When are Real-Time Feedback Loops Most Valuable? New Insights from Bandit Simulations of Decision Making in Turbulent Environments

Businesses are increasingly encouraged to invest in technologies to support real-time feedback loops between decision making and analysis, but research has little examined when real-time feedback is most and least valuable. In the present study we extend the canonical model of decision making under uncertainty to examine how the value of real-time feedback varies with the turbulence of the decision environment. Additionally, we explored the complementarity of real-time feedback with a knowledge renewal capability that serves to unlearn out-of-date knowledge. Simulation results reveal an unexpected nonlinearity: a real-time feedback loop has a markedly greater positive impact on performance at moderate levels of turbulence that at high or low levels of turbulence. The impacts of knowledge renewal capacity, as we simulated it, are less clear-cut, and the two capabilities were not found to interact. Implications for research and practice, and recommendations for further study are discussed in the paper's final section.

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