I tend to think about the world through systems and feedback loops. Feedback loops are when a system’s output is utilized as input. It functions similarly to a continuous cycle of action, response, and adjustment and has an impact on future functions.
Productivity systems don’t fail due to lack of discipline. As a result of not holding feedback loops or providing space and time for adaptability, systems undergo issues. Many systems eventually break down eventually due to overreliance on specific goals. Since systems don’t always take into account the changes of different constraints, it impacts their effectiveness.
Constantly responding to feedback loops help with growth since they’re a fundamental core. Although goals are a strong baseline for any system, they are fixed. Since feedback is more dynamic, it helps with identifying with what’s happening and assessing further.
Improvement isn’t always structured towards achieving perfection as a requirement. It’s from making decisions, observing, and adjusting to refining through adaptation across a period of time. Improving in complexity with decisions signifies growth. From here, it stems towards a deeper issue: how do we measure improvement?
Feedback at its core is useful when it indicates something that is applicable to the future for growth. Often, the things we decide to measure become what we want to optimize. With gaps across progress, a system can appear as working, while simultaneously enforcing incorrect pathways.
Feedback loops shape both behavior and decision. Concluding to good decisions isn’t solely about intention. Instead, it’s about response over time, meaning they’re not completely sole events.
With the considerations of how systems work, a specific set of steps is considered to further establish structure: Behavior → Measurement → Feedback → Decision (repeat after). Behavior is intertwined with measurement and feedback to achieve the desired result (decision).
Each step impacts the subsequent ones. If any step isn’t completed cohesively, then decisions drift from what works. Decision-making quality depends primarily on the structure of the system that surrounds it, rather than solely effort.
The difficulty amongst each part is that the correct things are rarely measured. In many systems, we default to easier things to track:
However, among these different factors, they often serve as proxies, rather than what is trying to be achieved directly. Measuring hours (time spent) doesn’t truly capture, assess, or indicate understanding. Analyzing output doesn’t directly reflect quality automatically.
Over time, systems start to optimize metrics rather than outcome. It ends up creating a subtle failure mode: the system improves depending on measurements and simultaneously drifts away from the target goal.
Steps in a loop:
Whenever the loop is aligned, the systems that are created have the ability to grow over time. However, when they’re aligned, the system may backtrack in progress. The loop can be visualized as a cycle where each stage impacts the next.