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Probability

Bayseian Probability

Bayesian thinking provides a systematic framework for updating beliefs and making decisions under uncertainty, crucial for both personal growth and business strategy.

Core Principles​

Explicit Priors

  • Start with base rates (e.g., 20:1 farmer-to-librarian ratio in the Steve example) before incorporating new evidence.
  • Example: In investing, prior failure rates in a sector (e.g., 90% of crypto startups fail) should anchor initial risk assessments.

Iterative Updates Using Bayes' formula

  • Example: If a startup CEO has a strong track record (evidence), update success probability multiplicatively rather than resetting beliefs.

Process Over Outcomes Evaluate decisions based on information available at the time, not results.

  • A 16.7% posterior (Steve's librarian probability) can be rational even if wrong.

Actionable Practices​

1. Decision Structuring Knowns vs. Unknowns:

  • Knowns: Quantify base rates (priors) and measurable evidence (likelihoods).
  • Unknowns: Use sensitivity analysis (e.g., "What if farmer ratio is 50:1?") to stress-test conclusions.

2. Improving Feedback Loops

  • Track prediction accuracy (e.g., calibration scores).
  • Automatically adjust capital allocation as posterior probabilities shift (e.g., reduce exposure if geopolitical risk likelihood rises).

3. Combatting Biases

  • Ego Mitigation: Adopt "premortems" to challenge assumptions (e.g., "Why might Steve not be a librarian despite stereotypes?").
  • Information Asymmetry: Treat gaps as Bayesian updates—seek data (e.g., expert consultations) to reduce uncertainty.

Business Applications​

ScenarioBayesian ApproachExample
InvestmentUpdate asset valuations using market signals (e.g., earnings surprises).Rebalance portfolios if inflation likelihood spikes.
NegotiationsModel opponent priors (e.g., industry norms) and update with behavioral cues.Adjust offers if counterparty hesitates.
Product LaunchesUse A/B testing to iteratively refine success probabilities.Beta test → Measure conversion → Scale.

Execution Checklist​

  • Explore/Exploit: Balance experimentation (updating priors) with leveraging known high-probability strategies.
  • Reversible vs. Irreversible: Apply Bayesian nets for reversible decisions; use game theory equilibria (Nash) for irreversible ones.
  • Quantify Intuition: Assign confidence scores (e.g., "60% sure") and track calibration over time.

Context​