Frequently asked questions about thinking, epistemology, and cognitive tools. 9738 answers
Everyone has specific recurring distortions — identify yours. Generic bias literacy is not enough. You need a personal bias profile: the particular set of systematic errors your brain commits most frequently, in the specific domains where those errors cost you the most.
Build the first draft of your personal bias profile. For each of the five categories below, rate yourself on a 1-5 scale (1 = rarely affects me, 5 = this is a persistent pattern) and write one concrete example from the last 90 days. The categories: (1) Confirmation bias — Do you seek out.
Treating bias awareness as a general intellectual stance rather than a specific diagnostic practice. You read about the anchoring effect, nod thoughtfully, and never once audit your own estimates for anchoring patterns. You learn about confirmation bias, agree that it is a serious problem, and.
Everyone has specific recurring distortions — identify yours. Generic bias literacy is not enough. You need a personal bias profile: the particular set of systematic errors your brain commits most frequently, in the specific domains where those errors cost you the most.
The structures and incentives of an organization determine individual action more than personality does.
A failure you analyze in writing becomes data. A failure you only remember becomes shame.
Dividing things into only two groups forces a false simplicity.
Tracing a chain of causes and effects reveals the full mechanism behind an outcome.
Multiple paths between important nodes make a system more robust.
Select a prediction you made in the last six months that turned out wrong. Write it down with as much specificity as you can: what you predicted, what actually happened, and the gap between the two. Now perform a schema autopsy. Do not ask "what did I do wrong?" Ask "what does this prediction.
Two opposite failure modes bracket this lesson. The first is treating failed predictions as evidence of personal inadequacy — collapsing the distance between "my model was wrong" and "I am wrong." This triggers ego defense, avoidance of future predictions, and schema stagnation. The second failure.
When your prediction is wrong you have learned something about where your schema is off.
Looking for evidence that supports your schema is not the same as rigorously testing it.
You can build schemas at different levels of abstraction each serving different purposes.
Concepts are nodes and relationships are edges — together they form a graph.
Choose five concepts you have been studying or thinking about recently — from any domain. Write each one on a separate card or sticky note. These are your nodes. Now draw lines between every pair that has a meaningful relationship. Label each line with the nature of the relationship: "causes,".
Treating nodes and edges as purely technical vocabulary — something that belongs to computer science or mathematics but not to how you actually think. This creates a wall between "graph theory" and "my knowledge," when the entire point is that your knowledge already has graph structure. You.
Concepts are nodes and relationships are edges — together they form a graph.
Areas where connections should exist but do not indicate knowledge gaps.
Something can be true now and have been false before without contradiction.
Find a belief you hold now that contradicts something you believed three or more years ago. Write both versions down with dates: 'In [year], I believed [X]' and 'Now, in 2026, I believe [Y].' Then answer three questions: (1) What changed in the environment between then and now? (2) What changed in.
Assuming that because your current belief contradicts a past belief, one of them must have been wrong. This is presentism — judging past reasoning by present conditions. The subtler failure is the opposite: assuming your current beliefs are as time-bound as the ones they replaced, and therefore.
Something can be true now and have been false before without contradiction.
When an agent fails to fire or produces bad results you learn how to improve it.