Level of uncertainty

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Level of uncertainty refers to the degree to which the object of study is uncertain, from the point of view of the decision maker.  A continuum of different levels of uncertainty includes:

  • Determinism » Read more  refers tothe situation in which everything is known exactly and with absolute certainty (i.e. where there is no uncertainty at all).  This is an ideal that is never achieved in policy relevant sciences due to the complexity of the problems concerned - though systems are often represented in deterministic terms in order to facilitate modelling. </more>
  • Statistical uncertainty » Read more describes the situation where there exist solid grounds for the assignment of a discrete probability to each of a well-defined set of outcomes. Potential outcomes can be identified as a finite set of discrete outcomes, or a single continuous range of outcomes.  In situations of statistical uncertainty, analysts possessing knowledge of the form of the distribution (normal, lognormal, exponential, etc…) and its properties (length, concentration, etc....) can describe the probability with which any of the potential outcomes will occur. 
  • Scenario uncertainty » Read more  describes the state where all of the possible outcomes are known, but where it is acknowledged that there exists no credible basis for the assignment of probability distributions to these outcomes. This can be due to the fact that the mechanisms leading to the potential outcomes are not well understood so that the probability of any one particular outcome occurring cacnot be quantified within acceptable limits.
  • Recognised ignorance » Read more  describes the state where there exist neither grounds for the assignment of probabilities, nor even the basis for defining the complete set of potential outcomes. It is a state where fundamental uncertainty about the mechanisms and functional relationships being studied has been identified, and where the scientific basis for developing scenarios is weak. In some cases ignorance may be lessened by conducting further research, which implies that it might be possible somehow to achieve a better understanding. In cases, however, neither research nor development can resolve the ignorance: for example, where the functional relationships are very complicated and/or the number of parameters is very large; or where the relationships are inherently unidentifiable (e.g. due to chaotic properties in the system that make predictions impossible). </more>
  • Total ignorance » Read more  is the other extreme from determinism on the scale of uncertainty, which implies a deep level of uncertainty, to the extent that it is not even known that knowledge is lacking.

Examples of scenario uncertainty and total ignorance are given in the boxes, below.

 

Example 1: scenario uncertainty

Consider the case antimicrobials and antibiotics in animal feedstuff. Antibiotics are probably the single most important discovery in the history of medicine. They have saved millions of lives by killing bacteria that cause diseases in humans and animals. Beginning in the 1940s, low levels of antibiotics began to be added to animal feedstuff as it was observed that this practice could increase the growth rate of the animals, increase the efficiency of food conversion by the animals, as well as have other benefits such as improved egg production in laying hens, increased litter size in sows and increased milk yield in dairy cows. Over the years, concerns developed over the potential for bacteria to develop resistance to the antibiotics. It was feared that the widespread use of the antibiotics would lead to the development of resistant bacterial strains, and that these antibiotics would therefore no longer be effective in the treatment of disease in humans. The scientific evidence available indicated that the development of bacterial resistance could take place, but how quickly and to what extent this could occur remain unknown to this day. The question of whether the short-term benefits outweigh the potential long-term risks is still being debated. In this case, the scenario is clear but the probability of its occurrence is unknown. The uncertainty here is of a level greater than statistical uncertainty, and is referred to as scenario uncertainty.

 

Example 2: ignorance

Consider the case of mad cow disease (also known as BSE) in Britain. Following the diagnosis of the first cases of BSE in 1986, it was noticed that the pathological characteristics of the new disease closely resembled scrapie, a contagious disease common in the UK sheep population. Health authorities soon observed that contaminated feed was the principle cause of BSE in cattle. However, the question remained: contaminated by what? There was no scientific evidence that eating sheep meat from scrapie-infected animals could pose a health risk, and health authorities could not be sure that the agent that caused BSE had in fact derived from scrapie. Moreover, there was no scientific evidence indicating that BSE could subsequently be transmitted to humans in the form of Creutzfeldt-Jakob disease (CJD), and it was a major surprise when, in 1995, it was discovered that this could happen.

The notion of ignorance is illustrated by considering the uncertainty characterising an assessment of the potential costs associated to BSE, performed at the time of the discovery of BSE in 1986. No historical data on BSE were available and scientific understanding of how the disease is contracted was limited. The extent of the public outcry that would eventually occur remained unknown, as did the extent of the loss of exports and the drop in domestic demand that ensued. Data on the relationship between BSE and CJD would not become available for another 10 years. Furthermore, at the time there was not even a credible basis to claim that all of the potential ramifications or costs (outcomes) of the BSE crisis had been thought of. The uncertainty characterising this situation thus represents an example of ignorance.