Uncertainty of knowledge

There has been considerable IS research interest in decision under uncertainty and the impacts of onUne security risks. However, there has not been a systematic model and approach available to address the impacts of variant uncertainties of knowledge of onUne information security risks on consumer decision making in the B2C e-commerce context. The theoretical basis for prior research on decision under risk and uncertainty primarily falls into three categories utility theoiy, attitudinal theories, and the psychometric paradigm. 

As the number of probable states available to a system increases, the uncertainty as to which state the system occupies increases and the entropy defined in terms of probability increases. A statistical interpretation of entropy is related to the uncertainty of knowledge about the state of the system. 

Bayesian probability theory The approach to probability theory which views a probability as a measure of our uncertainty of knowledge rather than as relative frequency of occurrence. 

Statistics represents a body of knowledge which enables one to deal with quantitative data reflecting any degree of uncertainty. There are six basic aspects of apphed statistics. These are ... 

Uncertainty on tlie other hand, represents lack of knowledge about factors such as adverse effects or contaminant levels which may be reduced with additional study. Generally, risk assessments carry several categories of uncertainly, and each merits consideration. Measurement micertainty refers to tlie usual eiTor tliat accompanies scientific measurements—standard statistical teclmiques can often be used to express measurement micertainty. A substantial aniomit of uncertainty is often inlierent in enviromiiental sampling, and assessments should address tliese micertainties. There are likewise uncertainties associated with tlie use of scientific models, e.g., dose-response models, and models of environmental fate and transport. Evaluation of model uncertainty would consider tlie scientific basis for the model and available empirical validation. 

The risk assessment steps and the risk characterization are influenced by uncertainty and variability. Variability arise from heterogeneity such as dose-response differences within a population, or differences in contaminant levels in tlie environment. Uncertainty on tlie other lumd, represents lack of knowledge about factors such as adverse effects or contaminant levels. 

Epistemic uncertainty —missing knowledge—is due to a lack of information that through R D you could buy directly or estimate through proxy methods, if you so chose. These are controllable risks, although in practice they may be unduly expensive to control relative to the risk exposure (threat x likelihood). 

Since the student will build neither, and since the professor probably cannot answer certain questions because of secrecy agreements or lack of knowledge, the student must learn to live with uncertainty. He will also learn how to defend his own views, and how to present material so as to obtain a favorable response from others. These learning experiences, coupled with exposure to the process of design as distinct from that of analysis and synthesis, are the major purposes of an introductory design course. 

Furthermore, knowledge on the variability and uncertainty associated with each component of the model should be addressed, and described. For any risk assessment process, the uncertainty of the component is fundamental. 

Traditionally, analytical chemists and physicists have treated uncertainties of measurements in slightly different ways. Whereas chemists have oriented towards classical error theory and used their statistics (Kaiser [ 1936] Kaiser and Specker [1956]), physicists commonly use empirical uncertainties (from knowledge and experience) which are consequently added according to the law of error propagation. Both ways are combined in the modern uncertainty concept. Uncertainty of measurement is defined as Parameter, associated with the result of a measurement that characterizes the dispersion of the values that could reasonably be attributed to the measurand (ISO 3534-1 [1993] EURACHEM [1995]). 

Some types of knowledge-based systems may make judgments based on data that contain uncertainty we shall learn more of this in the next chapter when we encounter fuzzy systems. Even when the information that the system reasons with is unambiguous, the system s conclusions may come as a surprise to a nonexpert. If the user doubts whether the ES has reasoned correctly, it is natural for them to seek reassurance that the line of reasoning used is robust, so the ES must be able to do more than merely provide advice, it should be able to explain how it has reached a particular conclusion. 

This is a sample that is typical of the parent material for the characteristic under inspection. You have to be careful in the way that you define the characteristic of interest. A sample may be adequate and representative if the concentration of the analyte is at a 5% mass/mass level (i.e. 5 parts per hundred) but it may not be acceptable if the analyte is present at the 5 mg kg-1 level (i.e. 5 parts per million). Knowledge of the method used for the analysis is also important. If the method produces results with an uncertainty of 30% (see Chapter 6, Section 6.3), the method of sampling need not be so finely controlled as in the case of a method which produces results with an uncertainty of only 5%. 

In accordance with this definition, an environmental risk assessment process is used especially in cases when the probability component arises during the calculation of various parameters which can be due to many reasons uncertainty of input information uncertainties in applying an algorithm due to lack of knowledge, insufficient knowledge and/or simplification of input information uncertainties in the defined geographic boundaries of pollutant influence uncertainties in both computer calculations and management operations based on these calculations. 

Is it justifiable to conclude that the factor x, has an influence on the output y, The answer requires a knowledge of the purely experimental uncertainty of the response, the variability that arises from causes other than intentional changes in the factor levels. This variance due to purely experimental uncertainty is given the symbol a, and its estimate is denoted sL. 

---