Fair enough! I think the circuit analogy is fitting; the primary analysis is at a system-level scale rather than an individual unit scale. That said, my goal would be to turn it into a tool for better understanding and changing biases themselves (without losing the higher-level functionality). Some people have offered some ideas on how that might be done, and that’s exactly what I’m hoping to get out of sharing here.

You are a step ahead of my latest post with the CBT comment. Good points on being able to write out thought chains and add distortion notation later and symbols for common biases. Have you seen examples of belief network diagrams used in this way?

I think my comments about it being helpful in working through biases led people to think I intended these primarily as active problem-solving devices. Of course you can't just draw a diagram with a jog in it and then say "Aha! That was a bias!" If anything, I think (particularly in more complex cases) the visuals could help make biases more tangible, almost as a kind of mnemonic device to internalize in the same way that you might create a diagram to help you study for a test. I would like to make the diagrams more robust to serve as a visual vocabulary for the types of ideas discussed on this site, and your comments on distinguishing types of biases visually are helpful and much appreciated. Would love to hear your thoughts on my latest post in response to this.

You are overestimating the ambition of the diagram. I know it does not add any new information. I am (working on) presenting the information in a visual form. That’s why I called it a new way of visualizing biases, not a new way to get rid of them with this one simple trick. You can convey all the information shown in a Venn diagram without a diagram, but that doesn’t mean the diagram has no possible value. And if there were a community dedicated to understanding logical relations between finite collections of sets back in 1880, I’m sure they would have shot down John’s big idea at first too.

It's a feature if the benefits of a more comprehensive model outweigh the costs. Whether that's true in this case is another question.

I think I see what you're saying, but let me know if I've misinterpreted it.

Let's look at the planning fallacy example. First, I would argue it is entirely possible to be aware of the existence of the planning fallacy and be aware that you are personally subject to it while not knowing exactly how to eliminate it. So you might draw up a diagram showing the bias visually before searching or brainstorming a debiasing method for it.

According to Daniel Kahneman, “Using… distributional information from other ventures similar to that being forecasted is called taking an ‘outside view’ and is the cure to the planning fallacy.”

So removing the planning fallacy is not a matter of simply compensating for the bias, but adopting a new pathway to that type of conclusion. I don't think overcompensating for a bias can be said to remove it on a systemic level, and I don't think it necessarily needs to be shown differently in the diagram. If you are able to habitualize taking the outside view to determine deadlines by default, you still may not perfectly predict how long things will take, but this will no longer be due to the planning fallacy.

That makes sense; they are intentionally somewhat fluid so they can adapt to capture a wider variety of biases/phenomena. I'm trying to use the same framework to visualize emotional reactions and behavioral habits.