Uncertainty, evidence, and explanation
Probability and Causality
Distinguish probabilistic belief, deductive consequence, and causal explanation through Bayesian updating and expected value.
Belief and evidence3 essays
Move from uncertainty and updating to the evidence required for causal explanation.
Probability and Bayes: Uncertainty, Evidence, and Rational Updating
The mathematics of probability is precise, but its meaning is contested. Bayes' rule shows how conditional probabilities relate without turning uncertainty into truth.
15 min readLogic and Probability: Deduction, Uncertainty, and Evidence
Logic constrains what follows from premises; probability represents uncertainty and evidential support. This essay separates truth, validity, credence, conditional probability, Bayes, causation, and AI generation.
15 min readCausation: Difference-Making, Mechanisms, and Reasons
A causal claim says more than one event followed or predicted another. It connects difference-making, intervention, counterfactual dependence, and mechanism while separating causes from evidence, reasons, and purposes.
19 min read
Risk and choice3 essays
Separate probability-weighted outcomes, risk, and practical choice.
Probability and Bayes: Uncertainty, Evidence, and Rational Updating
The mathematics of probability is precise, but its meaning is contested. Bayes' rule shows how conditional probabilities relate without turning uncertainty into truth.
15 min readExpected Value: Probability, Risk, and Decision
Expected value is a probability-weighted mean, not a prediction of the next outcome. This essay explains its mathematics, uses, limits, relation to risk, and role in decision theory.
13 min readDecision-Making: Judgment, Choice, and Commitment
Decision-making is not a moment of selection or a guarantee of good outcomes. It turns uncertain possibilities, evidence, and values into a revisable commitment to act.
12 min read