**Concepts of Uncertainty**:
– Lack of certainty is a state of limited knowledge.
– Uncertainty involves impossible exact descriptions of outcomes.
– Uncertainty involves assigning probabilities to possible outcomes.
– Second-order uncertainty is represented by probability density functions.
– Opinions in subjective logic involve uncertainty.
– Uncertainty involves a set of possible outcomes with assigned probabilities.
– Probability density functions are applied to continuous variables.
– Second-order uncertainty is represented in probability density functions.
– Uncertainty is distinct from variability.
– Variability is derived from observed data.
**Types of Uncertainty**:
– Knight distinguished uncertainty from risk in economics.
– Uncertainty is lack of knowledge that is impossible to calculate.
– Known risks have insured unfavorable outcomes.
– Unknown risks lead to extremely risky decisions.
– Uncertainty is present in the future, past, and present.
– Knightian uncertainty involves situations with unknown probabilities.
– Financial market investments like stock markets are subject to Knightian uncertainty.
– Uncertainty plays a significant role in economic decision-making.
– Uncertainty affects market behaviors and outcomes.
– Radical uncertainty is distinct from Knightian uncertainty.
– Radical uncertainty arises when knowledge is unresolvable.
– Resolving uncertainty requires acquiring knowledge.
– Lack of means to acquire knowledge leads to radical uncertainty.
**Measurement and Reporting of Uncertainty**:
– Calculation of measurement uncertainty follows ISO’s Guide to the Expression of Uncertainty in Measurement (GUM).
– NIST provides guidelines in Technical Note 1297 for evaluating uncertainty.
– Uncertainty components are categorized as Type A (statistical) and Type B (other means).
– Combined measurement uncertainty is the square root of the resulting variance.
– Notation like 1.00794(7) represents uncertainty in the least significant digits.
– Public interpretation of uncertainty in science differs from the scientific community.
– Information deficit model explains discrepancies between scientific studies.
– Transformation of indeterminacy and ignorance into uncertainty aids public understanding.
– Journalists may inflate or downplay uncertainty in reporting scientific issues.
– Media frames like economic development or social progress may downplay uncertainty.
– Providing context for changes in research findings helps avoid inflating uncertainty.
– Describing scientific consensus can help prevent misinterpretation of uncertainty.
– Including caveats and tentative wording in reporting maintains accuracy.
– Avoiding single-source stories and providing historical context aids in accurate reporting.
– Balancing perspectives and presenting scientific consensus accurately is crucial.
**Applications of Uncertainty**:
– Designed into games, notably in gambling.
– In scientific modeling for predicting future events.
– Common in computer science and data management.
– Used in optimization for scenarios beyond user control.
– Integrated into weather forecasting for more accurate predictions.
– Reasoning systems handle uncertainty for situated agents.
– Approaches include certainty factors and probabilistic methods.
– Techniques like Bayesian inference and Dempster–Shafer theory are used.
– Fuzzy logic and connectionist approaches are also common.
– Essential for developing intelligent systems that can navigate uncertainty.
**Philosophical and Scientific Perspectives on Uncertainty**:
– Pyrrho embraced uncertainty in Western philosophy.
– Led to philosophies like Pyrrhonism and Academic Skepticism.
– Aporia and acatalepsy are key concepts in ancient Greek philosophy.
– Central to questioning and skepticism in philosophical thought.
– Represents a fundamental aspect of philosophical inquiry.
– Heisenberg uncertainty principle is foundational in quantum mechanics.
– Measurement uncertainty is a key concept in metrology.
– Implicit in daily life measurements but explicit statements are necessary for precision.
– Used in engineering for validation and verification of material modeling.
– Uncertainty is inherent in every measurement and calculation process.
Uncertainty or incertitude refers to epistemic situations involving imperfect or unknown information. It applies to predictions of future events, to physical measurements that are already made, or to the unknown. Uncertainty arises in partially observable or stochastic environments, as well as due to ignorance, indolence, or both. It arises in any number of fields, including insurance, philosophy, physics, statistics, economics, finance, medicine, psychology, sociology, engineering, metrology, meteorology, ecology and information science.
