I visualize productivity through a basic input-output lens. If the information coming into my brain and information going out is both balanced and growing, I am conducting a productive lifestyle. Causation is the connector between input and output.
I ordinarily work from the results to the steps it takes to get there. For instance, I want to first know what I'm generally to write about, then find information to input that will help me achieve my goal. With that being said, my rate of productivity is dependent on the strongest and most retained aspects of my input.
If my output is weak, I ask myself why this is the case and what aspects of my strategy caused it. Usually, the answer is not due to my research, but rather, my output was too ambitious and there were gaps in my knowledge I wasn't fully aware of. This small failure is mostly a waste of time, seeing as I have to start the process all over again with either a modified output strategy or an entirely new concept to explore.
It's important to my process to understand what is causing failure and what is causing a success incrementally. But first:
What Defines A Cause To An Effect And Other Stuff ...
- Understanding the difference between causation and correlation.
→ correlation can also signal what the cause is
- Does a poor effect need a definite cause to change into a positive effect?
- Do casual connections or minor coincidences help produce an effect as well?
- Is it wise to detangle a web of causes to change an effect? Does being over-analytical backfire typically?
← for me, in some cases, an incremental approach and limiting the number of variables to a particular effect will reduce this problem. All the while, balancing an abstract mental model about a wider project to ascertain causality and effectiveness with ease.