Summary: This article debunks the erroneous beliefs regarding the expertise of individuals in making decisions within open-loop systems and presents an approach to overcome their limitations.

Open-loop systems don’t provide real-time feedback of the results.  This makes it difficult to adjust strategies effectively.  For instance, in real estate investments or marketing campaigns, the success or failure of a decision is not known for months or years, making it difficult to make informed decisions.  For example, human resource managers receive feedback on an employee’s performance after making the hiring decision .

The lack of actual feedback in open-loop systems impairs the accumulation of experience.  Unlike close-loop systems, like driving a car, where numerous decisions are made with real-time feedback.  Open-loop systems have fewer decisions and delayed feedback. This hinders individuals from gaining management expertise, even with lifetime of experience.

Furthermore, management is not based on sound scientific knowledge.  Unlike medicine, engineering, and physics, it relies on personal experience.  

Improving decision making in open loop systems

It’s crucial to acknowledge that the expertise appearance of an open-loop system is an illusion and that measures to counteract potential errors must be implement when making decisions. A manager who has been through similar situations several times may attain a false sense of confidence, reinforced when compared to others. The manager might say: “I know how to handle this, I’ve seen it before.” To make informed decisions in an open-loop system, it’s important to be vigilant and to employ safeguards and contingencies.

The following approach will enhance the accuracy of decision-making process  in open-loop systems:

  1. Simulate feedback loops:  Simulate the consequences of a decision before implementing it. For instance, before hiring someone for a specific role, request him to perform tasks that align with their responsibilities to evaluate his performance.
  2. Divide the decision making process into its components: premises, assumptions, and logic.  Then run simulations on each component to show all possibilities and probabilities.
  3. Use diverse know-how: Use Ai and involved individuals who have diverse experiences to identify potential blind spots and risk to verify facts and assumptions.
  4. Use formal decision-making frameworks: Utilize structured decision-making frameworks and seek training to enhance your reasoning skills.
  5. Regularly analyse the results of past decisions to include the know-how in the next decision.