I’m not sure that you’re using these variables consistently. In some cases, the external input node is a fact or basic premise, in others (like the planning fallacy example) it represents an intuition. Same goes for the cognition/conclusion node which is also a bit confusing. I can see the value in this type of diagram, but I think it could use some refinement.
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.
No, it is a bug in virtually all cases. A model which depicts a broad class of phenomena in a single way is a bad model unless the class of phenomena are actually very similar along the axis the model is trying to capture. These phenomena are not similar along any useful axis. In fact, there is no observable criterion you could choose to distinguish the examples depicted as biased from the examples depicted as correct. A biased inference, a correct inference where no bias you know of played a substantial role, an instance where multiple biases canceled out, an instance where you overcompensated for bias (e.g. “the world isn’t actually dangerous, so that guy at the bus top with a drawn knife probably doesn’t actually mean me harm”), and a Gettier case are all structurally identical.
This diagram format is a pure placebo and any value you perceive it to have given you is incorrectly attributed.
I’m not sure that you’re using these variables consistently. In some cases, the external input node is a fact or basic premise, in others (like the planning fallacy example) it represents an intuition. Same goes for the cognition/conclusion node which is also a bit confusing. I can see the value in this type of diagram, but I think it could use some refinement.
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.
Capturing a wide variety of phenomena is a bug, not a feature.
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.
No, it is a bug in virtually all cases. A model which depicts a broad class of phenomena in a single way is a bad model unless the class of phenomena are actually very similar along the axis the model is trying to capture. These phenomena are not similar along any useful axis. In fact, there is no observable criterion you could choose to distinguish the examples depicted as biased from the examples depicted as correct. A biased inference, a correct inference where no bias you know of played a substantial role, an instance where multiple biases canceled out, an instance where you overcompensated for bias (e.g. “the world isn’t actually dangerous, so that guy at the bus top with a drawn knife probably doesn’t actually mean me harm”), and a Gettier case are all structurally identical.
This diagram format is a pure placebo and any value you perceive it to have given you is incorrectly attributed.