State-space models have indeed become a crucial tool in addressing the complexities of modern business, economics, and finance. These models allow for the representation of a system's dynamics in a compact and flexible framework, making them ideal for analyzing complex and dynamic economic and financial phenomena. State-space models have found applications in various areas such as macroeconomic forecasting, asset pricing, risk management, and more.

Given the turbulent nature of modern business, the incorporation of state-space models provides a means to represent and capture the dynamics and uncertainties present in economic and financial systems. They allow for the incorporation of subjective (qualitative) and objective (quantitative) factors, providing a comprehensive framework to analyze the varying importance of these factors.

In economics and finance, state-space models enable the modeling of latent variables and unobserved factors that influence economic and financial processes, thus providing a more accurate representation of the underlying dynamics. These models are particularly suited for addressing situations where high complexity and turbulence are present, allowing for the integration of various sources of information and the modeling of their interactions over time.

As the discussion around state-space models in economics and finance intensifies, it becomes crucial to understand the diverse applications, advantages, and challenges associated with their usage. Further exploration of the appropriate weighting and integration of subjective and objective factors within state-space models is an important area of research to enhance their effectiveness in addressing the complexities of the modern business landscape.