Strong framing of how algorithmic thinking can sharpen our reasoning but also amplify biases when we're not careful. What clicked for me is that the same structure that helps us process evidence objectively can reinforce belief-consistant pathways if we skip the humility step. I've caught myself doing this when evaluating new research, essentially filtering data through prior assumptions rather than questioning them. The six fundamental beliefs list is practical, it gives you handholds for self-auditing rather than vague advice about stayin open-minded.
This was a thoughtful and engaging piece—thank you for writing it. As an epidemiologist, some of these ideas are relatively new to me, and I’m still learning how to think about the role of subjectivity alongside objectivity. In scientific practice, we’re trained to minimize subjective steps in causal inference and diagnostic reasoning, so I find myself pausing when ethical or moral perspectives are framed through scientific language, particularly around conspiracy beliefs. Without clear boundaries, this can create gray areas that are open to individual interpretation and risk blurring empirical explanation with normative judgment. I appreciate the perspective you’re offering and see real value in the conversation—it’s helped me reflect more carefully on where explanation, interpretation, and evaluation intersect.
I’d be curious to hear how you distinguish, in your framework, between empirical explanation and normative or ethical evaluation.
Clear and engaging explanation of how Bayesian thinking helps distinguish correlation from causation. The examples make complex ideas intuitive and highlight why causal reasoning matters for real world public health decisions.
Strong framing of how algorithmic thinking can sharpen our reasoning but also amplify biases when we're not careful. What clicked for me is that the same structure that helps us process evidence objectively can reinforce belief-consistant pathways if we skip the humility step. I've caught myself doing this when evaluating new research, essentially filtering data through prior assumptions rather than questioning them. The six fundamental beliefs list is practical, it gives you handholds for self-auditing rather than vague advice about stayin open-minded.
This was a thoughtful and engaging piece—thank you for writing it. As an epidemiologist, some of these ideas are relatively new to me, and I’m still learning how to think about the role of subjectivity alongside objectivity. In scientific practice, we’re trained to minimize subjective steps in causal inference and diagnostic reasoning, so I find myself pausing when ethical or moral perspectives are framed through scientific language, particularly around conspiracy beliefs. Without clear boundaries, this can create gray areas that are open to individual interpretation and risk blurring empirical explanation with normative judgment. I appreciate the perspective you’re offering and see real value in the conversation—it’s helped me reflect more carefully on where explanation, interpretation, and evaluation intersect.
I’d be curious to hear how you distinguish, in your framework, between empirical explanation and normative or ethical evaluation.
Conspiratorial thinking is the norm -- just look at religions.
Even objective truths are processed by our subjective brains.
BAT provides a method to update our beliefs, credibilities, and probabilities.
Clear and engaging explanation of how Bayesian thinking helps distinguish correlation from causation. The examples make complex ideas intuitive and highlight why causal reasoning matters for real world public health decisions.