Uncertainty
Uncertainty is incomplete knowledge or confidence about a value, state, interpretation, behavior, or outcome.
Collaborative Software Design: How to facilitate domain modeling decisions
Uncertainty is the lack of knowledge or trust in the information available when making a decision.
- How it is used
- Guides collaborative design decisions by exposing information gaps and directing investigation toward useful hotspots.
- How deep it goes
- Defines uncertainty, distinguishes it from risk, and gives confidence tracking and feedback-loop practices without formal probability analysis.
- Examples
- Uses coin flipping to contrast risk and tracks a participant’s confidence while exploring EventStorm hotspots.
- What you can do after
- Apply it via collaborative modeling by marking hotspots, monitoring confidence, and revising models through feedback loops.
Introduction to Generative AI, Second Edition
Uncertainty is the lack of confidence or knowledge about whether a model output or conclusion is correct.
- How it is used
- Frames why confident medical answers need scrutiny even when a model performs well on exams.
- How deep it goes
- Explains the practical importance of uncertainty through errors and risk, without giving a method for estimating it.
- Examples
- Uses GPT-4’s radiology exam responses and a comparison between highly accurate models and doctors who recognize knowledge limits.
- What you can do after
- Evaluate/understand why accuracy and confident wording alone cannot establish that a medical answer is trustworthy.
Read how Introduction to Generative AI, Second Edition explains uncertainty →
Statistics Every Programmer Needs
Uncertainty is the condition in which information, outcomes, or underlying quantities are not completely known.
- How it is used
- Frames uncertainty as the reason to use statistics and quantitative methods for forecasting, risk assessment, and structured decisions.
- How deep it goes
- Defines uncertainty and demonstrates Monte Carlo interpretation, but stops short of formal probability theory or mathematical proofs.
- Examples
- Uses call-center staffing, Poisson outcomes, and simulated January 2024 GM stock-price trajectories and summary statistics.
- What you can do after
- Apply it via Monte Carlo simulations to compare outcomes, quantify variability, assess risk, and plan under uncertain conditions.
Read how Statistics Every Programmer Needs explains uncertainty →
Grokking AI Algorithms, Second Edition
Uncertainty is the degree of unpredictability or class mixture in a dataset.
- How it is used
- Guides decision trees toward questions that separate labeled examples into clearer classes.
- How deep it goes
- Calculates uncertainty from label counts and probabilities, then uses weighted reductions to compare candidate splits.
- Examples
- Uses Okay and Perfect diamond labels, Carat and Price thresholds, and Python functions for counting, Gini, and information gain.
- What you can do after
- Evaluate candidate decision-tree questions by calculating class impurity and information gain.
Read how Grokking AI Algorithms, Second Edition explains uncertainty →
Bayesian Optimization in Action
Uncertainty is the lack of certainty about the value or behavior of an unknown quantity or function.
- How it is used
- Guides hyperparameter experiments and BayesOpt decisions when evaluations are expensive and untested outcomes remain uncertain.
- How deep it goes
- Develops intuition through hyperparameter and search examples, then connects uncertainty to Gaussian-process predictions and entropy-based information gain.
- Examples
- Uses neural-network depth trials, a one-dimensional Gaussian-process search, and a lost-remote search to illustrate uncertainty and its reduction.
- What you can do after
- Evaluate where to sample next by weighing uncertain regions against locations with stronger predicted performance.
Read how Bayesian Optimization in Action explains uncertainty →
The Art of AI Product Development
Uncertainty is the lack of complete confidence about the value, state, or outcome represented by an AI system.
- How it is used
- Guides AI product discovery, UX design, experimentation, communication, and cross-functional decision-making.
- How deep it goes
- Explains uncertainty through probabilistic behavior, interface safeguards, experimentation, feedback, and collaboration rather than mathematical internals.
- Examples
- Uses ready-aim-fire choices, simulated AI failures, confidence scores, editable outputs, and post-launch user feedback.
- What you can do after
- Apply uncertainty-aware practices to prototype, evaluate, communicate, and improve AI features while supporting informed user control.
Read how The Art of AI Product Development explains uncertainty →
BDD in Action, Second Edition
Uncertainty is incomplete or evolving knowledge about what a software system should accomplish and how it should be built.
- How it is used
- Manages evolving knowledge so teams progressively discover and deliver solutions aligned with underlying business goals.
- How deep it goes
- Explains nonlinear learning and gives BDD practices for feedback, prioritization, decomposition, and delayed commitment.
- Examples
- Uses early feature demonstrations, user and stakeholder feedback, feature decomposition, and a figure depicting nonlinear reduction of unknowns.
- What you can do after
- Apply it via BDD by seeking early feedback, prioritizing learning-rich features, and preserving options until evidence supports commitment.
Read how BDD in Action, Second Edition explains uncertainty →
Knowledge Graphs and LLMs in Action
Uncertainty is the lack of complete confidence that information, statements, or conclusions are true or precise.
- How it is used
- Frames uncertainty as a central reliability problem for intelligent-system reasoning and decision support.
- How deep it goes
- Contrasts deductive, inductive, and probabilistic reasoning, but does not formalize uncertainty mathematically.
- Examples
- Uses spam filtering and ambiguous medical diagnosis to illustrate generalization, missing information, and probabilistic recommendations.
- What you can do after
- Evaluate/understand how reasoning strategies handle incomplete, ambiguous, or unreliable information.
Read how Knowledge Graphs and LLMs in Action explains uncertainty →
Grokking Statistics
The lack of certainty about a value, outcome, or conclusion inferred from incomplete or variable data.
- How it is used
- Frames probability, sampling, and statistical inference as tools for reasoning with incomplete or variable data.
- How deep it goes
- Develops probability distributions, sampling, confidence intervals, and the central limit theorem, without formal derivations or advanced computation.
- Examples
- Uses deterministic algorithms, population samples, walking speed, confidence intervals, and predictive-model evaluation.
- What you can do after
- Evaluate estimates and claims using probability distributions, confidence intervals, representative samples, and bias-aware interpretation.
Causal AI
Uncertainty is the state of incomplete or imperfect knowledge about the value, state, or outcome of a variable or process.
- How it is used
- Frames action selection, causal inference, and policy evaluation when outcomes remain uncertain after an intervention.
- How deep it goes
- Connects uncertainty to causal factors, subjective probability, and counterfactuals but stops short of a probability-modeling procedure.
- Examples
- Uses business investment, economic conditions, investor choices, and rejected-option outcomes as concrete cases.
- What you can do after
- Evaluate decisions and policies by modeling uncertain outcomes, causal influences, and counterfactual alternatives with probability.
Related concepts
- entropymeasured by · Entropy is a quantitative measure of uncertainty or dispersion in a probability distribution and, more generally, of disorder in the possible states of a system.
- informationmeasured by · Information is content that conveys meaningful distinctions or reduces uncertainty about possible states, observations, or outcomes.
- probability theorymeasured by · Probability theory is the branch of mathematics that formalizes uncertainty and analyzes random events and outcomes using probability measures.
- cross entropy lossmeasured by · Cross-entropy loss measures the expected negative log probability that a predicted distribution assigns to outcomes drawn from a target distribution.
- confidence intervalmeasured by · A confidence interval is a range estimated from data by a statistical procedure designed to provide a specified long-run coverage probability for an unknown quantity.
- confidencecontrasts with · Confidence is a degree of certainty or assurance associated with a judgment, estimate, prediction, capability, or statistical procedure, often expressed as a score, probability, or coverage level.
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