Tuning the Innovation Cycle: A Macroprudential Policy for Creative Destruction

Dubina and Campbell (2019) argue innovation and crises coevolve in a tightening spiral; Nelson (2015) frames development as evolutionary and path-dependent; Buldyrev et al. (2020) model the stochastic rise and fall of firms. Yet we lack operational levers to stabilize this cycle. This project proposes a real-time, rules-based innovation policy that adjusts R&D tax credits, IP durations, and public procurement intensity based on system indicators of novelty saturation, concentration risk, and diffusion slack. We synthesize Aghion et al.’s creative destruction paradigm (reviewed in Bucci, 2022) with big data pipeline readiness (Sultana et al., 2021) to build a dashboard of early-warning metrics: e.g., patent text novelty, VC concentration, supply-chain fragility, and diffusion lags. The novelty is treating innovation policy like financial macroprudential regulation—countercyclical, data-driven, and network-aware—rather than static incentives. If successful, it reconciles the “optimize innovation” call (Dubina & Campbell, 2019) with evolutionary heterogeneity (Nelson, 2015), yielding higher mean growth with lower volatility and fewer destructive cascades.

References:

1. P. Aghion, C. Antonin, S. Bunel, The power of creative destruction: Economic upheaval and the wealth of nations. A. Bucci (2022). JOURNAL OF ECONOMICS.
2. Architecting and Developing Big Data-Driven Innovation (DDI) in the Digital Economy. Saida Sultana, Shahriar Akter, Elias Kyriazis, S. Wamba (2021). Journal of Global Information Management.
3. CREATIVE INDUSTRIES, INNOVATION DEVELOPMENT AND AN ECONOMIC CRISIS. И. Дубина, Дэвид Ф. Дж. Кэмпбелл (2019). The Journal of Economic Research & Business Administration.
4. Understanding long run economic development as an evolutionary process. R. Nelson (2015).
5. The Rise and Fall of Business Firms: A Stochastic Framework on Innovation, Creative Destruction and Growth. S. Buldyrev, F. Pammolli, M. Riccaboni, H. Stanley (2020).