Central Exhibit
A map for the distance between evidence, perception, and belief.
Credencing is the practice of matching confidence to evidence. Explore Bayesian reasoning, belief calibration, and the difference between Deep Rationality and Core Rationality through an interactive model, practical guides, and case studies.
Suggested Reading Paths
Three ways into the site
20-Minute Path
A quick orientation for first-time visitors.
One-Hour Path
A fuller path through vocabulary, diagnosis, and repair.
Teacher Path
Materials and pages for classroom or workshop use.
Why Study Credencing?
Belief is not a binary toggle; it is our dynamic orientation toward changing realities. We call this process Credencing.
The model helps separate failures of skill from failures of will: what the evidence is, what an agent perceives, what they finally believe, and how much rational resolution they bring to the task.
Deep Rationality
The skill axis: math, logic, probability, and inferential tools for seeing through epistemic fog.
Core Rationality
The will axis: the fortitude to align belief with what one actually takes the evidence to show.
Unbundling Failure
Traditional labels often blur the honest novice and the biased expert. Credencing keeps those failures separate so each can be diagnosed more precisely.
Concept Diagrams
Three ways to see the model
Evidence Flow
Objective evidence becomes perceived evidence, then assigned credence. The project studies where this flow bends.
Core vs Deep
Skill and integrity are separate axes. Intelligence alone does not guarantee calibrated belief.
Action Threshold
Credence and action threshold are distinct. High stakes can require more confidence without changing the evidence.
Featured Paths
The surrounding web
Start Here
A guided entry into credences, evidence, confidence, Bayesian updating, and the difference between being mistaken and being epistemically inconsistent.
2 clusters / 7 pages
Core Ideas
The conceptual vocabulary behind the model: priors, likelihoods, posteriors, uncertainty, objective evidence, perceived evidence, assigned credence, and rationality types.
3 clusters / 21 pages
Bayes Theorem
A dedicated walkthrough of Bayes theorem, its formula, its mental parts, and how scientists use it to revise confidence.
2 clusters / 9 pages
Interactive Lab
The live model, scenario presets, visual legend, comparison tools, and an interpretation guide for reading epistemic states.
3 clusters / 11 pages
Case Studies
Concrete examples of epistemic failure and repair in reasoning, institutions, media, medicine, politics, and ordinary judgment.
3 clusters / 11 pages
Skills
Practical exercises for thinking in credences, updating with new evidence, estimating priors, avoiding base-rate neglect, and asking better questions.
2 clusters / 12 pages
Discussion Groups
Session plans, discussion questions, scenarios, historical examples, and facilitator guidance for groups studying credencing together.
2 clusters / 10 pages
Applications
Where credencing matters: science, medicine, journalism, law, public debate, AI alignment, personal decisions, and institutional diagnostics.
2 clusters / 10 pages
Library
Books, papers, essays, glossary entries, references, visual archive, teaching materials, downloads, and project notes.
4 clusters / 50 pages
About
Project overview, methodological commitments, intended use, author notes, contact, and future directions.
2 clusters / 8 pages