Algorithms for the formation of a belief

 There’s no single infallible algorithm, but you can use a disciplined pipeline that turns vague hunches into calibrated credences and action-ready beliefs. Below is a compact, domain-agnostic process plus simple variants.

Core belief-formation pipeline

1. Specify the proposition

• State the claim precisely and bound its scope, time, and context.
• Operationalize key terms so it’s clear what would count as true/false.

2. Set stakes and acceptance thresholds

• Decide what probability or evidence standard you need to “act as if true” (e.g., low-stakes: >70%; safety-critical: >99.9%; legal: preponderance/clear-and-convincing/beyond reasonable doubt).
• Separate “believe” (credence) from “act” (decision threshold).

3. Establish priors using base rates

• Choose a reference class; use base rates or expert consensus to set an initial credence.
• Default to modest priors for extraordinary claims.

4. Generate alternatives

• List plausible competing hypotheses, including the null.
• For each, list predictions that would be more/less likely if it were true.

5. Seek targeted, independent evidence

• Prefer evidence that discriminates between hypotheses (high diagnosticity).
• Evaluate source quality, independence, and recency; avoid counting correlated sources twice.

6. Update credence (Bayes-in-plain-English)

• Ask: “How much more expected was this evidence if H is true than if it isn’t?” (the likelihood ratio/Bayes factor).
• Multiply prior odds by that factor across independent evidence; keep a running probability (credence), not a binary label.

7. Stress test the inference

• Try to falsify your favored hypothesis; actively search for disconfirming evidence.
• Probe alternative causal stories; check confounding, temporal order, and robustness to different assumptions.
• Run sensitivity analysis: How much would your credence move if key inputs were off by 20–50%?

8. Check for convergence and consilience

• Prefer beliefs supported by multiple independent methods (e.g., experiments, natural experiments, mechanism models, out-of-sample predictions).

9. Bias and fallacy check

• Look for confirmation bias, motivated reasoning, base-rate neglect, survivorship bias, cherry-picking, straw-manning, and equivocation on terms.
• Do a brief “steelman then critique” pass on the strongest opposing view.

10. Decide and label

• Compare current credence to your acceptance threshold for action.
• Label status: Unsupported, Plausible, Provisionally accepted, Established (with confidence interval), or Overturned.

11. Record and monitor

• Log your claim, reasons, sources, and current credence.
• Make at least one falsifiable prediction; revisit on a schedule or when new evidence arrives.
• Track calibration over time (are 70% beliefs true ~70% of the time?).

Practical rules of thumb

• Two-independent-sources rule for factual claims before strong confidence.
• Extraordinary claims require extraordinary evidence and methodological diversity.
• Prefer simpler hypotheses that explain the data (parsimony), but not at the expense of fit.
• Distinguish epistemic confidence from decision confidence: sometimes you must act under uncertainty; make that explicit.
• Don’t round probabilities to 0 or 1 on empirical matters; leave room for revision.

Variants by context

• Fast, low-stakes (minutes):

  1. Clarify claim and scope.
  2. Check base rate or consensus.
  3. Find at least one strong counterargument.
  4. Apply two-source rule.
  5. Set a provisional credence and move on; mark for later review if important.

• Scientific/analytic (days–months):

  • Pre-register predictions, use identification strategies for causal claims, report effect sizes/CIs, replicate or seek replications, and disclose uncertainties.

• Legal/policy:

  • Align with the relevant burden of proof; weigh harms of false positive vs. false negative; ensure procedural fairness and adversarial testing of evidence.

Lightweight pseudocode (conceptual)

• Input: proposition P, alternatives H1…Hk, prior odds Oi, independent evidence E1…En with assessed likelihood ratios Li
• For each Ei: update Oi ← Oi × Li
• Normalize to probabilities; compare to action thresholds; output credence, decision, and a list of pivotal uncertainties to monitor.

Common failure modes to guard against

• Ill-defined claims (unfalsifiable or moving goalposts).
• Overfitting to noisy evidence; double-counting dependent sources.
• Causal leaps from correlation; ignoring base rates.
• Stopping the search once you like the answer; not specifying a stop rule in advance.

Easy, quick example:

Here is an example of an everyday belief formation, testable in minutes, two independent checks, no privacy risks. Here’s a concrete, fast example that walks the belief-formation steps.

Example belief: “A fridge magnet will attract a steel paperclip, but it will not attract a same-sized ball of aluminum foil.”

Materials

• Fridge magnet
• Steel paperclip or safety pin (ferromagnetic)
• Small piece of aluminum foil, rolled into a tight ball

Pipeline (under 5 minutes)

1. Specify proposition

• Claim: “This specific magnet attracts steel but not aluminum.”

2. Stakes and threshold

• Low stakes; accept as “true for action” at ≥95% confidence.

3. Prior and alternatives

• Prior: High (common knowledge of magnetism).
• Alternatives to consider:
  • The magnet is too weak or demagnetized.
  • The “paperclip” isn’t steel (e.g., aluminum or brass).
  • Static cling or adhesive is faking attraction.

4. Tests (two independent checks)

• Check 1 (positive test): Bring magnet near the paperclip.
  • Expected if true: Paperclip jumps to or firmly sticks to the magnet.
  • If no attraction, try a second known-steel item (needle, small screw) to rule out a non-steel clip.
• Check 2 (negative control): Bring magnet near the aluminum-foil ball of similar size.
  • Expected if true: No attraction; the foil does not lift or stick.

5. Update credence (Bayes-in-plain-English)

• Observation “paperclip sticks” is far more likely if the claim is true than if false → big upward shift.
• Observation “foil does not stick” is also more likely if the claim is true → further upward shift.
• Combined, credence >99% for this setup.

6. Decide and label

• Status: Established (for these objects and this magnet).
• Note scope: Some “paperclips” are non-steel; very strong magnets can weakly move thin aluminum via eddy currents, but fridge magnets won’t.

7. Log/monitor (optional)

• Record: magnet type, objects used.
• If a later test contradicts (e.g., a non-steel “paperclip”), revisit the hypothesis: “This magnet attracts ferromagnetic metals but not aluminum.”

Why this fits your constraints

• Fast: 1–3 minutes.
• Two independent checks: a positive test on steel and a negative control on aluminum.
• No external sources, no personal data, no filming or location sharing.