theoretical novelty, including system integration, industrial collaboration, and technology deployment. Innovation ecosystems function best when theory, engineering, and industrial application interact continuously. Strengthening this interaction requires more than calls for greater tolerance of failure; it requires structural reform in how research is evaluated and funded.

A major challenge is that research governance is often guided by specialists whose expertise lies within narrow academic disciplines and who may have limited experience with the full innovation pipeline. This can make communication between policymakers, academia, and industry more difficult. Discussions may then focus primarily on theoretical breakthroughs or promising laboratory results rather than on the complex engineering and organizational processes required to translate ideas into deployable technologies.

Moreover, academic expertise is increasingly specialized, which can make it difficult to fully appreciate the interdisciplinary and system-level challenges involved in transforming scientific knowledge into practical innovations. As a result, policy debates may underestimate the complexity of the transition from theory to engineering systems and ultimately to industrial deployment.

Addressing these structural issues requires rethinking the relationship between scientific research, engineering development, and industrial application. Innovation does not end with theoretical insight or laboratory success; it must survive the far more demanding process of real-world implementation. Aligning research evaluation systems with this broader innovation pathway may be one of the most important challenges for contemporary research policy.


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